Amazon S3 Tables now support all data types in the Apache Iceberg V3 specification. You can create V3 tables or upgrade existing V2 tables to take advantage of V3 features like deletion vectors, row lineage, and new data types such as variant, nanosecond timestamps, unknown, geometry, and geography.

Apache Iceberg has become the open standard for managing large analytics datasets. It lets you manage petabyte-scale tables with features like schema evolution, hidden partitioning, and time travel queries, while keeping your data in open Parquet files in data lakes on object storage like Amazon S3. Amazon S3 Tables offer storage purpose-built to keep Iceberg tables performant and cost-effective as they grow, with fully managed features like automatic compaction, maintenance, replication, and Intelligent-Tiering.

Teams running analytics on Apache Iceberg V2 tables often hit the same limits as their data grows. A compliance request to delete 50,000 user records from a 2-billion-row table leaves behind positional delete files that slow queries until compaction runs. Semi-structured events land as JSON strings that every query has to parse. Geospatial coordinates and nanosecond-precision timestamps get encoded as strings or integers. Each workaround adds storage cost, query latency, and pipeline code. With V3, Iceberg solves these challenges by offering native support for semi-structured and geospatial data, faster row-level operations, and built-in row lineage for data governance.

Starting today, Amazon S3 Tables support all V3 data types, including variant, nanosecond timestamps, geometry, geography, and unknown, along with deletion vectors and row lineage. You can create new V3 tables or upgrade existing V2 tables in place, and S3 Tables continue to run compaction and maintenance for you.

Apache Iceberg V3
V3 is the latest version of the Iceberg specification. Among its many improvements, V3 introduces capabilities that address the most common pain points in V2. This includes:

Deletion vectors replace V2’s positional delete files with a compact binary format. That 50,000-row compliance delete now writes a single deletion vector file instead of thousands of small deletes, significantly reducing compaction time and delete file overhead.

Row lineage adds _row_id and _last_updated_sequence_number to each record automatically. Your downstream pipelines can query these fields to find changed rows without scanning the full table.

New data types let you store semi-structured, geospatial, and nanosecond-precision data natively instead of encoding it as strings or integers:

• Nanosecond timestamp(tz) for nanosecond-precision timestamps
• Geometry and geography for geospatial data
• Unknown for columns with no known type

Variant data type stores semi-structured data in columnar format. During writes, the engine shreds variant data into hidden columns and collects statistics. At query time, those statistics enable file pruning that significantly reduces I/O compared to parsing JSON strings.

The following sections walk through how to use these V3 capabilities in practice, with examples that show how to create tables, work with the new data types, and manage data at scale.

Getting started
A retail analytics team tracks user behavior across web and mobile apps. Each event has a different structure: page views include URLs and duration, purchases include items and amounts, and searches include query terms and result counts. With V3’s variant type, you store all event shapes in one table without predefined schemas:

CREATE TABLE my_catalog.namespace.clickstream (
  event_id bigint,
  event_time timestamp,
  user_id string,
  payload variant
)
USING iceberg
TBLPROPERTIES ('format-version' = '3')

Insert events with different payload shapes without worrying about schema evolution:

INSERT INTO my_catalog.namespace.clickstream VALUES
  (1, current_timestamp(), 'user-42',
   PARSE_JSON('{"action": "purchase", "amount": 99.99, "items": ["laptop_stand"]}')),
  (2, current_timestamp(), 'user-17',
   PARSE_JSON('{"action": "page_view", "url": "/products/webcam", "duration_ms": 4200}'));

Now query the variant column directly, without PARSE_JSON at read time. With Amazon EMR Spark, use variant_get:

SELECT
  event_id,
  user_id,
  variant_get(payload, '$.action', 'string') AS action,
  variant_get(payload, '$.amount', 'double') AS amount
FROM my_catalog.namespace.clickstream
WHERE variant_get(payload, '$.action', 'string') = 'purchase'
  AND variant_get(payload, '$.amount', 'double') > 50.00

To enable deletion vectors for write operations, configure merge-on-read mode:

ALTER TABLE my_catalog.namespace.clickstream
SET TBLPROPERTIES (
  'write.delete.mode' = 'merge-on-read',
  'write.update.mode' = 'merge-on-read',
  'write.merge.mode' = 'merge-on-read'
)

Now when you run a compliance delete, V3 writes a small deletion vector instead of rewriting data files:

DELETE FROM my_catalog.namespace.clickstream
WHERE user_id = 'user-42'

S3 Tables compaction handles these deletion vector files automatically on the next maintenance cycle.

Upgrading from V2
AWS provides backwards compatibility for both versions to minimize disruption during migration to V3. Existing V2 readers continue to work on upgraded tables until you’re ready to fully adopt V3 features. For more details, see the S3 Tables Iceberg V3 documentation.
Upgrade an existing table atomically without rewriting data:

ALTER TABLE my_catalog.namespace.existing_table
SET TBLPROPERTIES ('format-version' = '3')

On the next compaction cycle, S3 Tables remove old V2 delete files. New modifications use deletion vectors automatically. Row lineage fields initialize on the first data modification after the upgrade.

This is a one-way operation. The Apache Iceberg specification does not support downgrading from V3 to V2. Verify that all engines accessing the table support V3 before upgrading.

Using row lineage for incremental pipelines
After your table has V3 data, use row lineage to build efficient incremental pipelines:

SELECT *, _row_id, _last_updated_sequence_number
FROM my_catalog.namespace.clickstream
WHERE _last_updated_sequence_number > 42

This returns only rows modified after sequence number 42. Your downstream jobs can checkpoint this value and process only new changes on each run, instead of scanning the full table.

Compatibility across AWS analytics services
AWS offers the broadest native Apache Iceberg support of any major cloud provider, with Iceberg-compatible services at every layer of the data stack: ingestion, storage, catalog, and analytics. You can store and automatically optimize V3 tables in Amazon S3 Tables, write data with Amazon EMR Spark, integrate and manage data with AWS Glue, and run analytics with Amazon Redshift. To learn more about AWS analytics support for V3, see the Apache Iceberg on AWS prescriptive guidance.

Both S3 Tables and AWS Glue Data Catalog support the Iceberg REST Catalog (IRC) API, enabling interoperability across engines regardless of the catalog endpoint.

Things to know

• S3 Tables compaction fully supports V3 deletion vector files and preserves row lineage metadata.
• The new V3 data types (variant, nanosecond timestamps, geometry, geography, and unknown) require an engine built on Apache Spark 4.0 or later, such as AWS Glue 6.0 or later, or Amazon EMR release 8.1 or later.
• You can create V3 tables from the Amazon S3 console, AWS CLI, or any engine that supports the Iceberg REST Catalog API.
• The new V3 data types are supported only for tables that use the Parquet file format (not ORC or Avro).
• Columns of type variant, geometry, geography, or nanosecond timestamp can’t be included in a table’s sort order for compaction. Tables containing these columns still compact under the sort and Z-order strategies when the sort order uses columns of other types.

Now available
Amazon S3 Tables support for all Apache Iceberg V3 data types is now available in all AWS Regions where S3 Tables are supported. Apache Iceberg V3 support is available at no additional charge; standard S3 Tables pricing applies.

To get started, visit the Amazon S3 Tables documentation or create a table bucket from the Amazon S3 console. If you want to call APIs, search documentation, find regional availability, and check troubleshooting about this feature, try using the AWS MCP Server and plugins with your preferred AI tool. Send feedback to AWS re:Post or through your usual AWS Support contacts.

– Daniel Abib

The post Amazon S3 Tables now support all Apache Iceberg V3 data types appeared first on The NOKIA Blog.

Today, we’re announcing a new capability for Amazon Aurora PostgreSQL that you can use to directly query operational data together with data stored in your data lake in Apache Iceberg and Apache Parquet formats, using your existing PostgreSQL applications and tools. By eliminating the need to extract, transform, and load (ETL) structured data from data lakes into your operational database, you can reduce operational complexity and simplify application development. You can also use Aurora PostgreSQL to query data from data lakes managed in Iceberg REST Catalog (IRC)-compatible catalogs, giving you access to data across a breadth of analytics systems without moving or duplicating it. Whether you’re powering real-time dashboards, enriching transactions with historical context, or building AI agents that reason over both live and archived data, you can now do it all through a single, familiar interface.

Previously, if your application needed to combine recent transactional data in Aurora with historical records stored in Amazon S3, a common approach was to build reverse ETL pipelines that duplicated data, increased infrastructure costs, and required ongoing engineering effort to keep everything synchronized. This challenge only grows as you increasingly embed AI agents into your applications, where it is impractical to predict and pre-replicate every dataset an agent might need.

DuckLabs, the team that maintains the DuckDB project, recently joined Amazon, and this capability is an example of how the efficiency of DuckDB is being integrated into our services. DuckDB is now embedded directly within Aurora PostgreSQL, so you can query live operational data (including uncommitted writes) alongside your data lake in a single query. Query processing stays within Aurora, with no additional network hops and no ETL pipelines that duplicate data. You can query Apache Iceberg tables managed through the AWS Glue Data Catalog, as well as Parquet and Iceberg data stored in Amazon S3 and S3 Tables. You do all of this using familiar PostgreSQL syntax and your existing applications and tools.

We’re excited to bring the speed and simplicity of DuckDB directly into Aurora PostgreSQL, so you and your agents can query and combine operational and Iceberg data using the familiar PostgreSQL applications, tools, and endpoints already in use. By building this capability around DuckDB, future improvements to the open source engine can continue to bring performance and functionality gains to Aurora and other AWS services.

What is new
This capability is supported on two Aurora PostgreSQL major versions: 17 (starting with 17.11) and 18 (starting with 18.6). To use it, you create an Aurora PostgreSQL cluster, attach an IAM role with the AuroraAnalytics feature, and enable the aurora_analytics extension. The IAM role is what gives Aurora access to your data in Amazon S3 and the AWS Glue Data Catalog. You then create foreign tables that point to your Iceberg or Parquet data in the data lake, and query them using familiar PostgreSQL syntax. You can complete this setup through the Amazon RDS console, or with any PostgreSQL client such as psql. The process is well documented in the Aurora PostgreSQL documentation.

You can query data across external IRC-compatible catalogs through AWS Glue Data Catalog federation. You register the external catalog once with Glue, and then create foreign tables for the tables you want to query, the same way you would for any Glue-native table. A single query can then join data stored in Aurora with Iceberg tables registered across multiple catalogs, so applications get a unified view without moving data or replacing your existing catalog investments.

Aurora also applies optimizations such as predicate pushdown and column pruning so that only the relevant data is read. This keeps queries efficient even as the underlying data grows. Frequently accessed data is also cached in your Aurora instance, so subsequent queries against the same data return faster. You can inspect this behavior per query using aurora_analytics_stat_statements(), which reports metrics such as rows scanned, bytes read from Amazon S3, and cache hits.

To see how direct querying works, I connected to my Aurora PostgreSQL database using psql and created the extension:

CREATE EXTENSION aurora_analytics;

For my walkthrough, I set up a simple financial scenario. I have a recent_transactions table in Aurora with the last 7 days of customer transactions, and a Parquet file in Amazon S3 containing 5 years of historical transaction data. To make Aurora aware of the historical data, I created a foreign table pointing at the Parquet file in S3:

CREATE FOREIGN TABLE transaction_history ()
SERVER aurora_analytics_server
OPTIONS (
    location 's3://<my-bucket>/finance/transaction_history.parquet',
    format 'parquet'
);

Notice the empty parentheses in the CREATE FOREIGN TABLE statement. Aurora automatically reads the schema from the Parquet file metadata, so you do not need to define columns manually. For workloads with many tables, you can skip creating them one at a time: a single IMPORT FOREIGN SCHEMA statement bulk-creates foreign tables for every Iceberg or Parquet table in an AWS Glue Data Catalog database, inferring schemas automatically.

With both tables in place, I ran a single query that combines the recent operational data in Aurora with the historical data in S3:

SELECT merchant, category, amount, transaction_date, 'recent' AS source
FROM recent_transactions
WHERE customer_id = 'C-1001'
UNION ALL
SELECT merchant, category, amount, transaction_date, 'historical' AS source
FROM transaction_history
WHERE customer_id = 'C-1001'
  AND transaction_date >= CURRENT_DATE - INTERVAL '5 years'
ORDER BY transaction_date DESC
LIMIT 15;

The result shows both recent and historical transactions in a single result set. The 7 most recent rows come from Aurora, and the rest come directly from the Parquet file in S3. DuckDB handles the analytical scan of the Parquet data under the hood, while Aurora handles the operational data. That single query would have previously required a pipeline to move the historical data into the database first.

If a query pattern needs single-digit-millisecond latency, you can materialize data from the data lake into a native Aurora PostgreSQL table using familiar commands such as CREATE TABLE AS SELECT, INSERT INTO ... SELECT, or MERGE INTO. The materialized table lives in Aurora and is queried like any other PostgreSQL table, giving you a low-latency path for hot data without operating a separate ingestion pipeline. The read queries can run on any Aurora PostgreSQL instance in your cluster, whether the writer or a read replica, so you can offload analytical scans from your operational workload. The materialization commands write data into Aurora, so they run on the writer instance.

Get started today
Direct querying of Apache Iceberg and Parquet data from Amazon Aurora PostgreSQL is available today in all commercial AWS Regions and AWS GovCloud (US) Regions, at no additional charge. You pay only for the incremental Aurora compute the queries consume and Amazon S3 request costs for reading data lake files.

To learn more, visit the Amazon Aurora features page, read the Aurora PostgreSQL documentation, or try it in the Amazon RDS console. We welcome your feedback through AWS re:Post or through your usual AWS Support contacts.

— Esra

The post Amazon Aurora PostgreSQL now supports direct querying of Apache Iceberg and Parquet data in your data lake appeared first on The NOKIA Blog.

Amazon CloudWatch now offers CloudWatch Omni, an AI-powered observability experience for the applications and AI agents you run together. You reach Omni through a dedicated URL for your organization and sign in with the identities you already manage, so working in Omni does not require access to the AWS Management Console. Omni is built on OpenTelemetry: the telemetry you already send to CloudWatch appears in Omni with nothing to reconfigure, and any other workload you instrument with OpenTelemetry sends its telemetry to an OpenTelemetry Protocol (OTLP) endpoint.

CloudWatch Omni offers both agent observability and application observability in a single experience. In our companion post, we introduced the agent observability capabilities of Omni for generative AI and agentic workloads. In this post, we present the application observability experience.

Engineering teams spend a significant portion of their observability time maintaining dashboards, tuning thresholds, and switching between tools to piece together what happened during an incident. When an issue crosses team boundaries, context gets lost in Slack threads and screenshots rather than flowing naturally to the next engineer. CloudWatch Omni changes this by organizing observability around your applications rather than individual signals, and bringing your whole team into the same workspace.

What CloudWatch Omni brings
CloudWatch Omni addresses three problems that engineering teams told us they face today.

One collaborative experience for your whole team. Every engineer accesses CloudWatch Omni through a single URL with enterprise SSO (via IAM Identity Center, supporting Okta, Azure AD, and other providers). No AWS Console access is required. SREs, developers, database engineers, and managers share the same data and investigation context. When an investigation escalates, the next person joins the same session with full context already in front of them.

The system adapts as your applications evolve. CloudWatch Omni discovers your services, maps dependencies, and adjusts alarms automatically. Instead of manually curating dashboards and tuning thresholds, you declare what matters (availability targets, latency budgets, error rate thresholds) and Omni adapts as your system changes. When you deploy new services, Omni updates the application topology automatically.

AI-powered investigation with Amazon DevOps Agent. Amazon DevOps Agent participates alongside your team in investigation sessions, correlating signals and suggesting next steps. The agent works from the same telemetry your engineers see, so its suggestions are grounded in the actual state of your application. It identifies correlated events across services, traces root cause paths through your dependency graph, and maintains investigation history for post-incident review.

How an investigation works
When something breaks, CloudWatch Omni opens an investigation session pre-loaded with context. Here is a typical incident workflow:

An alarm fires on elevated error rates in your checkout service. Omni opens a session showing the service topology, correlated signals (a deployment 10 minutes earlier, increased latency from a downstream payment API), and DevOps Agent’s initial analysis.

Your on-call SRE confirms the deployment correlation, pulls in the trace view to identify failing endpoints, and checks if the payment API latency correlates with a capacity limit.

The SRE escalates to the payments team. The payments engineer joins the same session and sees everything found so far, plus DevOps Agent’s correlation with a configuration change in the payment provider’s API gateway. They identify the root cause and roll back.

The entire investigation history is captured automatically. No separate incident report needed.

Walkthrough: setting up your first Space
To set up CloudWatch Omni for your team, open the CloudWatch console and click “Try CloudWatch Omni.”

Figure 1. CloudWatch console — Omni setup page

Next, connect your identity provider through IAM Identity Center (supporting Okta, Azure AD, and other SAML 2.0 providers). Once connected, your team members access Omni directly at your dedicated URL without needing AWS Console credentials.

Create a Space for your team. A Space groups the applications your team owns and the telemetry associated with them.

Figure 2. CloudWatch Omni Home — your team’s workspace with application monitoring, analytics, and agent observability

Once created, Omni discovers your services automatically and maps the dependencies between them. You see your application topology immediately.

Figure 3. Application topology — services and dependencies mapped automatically

You can ask CloudWatch Omni any question about your applications in plain English, and Omni will analyze your telemetry data and surface insights.

Figure 4. Interact with your telemetry in natural language

You can also set up service health alerts, configure what matters to your team, and trigger an AWS DevOps agent investigation to identify the root cause and develop a mitigation plan.

Figure 5. Investigation session — DevOps Agent identifies root causes and suggests next steps

Application-centric organization
CloudWatch Omni organizes telemetry by application rather than by infrastructure component. The system automatically discovers services from the telemetry data and AWS Config resource discovery, maps dependencies, and lets you see your application as a connected system rather than a collection of isolated resources.

Each team gets a Space that contains the applications they own. A Space points at existing CloudWatch data (logs, metrics, traces, and alarms) with no additional data movement required. Dynamic views replace the maintenance burden of static dashboards, providing ongoing visibility into SLOs and application health.

Getting started
Getting started takes minutes and doesn’t require reconfiguration of your existing CloudWatch setup.

If you’re an existing CloudWatch customer: Click “Try CloudWatch Omni” in the CloudWatch console. All your existing telemetry (logs, metrics, traces, and alarms) is immediately available. Workloads are discovered automatically, and you can start an investigation or browse your application topology right away.

For organization-wide deployment: An administrator configures a domain, connects your identity provider via IAM Identity Center, defines Spaces for teams and environments, and invites users. Each Space points at existing CloudWatch data with no additional data movement required.

For applications in other environments: CloudWatch Omni provides connectors that make it easy to bring in telemetry from additional environments. All ingested telemetry appears alongside your AWS data in the same Spaces and investigation sessions.

For generative AI and agentic workloads: The same CloudWatch Omni experience delivers purpose-built observability for AI agents, including trace exploration, evaluation frameworks, and real-time monitoring. In our companion post, we introduced the agent observability capabilities of Omni; for that walkthrough, see Introducing Amazon CloudWatch Omni: AI-powered observability for generative AI and agentic workloads.

Things to know

• CloudWatch Omni extends CloudWatch. Existing alarms, dashboards, APIs, and console workflows continue unchanged.
• Access is through a dedicated web application with enterprise SSO. Engineers don’t need AWS Console access to use it.
• Once you setup, DevOps Agent is enabled by default in every Omni investigation session.

Pricing and availability
Amazon CloudWatch Omni is now available. Existing CloudWatch customers can try it directly from the CloudWatch console. For pricing details, visit the Amazon CloudWatch pricing page.

To get started, visit Amazon CloudWatch Omni or click “Try CloudWatch Omni” in the Amazon CloudWatch console.

If you want to call APIs, search documentation, find regional availability, and check troubleshooting about this feature, try using the AWS MCP Server and plugins with your preferred AI tool. Share your feedback on AWS re:Post or reach out through your usual AWS Support contacts.

— Daniel Abib

The post Introducing Amazon CloudWatch Omni: collaborative AI-powered observability for your applications appeared first on The NOKIA Blog.

Today, Amazon CloudWatch introduces CloudWatch Omni, a unified observability experience for application and AI workloads that is app-centric, AI-powered, built on open standards, and delivered off-console. CloudWatch Omni is a purpose-built observability, evaluation, and experimentation solution for AI agents. It helps teams design, evaluate, and operate AI agents across any model provider, framework, or runtime, with an eval-driven workflow, support for the tools you already use, and observability delivered where you work: directly in your IDE and through a standalone web experience, separate from the AWS Management Console.

Organizations deploying agentic AI systems face observability challenges that traditional monitoring can’t address. Agent behavior is non-deterministic: a prompt change can degrade response quality even when standard metrics show no errors. Teams spend hours manually reviewing logs across multiple systems, unable to pinpoint what changed or why. Existing tools force teams to choose between siloed generative AI monitoring or fragmented solutions requiring constant context-switching between their coding environment and browser-based dashboards.

CloudWatch Omni captures every trace and includes built-in evaluators for correctness, coherence, retrieval quality, and tool selection, among others. You can compare prompt versions side by side in the playground, build test datasets from production traffic, run experiments across different configurations, and detect regressions automatically.

Two surfaces for development and operations
CloudWatch Omni delivers observability through two complementary surfaces. Developers get a native extension inside VS Code and Kiro (the currently supported IDEs), where traces appear as you run your agent with a playground and evaluators a click away. Operators get a standalone web experience, separate from the AWS Management Console to monitor the fleet, accessible through SSO with no AWS console needed. Both share the same data: the trace a developer debugs is the trace an operator investigates.

The Cloud Login feature connects your local IDE environment to your AWS account, enabling you to send telemetry data to Amazon CloudWatch for persistent storage, share traces with your team, and access production dashboards. This connection is optional. You can use CloudWatch Omni entirely locally during development, then connect to the cloud when you are ready to monitor agents in production.

Getting started
CloudWatch Omni offers two ways to get started: through the IDE extension (for VS Code and Kiro) or directly through the cloud experience, where you can start sending telemetry data to CloudWatch without installing any IDE extension. In this walkthrough, I install the extension, create an agent, run it, and explore the traces and evaluation tools from my IDE.

After installing the CloudWatch Omni extension from the VS Code Marketplace, the CloudWatch Omni icon appears in the Activity Bar. From the welcome screen, I selected Get started with Sample Project to load a pre-configured agent with sample trace data or use shortcut to Command Palette using Command + Shift + P (on macOS) or Ctrl + Shift + P (on Windows/Linux) and select Omni: Create a new Project

Figure 1. CloudWatch Omni welcome screen & create new project in VS Code

The sample project comes with an agent implementation and example datasets. Part of the getting-started experience is adding OpenTelemetry instrumentation, and CloudWatch Omni guides you through each step. You can also create a new agent from scratch. CloudWatch Omni walks you through the process using an interactive chat where you define the agent’s purpose, select a model provider, and configure tools. All data is stored locally by default. You can optionally connect to AWS to send data to Amazon CloudWatch.

After verifying the configuration, I started the local dev server and sent a question to the agent. What makes this different from a typical chatbot interface is what happens next: selecting View Trace shows exactly how the agent processed the request.

Figure 2. CloudWatch Omni guides your AI code assistant to configure the local development environment for testing

CloudWatch Omni integrates with AI code assistants such as Kiro, Claude Code, and Codex to streamline the setup process. These assistants can configure the Dev Server, install dependencies, and set up instrumentation on your behalf, so you can go from installation to running your first traced agent session in minutes without manual configuration.

Figure 3. Interacting with the agent and viewing traces

Traces are essential for understanding AI agent behavior. Unlike traditional request-response systems, agents make multiple decisions per invocation: choosing tools, composing prompts, and chaining sub-calls. Without full trace visibility, diagnosing why an agent produced an incorrect answer or took an unexpected path becomes guesswork. CloudWatch Omni records every step in a structured timeline so you can pinpoint exactly where behavior diverged.

The Trace Explorer shows a detailed breakdown of every step the agent took (LLM calls, tool invocations, and reasoning steps) in a structured, hierarchical timeline. I could drill into any span to inspect inputs, outputs, token usage, and latency.

Figure 4. Trace Explorer showing the agent’s execution timeline

The Trace Explorer also supports Compare mode, which places two traces side by side to see how different prompts or configurations affect behavior. Compare mode is especially helpful when debugging regressions. And with Ask Assistant, an AI agent analyzes your traces to surface patterns and anomalies, answering questions like “Why did the agent call this tool twice?”

Figure 5. Comparing two traces side by side

Evaluation is what turns observability into actionable quality improvement for generative AI. Traditional metrics like latency and error rate cannot tell you whether an agent’s response was helpful, coherent, or factually correct. Evaluators score each response against quality dimensions, letting you measure what users actually experience and catch regressions that standard monitoring misses entirely.

CloudWatch Omni includes 17 built-in evaluators for metrics like coherence, helpfulness, faithfulness, and routing correctness. I selected traces from the Trace Explorer, chose evaluators, and ran an evaluation, getting per-example scores and aggregate metrics without building any custom evaluation framework.

Figure 6. Running evaluations on traces

From there, I used the Playground to test different system prompts side by side, comparing multiple model and prompt configurations in real time to see how each variation affects output quality before committing changes. With the Experiments view, I could run the same dataset against two agent variants and compare their evaluation scores, latency, and token usage side by side to pick the best-performing configuration.

Figure 7. Comparing evaluations across agent variants in the Omni Experiments console

With Prompt Management, you can version and track prompt configurations over time, making it easy to roll back when a new version underperforms.

CloudWatch Omni also provides a Session Explorer to review full conversation histories and understand how agents handle multi-turn interactions, along with an Agent Topology view that visualizes the architecture of your agent system, including sub-agents, tools, and their interconnections. You can drill into any node to inspect performance and identify bottlenecks.

CloudWatch Omni also offers a dedicated web experience accessible from any browser without an IDE. Teams can access all capabilities collaboratively, including application monitoring, analytics, agent observability, and AI-powered investigations.

Figure 8. CloudWatch Omni web experience with application monitoring, analytics, and agent observability

I curated traces into golden datasets for structured experimentation. The Experiment function runs the agent against a dataset and automatically scores results, creating benchmarks for regression testing whenever prompts or agent logic change.

If you already have an agent built with a supported framework, CloudWatch Omni provides two paths to add instrumentation: Auto-instrument with Kiro, which detects your framework and configures tracing automatically, or manual instrumentation with ready-to-use code snippets for Python and TypeScript. For detailed instrumentation guides, see the CloudWatch Omni documentation.

Supported frameworks and open standards
The walkthrough above uses the sample project, but CloudWatch Omni works with the agent frameworks teams are already using: LangChain, LangGraph, CrewAI, OpenAI SDK, Strands, Vercel AI SDK, and more, in both Python and TypeScript. It also provides native observability for agents built with Amazon Bedrock AgentCore, and uses AgentCore’s evaluation capabilities to assess agent quality directly within the Omni workflow.

Instrumentation uses open standards (OpenInference and ADOT), whether your agents run on Lambda, ECS, EKS, or other clouds. For evaluation, Omni integrates with third-party evaluators including AutoEval and DeepEval alongside built-in datasets, a playground, and batch experiments. No re-platforming required.

CloudWatch Omni brings agent observability and application observability together in a single experience. For the application observability experience, read the companion post Introducing Amazon CloudWatch Omni: collaborative AI-powered observability for your applications.

Pricing and availability
Amazon CloudWatch Omni is now generally available. The IDE extension is free to use. You don’t need an AWS account to get started. You only need AWS credentials for Amazon Bedrock models, or API keys for other providers like OpenAI or Anthropic. Get started today by installing the extension from the VS Code Marketplace.

To explore all capabilities and get started quickly, visit CloudWatch on AWS Builder Center.

If you want to call APIs, search documentation, find regional availability, and check troubleshooting about this feature, try using the AWS MCP Server and plugins with your preferred AI tool. Share your feedback on AWS re:Post or reach out through your usual AWS Support contacts.

Happy building!

— Daniel Abib

The post Introducing Amazon CloudWatch Omni: AI-powered observability for generative AI and agentic workloads appeared first on The NOKIA Blog.

Historically, part of this ritual includes installing a ton of apps. I add the requisite Adobe design tools, the Microsoft Office suite for productivity, and a host of other software. Apps for email, FTP, media conversion, and a local web server environment remain staples.

My current system is now a few years old, and I have dozens (hundreds?) of these apps. They still serve their purpose. But it recently dawned on me that most of what I use could be replaced by a web browser. That will likely mean fewer apps to install on my next computer.

These are words I never thought I’d utter. My web design career began before the first release of Netscape. As such, I’ve had a love/hate relationship with browsers. For every benefit, there have been limitations and quirks to deal with.

These days, you can do pretty much anything within your chosen browser. That got me thinking: are traditional design apps still relevant? Should we abandon them for browser-based tools? I’m not so sure, but let’s figure this out together.

Modern Web Apps Break Boundaries

It used to be that most browser-based apps were inferior to their desktop counterparts. Common workflows like text and media editing felt clunky and minimally useful. I used them in a pinch and usually came away frustrated.

Meanwhile, desktop powerhouses like Excel, Photoshop, and Dreamweaver were infinitely more flexible. They were built for those who care about quality and require high-level functionality. Such professional-grade tools have always been worth the cost.

That narrative has been turned on its head by a combination of more powerful web standards and server technologies. There are now online video editing platforms that can produce top-notch results. Design tools like Figma make it easy to create and share interactive mockups. Google’s suite of productivity tools offers all the bells and whistles without compromise. Even Photoshop has an online version these days.

Collaboration might be the biggest selling point of all. You can keep clients and colleagues in the loop with minimal effort. This improves efficiency and saves us from the never-ending email chains of yesteryear.

And the appeal of these apps goes beyond functionality and convenience. They’re also cross-platform and device-agnostic. It used to be that you’d need a desktop app built specifically for your operating system. Using a browser means it works on Windows, Linux, macOS, Android, iOS, and more.

Then there are the hardware requirements. Granted, you should still have a system with plenty of memory and a modern graphics processor. However, having a massive hard drive isn’t necessary when storing files in the cloud.

The old boundaries of software have vanished. Doing all the things within a browser isn’t a chore like it used to be. And these apps may be enough for most web designers.

Trust and Content Ownership Are Concerns

Software is always an investment, regardless of how you access it. There are also privacy and security risks to consider. Browser-based tools often amplify those concerns.

Companies like Adobe and Microsoft have some level of tracking within their desktop apps. And they do connect to the internet. So, this is nothing new. But working in a browser opens up a different (or, at least, a bigger) can of worms.

For instance, cloud storage is quite convenient. But what happens if the provider gets sold or goes out of business? Will you lose access to your data? Are policies in place to maintain account security in the event of a breach or server crash? The answers aren’t always clear, and you may not find out until something bad happens.

Then there is the matter of artificial intelligence (AI). It seems like every tool on the planet has an AI integration of sorts. How are these features used on your content? Does the provider train their models with your work? Could your sensitive information become exposed online?

Working online also opens you up to third-party tracking. It may be something relatively innocent, like an analytics suite. But ad networks and similar companies could be watching you – and there is no guarantee they’ll abide by the rules.

These are important questions to answer when thinking about your (and your client’s) data. Again, desktop apps aren’t immune to these issues. But an online app that shows up overnight deserves an honest assessment. The risks could far outweigh the benefits.

Desktop Apps Are Still Relevant When Control Matters

It’s truly amazing to see how far web technologies have come in the past decade. You can see it in both commercial and open-source software. Functionality once reserved for powerful desktop devices can now exist in the palm of your hand (and with responsive design, for that matter).

I still believe there’s a tradeoff, however. The work-from-anywhere tools we use have plenty of benefits (provided you really do want to work from your lawn chair). But you lose a measure of control along the way.

There’s something to be said for storing files locally (with routine backups, of course). Not to mention the ability to customize an app’s layout or tool set to match your workflow. There’s a level of personalization you don’t seem to get in a browser.

The reality is that you don’t need desktop software for every purpose. There are plenty of online tools that will do the job. But there are scenarios where these old-school beasts make sense.

For example, I can’t imagine Disney making their next hit movie solely within a browser. And web designers who want full control of their workflow might want to keep a few items installed on their hard drives. I know I will.

here. He recently started a writing service for WordPress products: WP Product Writeup. He also has an opinion on just about every subject. You can follow his rants on Bluesky @karks.com.

Read more articles by Eric Karkovack

The post Are Browser-Based Apps Enough for Web Designers? appeared first on The NOKIA Blog.

Sooner or later, a CFO looks at your wireframes and asks what any of it actually does for the bottom line. Storyboards don’t answer that question, and the era when a 5-minute pitch could answer it ended, unfortunately, a while ago.

These days, if you want to win budget, buy-in, and backing for UX, the design has to be provably good for the business, not only for the people using it.

And proving that takes more than taping a dollar sign to a redesign. You have to understand how your organization defines value in the first place, how it measures that value, and how a credible line gets drawn between a design initiative and an outcome leadership already cares about.

Rather than scatter tips, this article follows one worked example the whole way through. Meridian is a mid-size B2B SaaS company, and it is entirely made up — that label matters, so it gets repeated where it counts. Its onboarding redesign carries the same figures from goal-setting through cost accounting, causal testing, and the final ROI number, because a framework only becomes tangible when the numbers connect. Every step is one you can rerun inside your own organization.

Why ROI Matters More Than Ever in UX Conversations

Companies now want clarity on what every dollar buys, and “delightful user experiences” stopped clearing that bar some time ago. I still remember a former colleague celebrating a $1 million redesign he’d gotten greenlit mostly on the strength of a couple of three.js tricks. Try that pitch in front of a finance team today and see how far the particles get you.

Executives don’t hate UX, they just hate vagueness. A pitch built on “users will find it easier” loses, every time, to the department promising 12% more sales in Q3. The difference is the one between a streamlined checkout flow that reduced cart abandonment with completed purchases up 22%, and the same work rewarded with “the QA testers like it.” One of those goes on your resume. The rest of this article is about earning the first version, with Meridian’s numbers doing the work.

When Business Goals And KPIs Don’t Exist Yet

Most writing about UX ROI makes a convenient assumption: that the organization already owns clean business goals and KPIs for you to hook your work onto. Real companies are messier than that. Plenty run on ambitions like “grow faster” or “improve the customer journey” that nobody ever broke into anything measurable, and an ROI case built on that ambiguity sounds impressive right up until somebody scrutinizes it.

So the first job is often to help the organization define what success even looks like. Interview stakeholders across departments — what does product consider a good quarter, where does customer success watch users struggle, where do sales deals stall — and listen for the themes that keep resurfacing across conversations, because those recurring themes are the company’s latent business objectives. A useful forcing function is the OKR model (Objectives and Key Results), which doesn’t tolerate vagueness.

At Meridian, the stated ambition was “improve the rate of new users’ adoption of the platform,” which you can neither design toward nor measure against. Interviews turned up the real shape of the problem. Trial users needed a median of 14 days to reach first value, most churned before getting there, and onboarding questions were burying the support queue. Out of that came an OKR with actual edges: reduce median time-to-first-value from 14 days to 7 with the use of a guided setup flow, and lift trial-to-paid conversion from 8% to 9.5%.

One warning about formalizing KPIs: Impose them from inside the UX team and leadership will suspect you’ve rigged the field in your own favor, so co-create them with whoever owns the outcome — though never at the price of accepting targets that set your team up for an uncomfortable situation. Meridian’s head of product agreed that setup-completion rate was a fair proxy for onboarding usability, and customer success signed off on time-to-first-value, a number already sitting on their own dashboard.

A KPI ladder that ends at a metric somebody already watches buys you credibility before any design work starts.

Quantifying The Full Cost Of The Investment

ROI has a denominator, and the denominator is where most UX teams go wrong. You can’t calculate a return without strategic financial planning, yet cost usually gets counted as designer salaries or consulting hours and nothing else. A finance team will find the rest whether or not you counted it, so count it first.

Direct costs are the visible ones. Meridian’s redesign ran $45,000 in design and research labor plus another $8,000 in tooling and participant incentives. Licenses for Figma, UserTesting, Hotjar, analytics platforms, research incentive spend — all of it belongs in the total, and that’s before the inevitable instances of vendor lock-in every UX team eventually faces. Engineering sits in the same column, because a UX redesign doesn’t stop at the mockup. Building the guided setup took two frontend sprints plus a QA pass, $38,000 worth, and the project generated about $4,000 of coordination overhead along the way in new syncs and shared dashboards.

The line item nearly everyone misses, and the one worth stealing from this article if you steal nothing else, is stakeholder time. Workshops, design reviews, and feedback sessions all pull senior people away from their primary work. A VP of Product spending four hours a week in UX reviews is a VP not spending those hours on roadmap planning or partner negotiations. Log the attendance — who came, for how long, at what seniority — and price it at fully loaded cost, meaning salary plus benefits divided by productive hours. A quarter’s worth of workshops, reviews, and interviews at Meridian priced out at $22,000.

Add it all up: $45,000 in design labor, $8,000 in tooling, $38,000 in engineering, $22,000 in stakeholder time, $4,000 in coordination. The investment is $117,000. Saying that number out loud beats saying “we spent $45K on design,” precisely because it already includes everything a finance team would have dug up on its own.

Proving Causation, Not Just Correlation

Most UX ROI pitches die right here. Conversions rose after the redesign, sure — and the CFO wants to know how you ruled out the new pricing, the seasonal traffic bump, and the marketing campaign that shipped the same week. Without a convincing answer, your entire ROI story crumbles.

After all these years, the gold standard for proving causation is still A/B testing: run the old experience against the new one on an even traffic split until the sample means something.

Onboarding happens to suit a phased rollout, which is why Meridian could do this cleanly. For eight weeks, half of new trial signups received the redesigned guided setup while half stayed on the legacy flow. Control converted to paid at 8.0%. The variant came in at 9.4%. With roughly 6,100 trials inside the window, the difference was statistically significant, but a 1.4-point gap on a single test is still the kind of result that deserves a second look before anyone builds a budget on it, which is one reason the team held back on attribution below. Where a split isn’t feasible — a change too structural, a user base too small — fall back to a time series instead. Measure steadily for weeks before the change, implement, then keep measuring against the baseline you established.

Documenting whatever else happens around the same time as your UX change is the unglamorous half of causation.

A pricing-page test from Meridian’s marketing team overlapped weeks five through eight of the rollout. The UX team noted it, confirmed it hit both cohorts evenly, and still chose to attribute only 70% of the observed lift to the redesign in the final math. There is no formula that produces that number; treat it as an illustrative assumption for this example.

The team asked how much of the lift could plausibly belong to the pricing test if it had helped one cohort slightly more than the other, settled on a ceiling of about a third, and rounded the redesign’s share down to 70%. Your figure will differ. What matters is that it is written down and argued for before the results arrive, not fitted to them afterwards. That restraint is worth money in a skeptical room. “We attribute roughly 70% of the lift to the onboarding change, with the remainder likely influenced by concurrent pricing work” survives cross-examination; claiming everything does not. Cohort analysis then backed the number up, since the lift held across acquisition channels and tenure bands, and at that point the skeptics had very little left to work with.

Leading and lagging indicators belong on the same slide, because each covers the other’s weakness. Meridian’s leading indicators moved first — setup completion climbed from 62% to 89%, median time-to-first-value dropped from 14 days to 6.5 — and the lagging trial-to-paid number followed. Mechanism first, business outcome second. Presented together, they form a causal chain that’s harder to poke holes in than either one alone.

The ROI Calculation, End to End

So what did Meridian actually earn? The company sees about 40,000 trial signups a year. Lifting conversion from 8.0% to 9.4% adds roughly 560 paying customers annually, and at an average of $1,800 in annual recurring revenue per account, those customers represent about $1,008,000 in new ARR. Applying the conservative 70% attribution from the causal work trims the defensible figure to roughly $706,000.

Set that against the full $117,000 investment and the first-year ROI lands near 5:1, with payback arriving in roughly two months. There’s a second line, too. Onboarding-related support tickets dropped about 30%, some 3,600 fewer tickets a year, worth another $54,000 annually at $15 per resolved ticket. Keep it as its own line rather than folding it into one swollen headline number. The case reads as more honest that way and loses none of its force.

Three assumptions carry that result, and each belongs on the slide next to it. The 40,000 signups and the $1,800 average ARR are the prior year’s actuals held flat, so a growth or pricing change moves the outcome in either direction. The 70% attribution is the illustrative assumption from the causal work, not a measured quantity. And the two-month payback counts new ARR as it lands rather than revenue recognized net of churn, which flatters the timeline; on a net basis the payback stretches to roughly a quarter. State those three plainly and a finance team can adapt the example to its own numbers. Hide them and the whole thing starts to look like marketing math, however careful the experiment was.

What persuades in the final presentation is not sophistication. Open with the baseline: what stalled trials and support volume were already costing. Show the delta in terms leadership reads fluently, metrics like conversion rate uplift chief among them. A chart of setup completion climbing from 62% to 89% will beat a paragraph of UX jargon, and a translation like “each abandoned setup costs us 0.3 support tickets” beats the chart. Above all, keep every figure identical from the first slide to the last. A room full of finance people forgives many things, but never numbers that wobble between slides.

Tailoring The Case To Whoever Holds The Purse Strings

Budget decisions come out of coalitions. A CFO may hold the final say when adding AI to the checkout process, but marketing, product, and customer success all lean on that decision, and each means something different by “value.”

A CFO hears cost, revenue, and risk. A CMO hears conversion and acquisition cost, since UX is a lever for increasing marketing ROI. Product counts support tickets; customer success thinks in retention. The underlying numbers never change; only the framing rotates, and a CFO wants a projection, not a moodboard. Meridian’s CFO slide read “the onboarding redesign protects roughly $706,000 in new ARR a year against a $117,000 investment,” while the CMO version led with what a 9.4% trial conversion does to blended acquisition cost.

Beyond the Dollar Sign: Qualitative and Non-Financial Metrics

Some UX outcomes never translate cleanly into revenue, and pretending they do weakens the parts of your case that are solid.

The trick with qualitative evidence is collecting it rigorously enough that nobody can wave it off as anecdote.

Scores like Net Promoter Score (NPS), CSAT, and Customer Effort Score already sit inside most reporting cadences, which makes them cheap to borrow. Tie your work to their movement, and segment wherever the data allows.

Meridian could say that NPS among trial users on the redesigned onboarding was 51 against 34 for the legacy flow, which lands far harder than any blended average. Verbatim feedback from surveys, support transcripts, and app store reviews adds the emotional weight the scores lack. Internal tools deserve the same discipline, since employee experience is increasingly recognized as a business driver — a dashboard redesign that hands account managers 45 minutes a day back is a productivity gain, a satisfaction gain, and a retention lever all in one.

Brand perception resists direct measurement but leaves tracks in repeat visits, organic referrals, and social sentiment. It carries extra weight in trust-sensitive industries like finance or healthcare, and it forms fast, given that UX design influences the first impressions of a whopping 94% of customers.

Whatever you collect, systematize the collecting. Run pre- and post-surveys with consistent question sets, use structured usability testing with task-based scoring, and put the qualitative right next to the quantitative when you present.

“Setup completion rose from 62% to 89%, and in post-test interviews 8 of 10 participants called the new flow intuitive, against 3 of 10 for the old one” — a pairing like that is much harder to dismiss than either half on its own.

Making the Case Stick

UX loses the budget battle unless it’s mapped to company-wide objectives, so phrase the proposal in the words of this year’s board presentation.

Nobody at Meridian pitched “simplify the onboarding UI”; the pitch was a redesigned trial experience worth 1.4 points of upgrade rate, roughly $1M in annual recurring revenue before attribution. Bring evidence in both registers, since that is what social proof is for: the case, clearly labeled, plus screenshots, impact graphs, and user quotes. Find internal allies who can repeat the ROI narrative in rooms you’ll never enter, and write the playbook down, because repeatable ROI is what earns recurring investment.

Resources for Going Deeper

This topic has been explored extensively by researchers, practitioners, and consultancies. Here’s a curated set of resources worth studying if you want to build a stronger ROI practice around UX.

• “Measuring the User Experience” by Tom Tullis and Bill Albert is the definitive guide to UX metrics. It covers everything from task-based measurements to survey design to statistical analysis, and it’s written for practitioners, not academics.
• Jared Spool’s “The $300 Million Button” case study is a classic example of how a single UX change (removing a mandatory registration step) generated massive revenue uplift. It’s a story every UX professional should have in their back pocket.
• Forrester’s research on UX ROI provides enterprise-focused frameworks for building business cases around experience design, including their widely cited finding that every dollar invested in UX returns $100.
• “UX Strategy” by Jaime Levy bridges the gap between design thinking and business strategy, offering practical tools for aligning UX initiatives with organizational goals and market positioning.
• The Design Value Index by the Design Management Institute tracks publicly traded companies that invest heavily in design against the S&P 500. The data consistently shows that design-led companies outperform the index by significant margins, and it’s a powerful data point for executive presentations.
• Google’s HEART framework provides a structured approach to selecting UX metrics at scale. HEART stands for Happiness, Engagement, Adoption, Retention, and Task success, and it’s particularly useful for teams that struggle to decide which metrics to track.

Conclusion

A seat at the table never comes from beauty or novelty. It comes from measurable, defensible impact, which means UX leaders have to trade the artist’s posture for the strategist’s. Speak in outcomes rather than outputs. Connect pixels to profit.

When someone challenges the numbers, don’t flinch. Show the controlled experiment, the cohort analysis, and the before-and-after metrics, every figure holding steady from the first slide to the last, the way Meridian’s did, with the customer quotes and the employee-satisfaction data sitting right beside the revenue impact. Prove the work does more than delight users, and be ready to defend the ratio line by line. That’s when the CFO leans in, and that’s when design stops being optional.

(yk)

The post Building A UX ROI Case That Survives The Boardroom appeared first on The NOKIA Blog.

Today, Amazon EC2 R9g and R9gd instances are generally available, powered by AWS Graviton5 processors. R9g instances are memory-optimized and deliver up to 25% better compute performance compared to Graviton4-based R8g instances, powered by the most energy efficient processor AWS has ever built.

R9g instances are ideal for memory-intensive workloads including databases, in-memory caches (Valkey, Redis, MemCached), real-time big data analytics, Linux-based workloads including containerized and micro-service-based applications (e.g. Kubernetes, Docker, EKS, ECS), as well as applications written in popular programming languages such as C/C++, Rust, Go, Java, Python, .NET Core, Node.js, Ruby, and PHP.

R9gd instances include local NVMe-based SSD block-level storage, ideal for memory-intensive workloads requiring fast, low-latency local storage such as open-source databases, distributed real-time big data analytics, large in-memory databases, and large caching workloads.

If you’re running workloads on R8g instances today, R9g gives you more performance per vCPU with faster memory, higher network and Amazon EBS bandwidth, and a larger L3 cache, all while using less energy.

What makes R9g different
Graviton5 processors bring several hardware improvements over Graviton4:

• Up to 25% higher compute performance per vCPU
• DDR5 8800 MT/s memory (up from 5600 MT/s in Graviton4), the fastest memory available in the cloud
• 5x larger L3 cache for better data locality
• Up to 2x higher network and EBS bandwidth for the largest instance sizes (up to 100 Gbps network, up to 72 Gbps EBS on the 48xlarge)
• Up to 3x higher packet-processing performance

R9g and R9gd instances support Instance Bandwidth Configuration (IBC), which lets you adjust the allocation of bandwidth between Amazon EBS and Amazon VPC networking by 25%. This helps optimize performance for workloads with specific bandwidth requirements such as databases and caching.

All R9g and R9gd instances run on the AWS Nitro System, which offloads virtualization, storage, and networking to dedicated hardware. This gives your applications near-bare-metal performance while maintaining strong security isolation between instances.

R9g and R9gd instances feature the Nitro Isolation Engine (NIE), the same enhancement to the Nitro System introduced with C9g and M9g instances earlier this year, which enforces isolation of instances and harnesses formal verification to provide assurances of isolation with mathematical precision. Nitro Isolation Engine is a purpose-built component that is responsible for enforcing isolation between virtual machines, including mediation of all access to virtual machine memory, CPU register state, and I/O devices through a minimal set of APIs. Nitro Isolation Engine leverages formal verification, a technique to mathematically demonstrate that the hardware or software behaves as intended, and not just in specific test cases. This intensive verification technique establishes Nitro as the first formally verified cloud hypervisor, pioneering a new standard for mathematically proven cloud security. To learn more about the Nitro Isolation Engine, visit the blog post. For details on the formal verification results, including scope and assumptions, see the technical white paper.

EC2 R9g and R9gd instance specifications
R9g and R9gd instances are each available in 11 sizes, from medium to metal-48xl. The following tables show the full specifications for each size.

R9gd instances offer the same compute and networking performance as R9g, with the addition of local NVMe-based SSD storage for workloads that need fast, low-latency scratch space or temporary caches.

Getting started
You can launch R9g and R9gd instances from the Amazon EC2 console using any supported Arm-based AMI. R9g instances support Amazon Linux 2023, Amazon Linux 2, Ubuntu 22.04+, RHEL 8.4+, SUSE Linux Enterprise Server 15 SP3+, Debian 12+, and other major Linux distributions.

If you’re migrating from R8g, no code changes are required for most applications. Select the equivalent R9g instance size and your application runs with better performance. For containerized workloads, R9g works with Amazon EKS, Amazon ECS, and standard Kubernetes deployments. Multi-arch container images built for Arm64 run without changes.

Several resources help you get started: the AWS Graviton Getting Started Guide covers how to build, run, and optimize workloads on Graviton-based instances. The Graviton Savings Dashboard helps you track cost savings. AWS Transform automates code transformations for migrating Java applications from x86 to Graviton. To learn more, visit AWS Graviton Processors or Level up your compute with AWS Graviton.

Pricing and availability
Amazon EC2 R9g and R9gd instances are available in US East (N. Virginia, Ohio), US West (Oregon), and Europe (Frankfurt) Regions.

R9g and R9gd instances are available for purchase through Savings Plans, On-Demand, Spot Instances, Dedicated Instances, or Dedicated Hosts. For detailed pricing, visit the Amazon EC2 pricing page.

Ready to get started? Launch R9g instances from the Amazon EC2 console. For more details, visit the Amazon EC2 R9g instances page.

If you want to call APIs, search documentation, find regional availability, and check troubleshooting about this feature, try using the AWS MCP Server and plugins with your preferred AI tool. Share your feedback on AWS re:Post for Amazon EC2 or reach out through your usual AWS Support contacts.

— Daniel Abib

The post Amazon EC2 R9g and R9gd instances powered by AWS Graviton5 processors are now generally available appeared first on The NOKIA Blog.

The news that interested me the most last week was the DuckLabs acquisition. AWS has signed a definitive agreement to acquire DuckLabs, the Amsterdam-based company behind DuckDB, the popular open source analytical database that runs in-process and executes SQL directly against files like Parquet, CSV, and JSON. DuckDB stays open source under its independent foundation and the MIT license, and over time AWS plans to combine its speed at everyday queries with the enterprise scale of services like Amazon S3, Amazon Redshift, and Amazon Athena.

Co-founded by Hannes Mühleisen and Mark Raasveldt, DuckDB runs locally or on Amazon S3, which makes it remarkably fast for the everyday queries (a terabyte or less) that make up the bulk of real-world analytics. It also happens to pair beautifully with AI agents, which “poke” and experiment their way through data much like humans do. The co-founders will continue leading its technical direction while AWS combines DuckDB’s speed with analytics services like Amazon EMR, AWS Glue, and Amazon SageMaker. For the bigger picture on why this matters, Andy Warfield, Vice President and Distinguished Engineer shared his thoughts on the post DuckDB and the changing physics of analytics on All Things Distributed.

Now, let’s get into this week’s AWS news…

Last week’s launches
Here are some launches and updates from this past week that caught my attention:

• Amazon ECS now automatically detects and recovers container instances that lose agent connectivity – Amazon ECS now continuously monitors agent connectivity to the control plane and surfaces a new AGENT_CONNECTIVITY health event across AWS Fargate, Amazon ECS Managed Instances, and Amazon ECS on EC2. On Fargate and Managed Instances, ECS handles recovery automatically, draining tasks, launching replacements, and deregistering the impaired instance. On EC2, you can wire the event into your own workflow. Available at no additional cost in all AWS Commercial and AWS GovCloud (US) Regions.
• AWS Lambda introduces public preview runtimes, starting with Node.js 26 and Python 3.15 – You can now test upcoming Lambda runtimes before they reach general availability. Preview runtimes use the same identifier as the eventual GA version, so your functions graduate automatically with no action required. Third-party tools and deployment frameworks can also validate compatibility ahead of GA. Not meant for production yet (breaking changes are possible), but a great way to get ahead of your next upgrade. Available in all AWS commercial, AWS GovCloud (US), and China Regions.
• AWS IoT Core adds a native InfluxDB rule action – You can now route time-series data from your IoT devices straight into InfluxDB (Amazon Timestream-managed or self-hosted) without writing custom code or standing up an intermediate service. IoT Core formats data into InfluxDB’s line protocol and supports device-side and server-side batching. Available in all AWS Regions where Amazon Timestream for InfluxDB is offered.
• Amazon GameLift Servers now includes enhanced DDoS protection – Your game servers now get automatic protection against network and transport layer (layers 3 and 4) DDoS attacks – UDP reflection, SYN floods, and similar vectors – with nothing to enable or opt into. Built on top of AWS Shield Standard with gaming-optimized traffic shaping, it turns on the moment your servers start running (Server SDK 5) at no extra cost. It’s available in all supported GameLift Servers Regions except China (Beijing) and China (Ningxia).
• Amazon SageMaker HyperPod expands support for Ray – You can now run Ray workloads on SageMaker HyperPod with built-in observability, resilient training, and accelerated inference. Create and manage Ray clusters from Amazon SageMaker Studio, attach JupyterLab or your local IDE so a multi-node cluster behaves like a local dev environment, and get auto-provisioned Grafana dashboards. Node auto recovery, hung job detection, and tiered checkpointing keep large training runs healthy, while Ray Serve adds a tiered KV cache for inference. Your existing open source Ray code runs unchanged. Available for HyperPod clusters orchestrated by Amazon EKS.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Other AWS news
Here are some additional posts and resources that you might find interesting:

• Happy 20th birthday, Amazon EC2! – Amazon EC2 turns 20. Channy Yun looks back at how EC2 grew from a single m1.small instance type in one Region to more than 1,200 instance types across 39 Regions, along with the custom silicon journey from the first Graviton to Graviton5 and Trainium3. A fun and worthwhile read on the service that still underpins so much of AWS – including Amazon ECS, Amazon EKS, AWS Lambda, Amazon SageMaker, and Amazon Bedrock.
• Agentic Resource Discovery (ARD): an open specification for agent discovery – As organizations scale up agents, tools, and MCP servers, those resources end up scattered across clouds, on-premises infrastructure, and SaaS platforms – each with its own registry and metadata. ARD is a new open specification (Apache 2.0) that defines a common way to describe and discover agentic resources, so publishers “describe once” and consumers “discover everywhere” – think DNS, but for agents. AWS contributed feedback but doesn’t own the spec, and it complements the AWS Agent Registry by letting you federate across catalogs without migrating.
• Get started with the Agent Toolkit for AWS in the AWS CLI – A single AWS CLI command (aws configure agent-toolkit) now equips AI coding agents like Kiro, Claude Code, Codex, and Cursor with curated, up-to-date AWS knowledge and a secure connection to thousands of AWS APIs through the AWS MCP Server. If you build with an AI coding assistant, this helps it choose the right services, use modern APIs, and follow security best practices – so it gets AWS code right more often the first time.

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

• AWS Summits – Free in-person events where builders come together to learn, connect, and explore the latest in cloud and AI. Upcoming stops include Zurich (September 2), São Paulo (September 3), Tel Aviv (September 10), and Dubai (September 30). Can’t attend in person? You can stream sessions through the Global Livestream and On-Demand Hub. I’ll be presenting two sessions on generative AI and Amazon Bedrock at the São Paulo Summit – if you’re there, come say hello.
• AWS Community Days – Community-led conferences where content is planned, sourced, and delivered by community leaders. Upcoming events include JAWS SONIC 2026 in Tokyo (September 5) and Warsaw, Poland (September 8).

Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development. Browse here for upcoming AWS-led in-person and virtual events and developer-focused events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Daniel Abib

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

The post AWS Weekly Roundup: Welcome DuckLabs to the team, Agentic Resource Discovery (ARD), and more (August 31, 2026) appeared first on The NOKIA Blog.

In organisations today, data has never been more available. Dashboards and performance decks exist for almost every function — sales, product, marketing, operations — and the tools to build them have never been more accessible. And yet, in weekly standups and quarterly reviews, the same thing happens constantly: someone shares the numbers, the room nods, and the meeting ends without a decision or clear direction.

When that happens, the data usually takes the blame. The numbers weren’t granular enough, the dataset wasn’t complete, we need more information before we can act. But the data is almost never the problem. The reality is that nobody designed it to deliver insights. The chart was built from what was available, not from the question that needed answering. The audience was assumed rather than understood, and what should actually change as a result of seeing this data — that question — was never asked at all.

Data visualisation and UX are solving the same underlying problem: both are trying to move the right information to the right person in a way that changes something. The vocabulary is different, but the underlying challenge is identical, and the moment you start treating them as complementary disciplines is the moment dashboards stop being a passive collection of charts and start doing something functional.

For designers who work with data, analysts who present to non-technical audiences, and marketers who need their numbers to do more than sit in a slide, this read is for you.

The Chart Was Never The Whole Story

In 1973, the statistician Francis Anscombe (PDF) published a paper that made a quiet but clarifying point. He constructed four datasets that are statistically identical: same mean, same variance, same correlation coefficient, and same regression line. Run the numbers on any of them, and they are identical. Plot them, and they could not be more different.

Anscombe’s lesson to statisticians was about diagnosis: visualisation reveals the operational truth that raw numbers conceal.

But visualisation isn’t just diagnostic; it is also communicative. The form you choose is where understanding either emerges or gets lost in the noise. Does your audience walk away with numbers, or with a story they’ll quote and talk about?

One of the most striking examples is Visual Capitalist’s History of Pandemics. Instead of burying the reader in a massive data table of casualty counts, it maps the death toll of major historical pandemics using a proportional bubble layout on a single timeline.

Before your brain reads a single number, your visual system grasps the sheer scale of the Black Death relative to everything else on the page. The right visualisation does not just plot the data but makes the story impossible to miss.

Edward Tufte codified a foundational principle for the craft with his data-ink ratio: every mark on a chart should serve the data, not decorate it. It remains a widely used framework in data visualisation, anchored in the assumption that clarity and visual hygiene are the goal.

For a chart in isolation, that holds. But a chart is never read in isolation: it’s read by a person, in a specific context, under specific pressure. Strip a chart down to its cleanest form, and you might be removing the exact layer of context a decision-maker needs. Simplicity isn’t the goal in itself; appropriate complexity is.

Data is a message, and the right amount of signal depends entirely on who’s receiving it.

Which leads to the core principle of data UX: roughly 80% of the work that determines whether a dashboard succeeds happens before you ever draw a chart.

That dependency is the thread the rest of this piece pulls on.

The 80% That Happens Before The Chart

The high-leverage 80% almost never happens on screen. It happens upstream: before a tool is opened, before a dataset is pulled, before a single design choice is made. It comes down to three questions, and once they become habitual, they change what you notice, what you ask, and what you push back on at the outset of every project.

1. Context: What are we trying to show with this data?
   This is where you define what the visualisations actually need to serve before you touch any raw data. Writing down the precise operational questions, specifically enough to determine what gets pulled and what gets filtered, is what produces a dashboard that helps with decision-making.
2. Audience: Who is this for, and how do they think?
   This is the empathy step. Knowing who’s in the room, what they’re accountable for, and how they engage with data determines how much complexity the visualisation can carry, and how it should be presented.
3. Insight: What should change once this data lands?
   A decision, a new direction, a shift in understanding. If the intended strategic outcome isn’t clear during the design phase, it will remain invisible once the dashboard goes live.

Context: What Are We Trying To Show With This Data?

Most data-heavy projects start backward: teams pull whatever metrics their internal analytics tools already track and build visualisations around them, while the question the data was supposed to answer either gets assumed or never gets asked. This happens simply because we anchor on the data in front of us as the boundary of what’s possible.

Defining a goal first sounds obvious, but in practice, it rarely happens with the necessary clarity. “Show me how the product is performing” is not a goal; “Identify which features drive retention among users who signed up in Q1” is, as it includes three things the first doesn’t: a metric, a population, and an implied action. That specificity is what converts an open-ended exploration into a constrained, answerable design problem. Which one you start from determines everything that follows: what you include, what comparisons matter, and what you leave out entirely.

Starting with available data produces a dashboard that answers no particular question, because it was never built for one — every number is present, none of them pointed anywhere. Starting with the operational question does the reverse: every element on the screen earns its place, because each one is there to help answer it.

For example, consider a UX team trying to fix a leaky checkout flow for an e-commerce website. A data-first approach pulls everything available, from clicks to scroll depth and device types, yielding a massive dashboard that leaves everyone asking, “Okay, but what do we actually change?” A context-first approach starts with a constraint: “At which step of the checkout do users drop off?” By filtering out 90% of the noise, the team builds a simple funnel chart, instantly spots a bottleneck on the payment screen, and knows exactly what to redesign.

Audience: Who Is This For, And How Do They Think?

Designing for an audience comes down to two things: accountability and familiarity.

Familiarity is about data literacy. Do they read charts instinctively, or does a complex visualisation create friction? Handing a dense, multi-layered dashboard to a Head of Sales and a senior analyst is like giving the same map to someone who navigates by landmarks and someone who reads grid coordinates. The data is accurate, but it is only functional for one of them.

Accountability dictates how that complexity must be presented. A chart showing a 12% decline carries vastly different weight for the executive responsible for that number versus the analyst simply reporting it. Understanding your audience means grasping this relationship; data is never processed neutrally when your performance is on the line.

Together, familiarity and accountability decide one practical thing: how much you can put in front of someone.

In data visualisation, simplicity is not a fixed virtue — the right level of it is contingent on who is reading, and what they need to do.

An analyst relies on a high-density environment to conduct diagnostic discovery. By isolating individual behaviour nodes and mapping out raw user flows, they interrogate the data at its atomic level to uncover the hidden insights and underperforming spend that will shape future campaigns.

An executive, by contrast, requires a highly synthesised translation of that data to immediately identify what is driving commercial growth. Tailoring a dashboard to your audience means adjusting the density dial, delivering maximum signal with appropriate complexity for the specific brain in the room.

Insight: What Should Change Once This Data Lands?

Most data projects operate on the comfortable assumption that if a chart is accurate and clear, the insight will take care of itself. In reality, information and insight are entirely different states. Information is what the data shows, whereas insight is the specific decision, shift in understanding, or course correction someone makes as a result of seeing it. If the intended business change isn’t defined before the design begins, a dashboard will default to passive reporting rather than driving action.

Marketing and engineering teams experience the danger of this gap whenever a core business metric suddenly plummets.

A dashboard built for information simply sounds the alarm, showing a chart that tracks a sharp 15% drop in booking rates. Because the data lacks depth, leadership defaults to panic: they immediately call the UX design team, assuming the app is broken or the checkout flow is flawed. Because the data doesn’t pinpoint the core of the problem, it triggers a costly, misplaced fire drill.

A dashboard built for insight isolates the variables required to make an informed decision. Instead of a single, flat booking metric, the visualisation maps the drop against traffic sources and campaign launches — instantly revealing that while app performance and core user conversion are perfectly stable, the sitewide rate was artificially diluted by a massive influx of low-intent click traffic from a newly scaled campaign. The team doesn’t waste time redesigning a functioning app; they get the exact insight needed to pause the underperforming marketing campaign and adjust their acquisition strategy.

Every visualisation implies a next step, even if that step is “nothing needs to change right now.” The question is whether the design makes that implication clear enough for the viewer to recognise it.

From Questions To Dashboard: A Project Walk-through

My aha moment in data visualisation happened during a project for a client-facing B2B SaaS platform focused on enterprise talent management and competency tracking in Pegasystems skills. The platform captured a massive footprint of daily telemetry, and the brief arrived open-ended: “We have an immense archive of user activity, now we need to present it to enterprise teams.”

We could very easily have charted everything that was captured, but we wouldn’t be doing end users any favours if they just ended up looking at a data graveyard. My responsibility immediately moved beyond pure interface craftsmanship; it became about architecting a highly practical tool for real people who would open this dashboard routinely and need it to tell them an honest, immediate story about their workflows.

Here is how the project actually went.

Context

The client believed this massive pool of data could help their users perform better, and wanted an interface that finally enabled that growth. Translating that broad ambition into tangible visualisations required defining the practical mechanics of performance. What variables indicate advancement vs passive usage? What does “perform better” actually mean? How do you measure it?

An obvious candidate was time spent by product. Every platform tracks it. It is easy to show, and it feels meaningful. But time spent is a proxy; it tells you someone was there, not whether they got anything out of it.

The more meaningful signals were competency scores by area, certification completion rates, and historical performance trajectories. Integrating time-spent data alongside these performance metrics added a useful layer of interpretation, helping us surface which modules users were underutilising and whether that directly correlated with lagging scores. Time spent became a supporting signal in the larger story.

The second question was about the appropriate level of granularity. An identical metric carries completely different weight depending on who is looking at it. An individual contributor tracking their own completion rate needs to know if they are pacing correctly, whereas a manager reviewing a team aggregate needs to know precisely who requires immediate support. This distinction shaped every subsequent data-exposure and filtering decision. Defining that structural story early determined exactly what to show, and to whom.

Audience

The most straightforward approach to this design would have been predictable: use the same charts, but offer an individual view and an aggregated team view. Far too many dashboards rely on this shortcut, subtly tweaking the scale of identical data visualisations and labelling it “personalisation.”

An authentic analysis of the audience reveals a much deeper rift. Frontline team leads and individual contributors required distinct narrative structures and design principles.

Having used e-learning platforms myself, I remember the frustration of opening a tool without a clear sense of my current standing, core strengths, or slipping metrics. That personal experience directly guided the individual contributor workspace; it needed to act as a highly tailored, self-directed mirror that was granular, honest, and personal.

Conversely, the manager’s interface had to bypass individual milestones initially to provide a macro pulse check on team vulnerabilities. It was designed around a different operational reality: how is the group progressing, and where are the consistent gaps? The view prioritised the aggregate picture first, while retaining an intuitive path to drill down into tactical day-to-day coordination when needed.

Insight

Our insight strategy was locked in during the early conceptual phase, long before wireframing a single chart. We intentionally abandoned the idea of building a dense, passive data log and focused on pacing the narrative arc.

For individual contributors, the core value was self-direction, ensuring they could glance at the interface on a Monday morning and immediately derive a clear priority list for the week ahead.

For managers, the goal was to fundamentally shift the timing of operational conversations, providing them with the necessary baseline to intervene before a skill gap evolved into a critical project failure. The visualisations needed to surface the exact moments when a human check-in would be useful, shifting their workflow from reactive post-mortems to proactive guidance.

The comparison tool was the highlight nobody had asked for. A manager dashboard and an individual dashboard are easy to anticipate. What is harder to land is: what if a manager wants to compare two specific team members against the same metrics, side by side? That view came from a design assumption, and it turned out to be the feature that resonated most.

The decisions that mattered most in this project weren’t in the initial brief — capturing data they hadn’t thought to request, and structuring it to answer questions they had not previously known how to articulate, which is the core differentiator of a user-centric data strategy.

Designing The Mental Model Early

The choice of visualisation layout must follow the geometric nature of the data itself. For this project, the core design problem was enabling an individual user to answer a specific question at a single glance: across eight distinct competency areas, where are my relative strengths and gaps?

To solve this, we mapped the data using a radar chart. By organising multiple variables across axes radiating from a central point using polar coordinates, the interface connects the data points to form a single, unified shape. An even, balanced polygon instantly signals well-rounded proficiency, while a sharply skewed shape draws the eye immediately to an outlier area.

While a traditional linear bar chart would have forced the viewer to scan eight individual bars and mentally calculate the variance, a concentric, radial layout segments the data layers to make progress tracking and skill gaps immediately readable. When all dimensions share an identical scale and scoring method, the radar chart is not an unconventional aesthetic choice — it is the most functional tool for multi-dimensional analysis.

The colour system was the other decision made early, and I mean early. During the branding exercise, each of the three products was assigned a colour. That colour did not stay in the brand guidelines. It was built into the data model from the beginning, running consistently across every chart, every filter, every breakdown. By the time a user landed on the dashboard for the first time, the mental model was already in place. They had not been taught the language. They already knew it.

What Changed

The shift from passive information to active insight showed up first in how the dashboard was actually used. Following the deployment of the personalised dashboards and side-by-side team comparison tools, weekly active engagement on the platform’s analytics features rose noticeably, per internally reported figures. Managers were no longer opening the tool once a month to pull static reports. They were using it every Monday morning to actively plan their week.

Revenue and user growth also moved in the right direction over the following two quarters, though — as with most single-project outcomes — it’s hard to isolate the dashboard’s exact contribution from everything else that changed at the same time. The client reported churn across the platform falling to one of its lowest points on record. The clearest evidence of impact came from qualitative feedback. Managers reported that instead of using data to dissect a ‘bad’ month after the fact, the visualisations allowed them to instantly spot slipping performance and schedule a quick supportive catch-up before it turned into a real gap.

Closing

Data design reaches its full potential when visual presentation is treated as an upstream architectural choice rather than a downstream formatting step. Bringing structured UX thinking to data turns visualisations into active decision-making engines, ensuring every chart, report, and metric directly serves a human purpose:

• Upstream framing: Grounding every visual choice in a specific operational question rather than defaulting to available metrics.
• Calibrated density: Tuning the level of complexity directly to the literacy and accountability of the specific reader.
• Decision-driven insight: Structuring data to reveal strategic outcomes rather than isolated stats, turning visual signals into immediate operational momentum.

The next time you are tasked with creating a data visualisation — whether it is an enterprise dashboard, an executive report, or a public-facing infographic — step away from the design canvas and BI tools. Focus your initial effort on the human decisions behind the screen. Only then will your data stop being a passive log of the past and start driving the direction of the future.

(yk)

The post Rethinking Data Visualisation: A UX Approach To Dashboards That Actually Drives Decisions appeared first on The NOKIA Blog.

There are also big differences between hosting providers. Some focus on low-cost shared hosting, while others offer managed WordPress hosting, cloud hosting, or plans built for businesses and agencies. The tools included with each plan can vary just as much. Backups, staging sites, caching, security, migrations, CDN access, and developer tools may be included, limited, or cost extra.

This comparison looks at six popular WordPress hosts: WordPress.com, Bluehost, Pressable, Cloudways, Kinsta, and SiteGround. Each provider has been tested and compared across the areas that matter when running a WordPress website.

The goal isn’t to name one host that works for everyone. A personal blog, WooCommerce store, client website, and busy online publication have very different hosting needs. Instead, the results should make it easier to see where each provider works well, where it falls short, and which type of WordPress website it suits best.

Best WordPress Hosting: Quick Recommendations

Not everyone needs to compare every plan and technical detail. These recommendations offer a quick starting point based on common WordPress projects. Each host has been selected for a different type of user, with performance, management tools, support, pricing, and the overall hosting experience taken into account.

Best Overall WordPress Host: WordPress.com

WordPress.com is our best overall host because it combines managed WordPress hosting with the flexibility and ease of use many websites need. It works well for blogs and business sites, with plans suitable for larger publications and WooCommerce stores.

Much of the technical work is handled for you, including caching, security, updates, SSL, and CDN access. All paid plans now support third-party plugins and custom theme uploads. Business adds daily backups, staging, SFTP, SSH, database access, WP-CLI, and other developer tools.

WordPress.com is run by Automattic, a company with deep ties to the WordPress open-source project and WordPress.org. That experience carries through to its managed hosting and support, which is useful when a problem involves both WordPress and the hosting platform.

There is less server maintenance to handle, while developers can choose Business when they need more advanced access. Those needing full server control may prefer cloud or VPS hosting.

Pricing varies between plans, so always compare promotional rates with the normal renewal price.

Further Information:

WordPress.com Plans & Pricing

• Personal: Supports third-party plugins and themes, custom CSS and code, along with security, support, and migration tools.
• Premium: Includes everything in Personal, with additional design, analytics, SEO, advertising, and monetization tools.
• Business: Adds daily backups, staging, SFTP, SSH, WP-CLI, database access, GitHub deployments, and other developer tools.
• Commerce: Includes the tools available with Business and adds WooCommerce and online selling tools.

All data correct as of August, 2026.

Best WordPress Managed Host: Kinsta

Kinsta is our choice for the best managed WordPress host. It is built for WordPress sites that need strong performance, reliable hosting, and useful management tools without the site owner having to manage the server.

Every WordPress site runs in its own isolated container, with access to Kinsta’s cloud infrastructure and a choice of 30 data center locations. Server caching, edge caching, a Cloudflare-powered CDN, security, daily backups, and performance monitoring are included.

Kinsta also provides a strong set of developer tools, including DevKinsta. SSH and SFTP are included with all WordPress hosting plans, WP-CLI is installed by default, and staging gives developers a separate platform to test changes. MyKinsta also offers database management, logs, analytics, and Kinsta’s APM performance tool.

The main drawback is price. Kinsta costs more than budget WordPress hosting, so it is harder to justify for a small personal site. But for businesses, busy publications, stores, and other sites where performance and support matter, the extra cost makes sense.

Further Information:

Kinsta Plans & Pricing

• Single Site Plans: Built for one WordPress website, with several levels based on traffic or bandwidth. All include CDN, edge caching, daily backups, staging, security, migrations, SSH, SFTP, and WP-CLI.
• Multiple Site Plans: Designed for users who manage several WordPress installations. These plans increase the number of sites and available resources while keeping them under one Kinsta account.
• Agency Plans: Made for agencies that manage larger collections of client sites. They provide higher limits and more capacity while retaining the same managed WordPress platform and development tools.
• Custom Plans: Available for WordPress projects that need more resources than the standard plans provide. These are better suited to very busy sites and projects with unusual hosting requirements.

All data correct as of August, 2026.

Best WordPress Blogger Host: Cloudways

Cloudways is our choice for the best WordPress blogger host, particularly for established blogs and publications that have grown beyond basic shared hosting. It provides managed cloud hosting while still giving site owners more control over server resources than most managed WordPress platforms.

With Cloudways Flexible, WordPress can run on cloud infrastructure from DigitalOcean, Vultr, Linode, AWS, or Google Cloud. You choose the provider, server size, and location, while Cloudways handles much of the server setup and management. Resources can also be increased as traffic grows.

WordPress tools include server caching, staging, cloning, automated backups, SSL, migrations, security tools, SSH, SFTP, Git, and WP-CLI. Multiple WordPress sites can also run on the same server, provided it has enough resources.

This flexibility is why Cloudways works well for established bloggers. A growing publication can increase its hosting resources without moving to another platform. The tradeoff is complexity. There are more server settings to understand than with WordPress.com or Kinsta. But for bloggers comfortable managing WordPress, that extra control can be useful.

Further Information:

Cloudways Plans & Pricing

• DigitalOcean: The most practical starting point for many WordPress blogs. Plans provide dedicated cloud resources, SSD or NVMe storage, vertical scaling, and a choice of server locations.
• Vultr: Offers a broad choice of data center locations and server configurations. It is useful when the main audience is closer to a Vultr location or a different server setup is required.
• Linode: Another option for blogs that want predictable cloud resources and a choice of server locations while keeping Cloudways responsible for much of the server management.
• AWS: Uses Amazon Web Services infrastructure and provides more server configuration choices. It is generally more expensive and is better suited to sites with requirements that justify the added cost.
• Google Cloud: Runs WordPress on Google Compute Engine through the Cloudways dashboard. Like AWS, it provides flexible server resources but usually costs more than the simpler cloud options.

All data correct as of August, 2026.

Best WordPress Developer Host: Pressable

Pressable is our choice for the best WordPress developer host. It is a fully managed WordPress platform from Automattic, the company behind WordPress.com and WooCommerce, and runs on its WP Cloud infrastructure. That WordPress focus is one of the main reasons it works so well for development work.

Developers get staging and sandbox environments for every site, along with SSH, SFTP, WP-CLI, Git, database access, and other tools needed for building and maintaining WordPress sites. DupliKits can also be used to create reusable site templates, which can save time when several projects start with a similar setup.

Pressable handles much of the hosting work in the background. Hourly and daily backups, caching, security monitoring, automatic scaling, a global CDN, and site migrations are included. There is also 24/7 support from a team that works with WordPress every day.

Pricing is higher than budget hosting, but that is not really what Pressable is competing with. The value comes from reducing server maintenance while keeping the development tools needed for regular WordPress work. For freelance developers and teams managing several sites, that balance makes Pressable a strong choice.

Further Information:

Pressable Plans & Pricing

• Signature 1: Supports one WordPress site, 30,000 monthly visits, and 20GB of storage. Suitable for developers managing a single business or client site.
• Signature 2: Supports three WordPress sites, 50,000 monthly visits, and 30GB of storage. Useful for developers who manage a small collection of client sites.
• Signature 3: Supports five WordPress sites, 75,000 monthly visits, and 35GB of storage. Offers more room for freelancers maintaining several active WordPress projects.
• Signature 4: Supports ten WordPress sites, 150,000 monthly visits, and 50GB of storage. Excellent for developers and small teams that manage a larger group of websites.
• Signature 5 and Above: Plans increase from 20 to 100 WordPress installations, with higher traffic and storage limits. They are aimed at larger development teams and agencies with many sites.

All data correct as of August, 2026.

Best WordPress Agency Host: SiteGround

SiteGround is our choice for the best WordPress agency host. It combines managed WordPress hosting with tools for building, testing, managing, and handing client websites once a project is complete and launched. GrowBig and GoGeek also allow multiple websites under one account, which makes them better suited to agency work than the single site StartUp plan.

SiteGround handles many routine hosting jobs, including WordPress updates, daily backups, caching, CDN access, SSL, security, and server maintenance. GrowBig and GoGeek also include WordPress staging, while developers have access to SSH, SFTP, WP-CLI, databases, and other common development tools.

The agency tools are the main reason for choosing SiteGround here. Team members can be added as collaborators without sharing the main account login. Finished websites can also be transferred to clients while the agency remains attached as a collaborator. GoGeek adds white label client access, so clients using Site Tools do not see SiteGround branding.

The main drawback is renewal pricing. SiteGround offers large discounts for the first year, but regular rates are much higher. Agencies should use those renewal prices when calculating the long-term cost of hosting client websites.

Further Information:

SiteGround Plans & Pricing

• StartUp: Supports one website, around 10,000 monthly visits, and 10GB of storage. It includes managed WordPress tools, daily backups, caching, CDN access, SSL, and email. The single site limit makes it less useful for agency hosting.
• GrowBig: Supports unlimited websites within its resource limits, around 100,000 monthly visits, and 50GB of storage. It adds more server resources, WordPress staging, on-demand backups, and collaboration tools. This is a good starting point for smaller agencies.
• GoGeek: Supports unlimited websites within its resource limits, around 400,000 monthly visits, and 100GB of storage. It provides more server resources and adds white-label client access, making it the strongest shared hosting plan for agencies managing client websites.

All data correct as of August, 2026.

WordPress.com is our choice for the best WooCommerce host. WooCommerce can now be installed on any paid WordPress.com plan, giving store owners more choice over how much hosting and how many store tools they actually need.

The close connection with WooCommerce is a major advantage. WordPress.com and WooCommerce are both part of Automattic, so the hosting platform is built and supported by people with a deep understanding of WordPress and WooCommerce. This becomes useful when a problem involves the store, WordPress, and the hosting environment.

For larger stores, the Commerce plan includes everything in Business, along with WooCommerce and additional selling tools. This includes real-time backups, staging, SFTP, SSH, database access, payment tools, shipping integrations, automated taxes, product recommendations, and other WooCommerce extensions.

WordPress.com also handles much of the hosting work, including caching, security, updates, SSL, and server management. Store owners can concentrate on products, orders, customers, and sales rather than maintaining the server. It may cost more than basic WooCommerce hosting, but the managed setup makes more sense as a store becomes busier.

Further Information:

WordPress.com Commerce Plans & Pricing

• Personal: WooCommerce can be installed alongside third-party plugins and themes. It provides a low-cost starting point for a very small store, but it does not include the advanced backups, staging, and developer tools available on higher plans.
• Premium: Includes everything in Personal, along with additional design, analytics, SEO, advertising, and monetization tools. WooCommerce can be installed, although growing stores may benefit from the stronger hosting tools available with Business.
• Business: Adds real-time backups, staging, SFTP, SSH, database access, WP-CLI, and other developer tools. WooCommerce can be installed normally, making Business a good choice for store owners who want to build their own WooCommerce setup.
• Commerce: Includes everything in Business and adds WooCommerce with a collection of store tools and extensions. It supports unlimited products and orders, payments, shipping integrations, automated taxes, product recommendations, abandoned cart emails, and other selling tools.

All data correct as of August, 2026.

Best Budget WordPress Host: Bluehost

Bluehost is our choice for the best budget WordPress host. Its lower-priced plans provide the tools needed to run a blog, portfolio, small business site, or other WordPress project without paying the higher monthly cost of premium managed hosting.

WordPress hosting includes managed updates, SSL, CDN access, malware scanning, backups, migration tools, and a domain for the first year. Bluehost also provides a 99.99% uptime SLA and several server locations. WordPress setup and site management are handled through a dashboard aimed at users who may not have much hosting experience.

Starter is the obvious choice for smaller sites, while Business provides more storage, supports more websites, and adds further security and backup tools. Higher performance plans are available when a site needs more resources, so there is room to move beyond the entry-level packages.

The main issue is renewal pricing. Bluehost offers large discounts for the initial term, but the regular price is considerably higher. That needs to be included when comparing it with other budget hosts. For a smaller WordPress site where keeping the initial cost low matters, Bluehost still provides a strong set of hosting tools for the money.

Further Information:

Bluehost Plans & Pricing

• Starter: Supports up to ten websites with 10GB of NVMe storage. It includes managed WordPress updates, CDN access, SSL, malware scanning, weekly backups, migration tools, and a domain for the first year. It is the best fit for blogs and smaller WordPress sites.
• Business: Supports up to 50 websites with 50GB of NVMe storage. It provides more room for growing sites and is better suited to users managing several WordPress installations or a busier business website.

All data correct as of August, 2026.

Most WordPress hosts include more than just server space. Backups, staging sites, caching, CDN access, SSL, security, and migration tools reduce the amount of work needed to maintain a site. A host may advertise backups but run them only weekly, or offer staging only on its more expensive plans.

There is also a difference between a tool being included and it being well connected to the hosting platform. The right setup depends on whether the project needs simple management or greater control.

Developer requirements can be very different from those of someone running a single blog. Staging sites, SSH, SFTP, WP-CLI, Git, database access, logs, and local development tools will save a lot of time. They are also useful when testing updates or tracking down a problem without making changes directly on a live site.

Cloudways provides the greatest server control of the hosts compared here, while Kinsta and Pressable combine strong development tools with a more managed setup. WordPress.com also provides many common development tools on suitable plans. SiteGround covers most everyday development needs, while Bluehost provides enough access for many smaller projects.

The main question is how much control is actually required. More server access is useful for some developers, but it also means taking responsibility for more of the hosting setup.

AI tools are starting to appear in WordPress hosting platforms. Many providers now use it to help monitor websites, detect security threats, improve performance, and simplify routine management tasks. Some platforms also include AI assistants that can answer questions, troubleshoot common issues, or help configure hosting settings.

While these tools can save time, they shouldn’t be the main reason for choosing a host. Performance, reliability, support, and security still matter more. AI is best viewed as a useful addition rather than a replacement for a well-managed hosting platform.

The table below shows how the six hosts in this comparison currently approach AI.

All data correct as of August, 2026.

WordPress.com currently has the most complete AI setup of the hosts compared here. Its AI website builder can create a new WordPress site, while WordPress Agent continues working inside the site after it has been built. It can help with content, layouts, images, pages, and other editing jobs.

The more interesting addition for developers is MCP support. WordPress.com can connect with outside AI agents such as ChatGPT, Claude, Cursor, and other compatible tools. These agents can read site information and, with permission, carry out tasks such as creating posts, managing content, working with media, and changing site settings. MCP is available across paid WordPress.com plans.

Other hosts are using AI in narrower ways, often concentrating on initial site creation, content generation, or hosting support. Those tools can still be useful, but they should not be a major reason to choose a host on their own. Hosting performance, support, backups, security, development tools, and long-term cost still matter much more.

How to Choose a WordPress Host

Choosing a WordPress host is one of the biggest decisions you’ll make for your website. Your hosting provider affects performance, reliability, security, and how easy your site is to manage. The right host will keep your website running smoothly and give you access to support when you need it.

No hosting plan suits every website. A personal blog has different needs than an online store or an agency managing client sites. Knowing what your website requires before comparing providers will help you narrow your options and avoid paying for services you won’t use.

When comparing hosts, look at uptime, page speed, server locations, backups, staging sites, security, support, and renewal pricing. These factors often matter just as much as the monthly cost.

The articles below cover the main topics involved in choosing a WordPress host. Whether you’re building your first website or moving to a new provider, they’ll help you compare your options and make a confident choice.

WordPress Hosting for Different Needs

Choosing a hosting plan that matches your needs can improve performance, make your website easier to manage, and help you avoid paying for resources you won’t use. As your website grows, you can move to a plan with more storage, power, and management tools.

Whether you’re building your first WordPress website, running an online store, or managing client sites, understanding the strengths of each hosting option will make it easier to choose the right provider.

WordPress Performance & Security

A good hosting provider gives your WordPress website a solid foundation, but hosting is only part of the picture. Your website’s setup, the tools you use, and your security practices all affect performance, reliability, and keeping your visitors safe.

Many WordPress hosts include caching, content delivery networks (CDNs), backups, malware scanning, and SSL certificates. Knowing how these features work will help you get more from your hosting plan. Regular maintenance also helps keep your website running well. Updating WordPress, plugins, and themes, monitoring performance, and following good security practices can prevent many common problems.

The articles below cover the tools and techniques that can improve website speed, strengthen security, and simplify day-to-day management.

Managing Your WordPress Hosting

Launching a WordPress website is only the start. Regular maintenance helps keep your website secure, reliable, and running smoothly. Many hosting providers include tools that simplify routine tasks, but it still helps to understand how they work.

Common tasks include migrating websites, installing SSL certificates, updating PHP, managing domains, restoring backups, and monitoring uptime. Learning these skills will help you solve problems more quickly and get more from your hosting provider.

Whether you’re looking after one website or several client projects, a regular maintenance routine can reduce downtime and keep your websites in good shape.

The articles below cover the most common hosting management tasks and show you how to handle them with confidence.

• When Your WordPress Website Has Outgrown its Hosting

  How do you know when a hosting change is needed? Here are a few things to consider.

• Hosting Your Client’s WordPress Website

  Learn how to avoid common hosting mistakes when hosting WordPress sites for clients.

• Understanding Your Web Host

  Your host may not cover all your needs. Take these steps to ensure your website runs smoothly.

• Building a Performant WordPress Website

  Learn how to boost performance with better hosting, lighter themes, and smart optimzation strategies.

• What To Know About Your Client’s WordPress Website

  We cover what to check first, including themes, plugins, custom code, hosting, and unused tools.

• Add an SSL Certificate to Your WordPress Website

  A quick SSL certificate setup can mean big benefits for your site and visitors.

• WordPress Website Email Issues

  Learn how to test sending, check logs, change the from address, set up SMTP, or use a mail delivery service.

Final Verdict: Which WordPress Host Should You Choose?

WordPress.com is our best overall choice because it works for a broad range of WordPress projects while handling much of the hosting management. It is suitable for blogs, business websites, larger publications, and WooCommerce stores, with access to plugins, themes, staging, backups, security, caching, and development tools on suitable plans.

Kinsta is the better choice for users who want premium managed WordPress hosting and are willing to pay more for it. Cloudways suits established bloggers and publications that want greater control over server resources. Pressable is our choice for developers because its managed platform includes a strong set of WordPress development tools.

SiteGround makes sense for agencies managing several client websites, particularly when collaboration and site management tools are needed. And Bluehost remains the budget choice for smaller WordPress sites where keeping the hosting bill low matters more than having advanced management tools.

No host wins every category because WordPress websites have different requirements. The best option is the provider that has enough performance and resources for the site, includes the tools that will actually be used, and remains affordable after introductory pricing ends. For most WordPress users looking for a balance across those areas, WordPress.com gets our overall recommendation.

WordPress Hosting FAQs

WordPress hosting can look more complicated than it needs to be. Hosting companies use different plan names, prices, limits, and technical terms, even when the services are similar. These answers cover some of the most common questions about choosing and using WordPress hosting.

• What is the best hosting for WordPress?
  WordPress.com is our best overall WordPress host because it combines managed hosting with good performance, security, backups, CDN access, and WordPress support. Kinsta is our choice for managed hosting, Cloudways for bloggers, Pressable for developers, SiteGround for agencies, and Bluehost for smaller budgets.
• How much should WordPress hosting cost?
  Basic WordPress hosting can cost only a few dollars per month, while managed hosting may cost $20 to $50 per month or more. Larger sites and stores can cost considerably more. Always check the regular renewal price because introductory rates can make the first term look much cheaper.
• Do I need managed WordPress hosting?
  No. Smaller blogs, portfolios, and business sites can run well on lower-cost hosting. Managed WordPress hosting becomes more useful for busy sites, stores, and businesses that benefit from automatic backups, security, caching, staging, and WordPress-focused support.
• Can I change WordPress hosts later?
  Yes. WordPress sites can be moved between hosting providers. Many hosts provide migration tools or will move the site for you. Keep the old hosting active until the new site has been checked and the domain is pointing to the new server.
• Does better hosting make WordPress faster?
  Better hosting can improve WordPress performance by providing faster servers, more resources, caching, and CDN access. But hosting cannot fix every problem. Large images, heavy themes, slow plugins, external scripts, and database problems can still make a site slow.
• Is WordPress hosting different from regular web hosting?
  WordPress hosting is web hosting set up with WordPress in mind. It may include WordPress updates, caching, backups, security, staging, WP-CLI, and specialist support. WordPress can also run on regular hosting as long as the server meets its requirements.
• Do I need separate hosting for WooCommerce?
  No. WooCommerce runs on WordPress and does not require a separate type of hosting. But stores can need more server resources because carts, checkout pages, customer accounts, orders, and stock changes create more database activity than a typical WordPress site.
• How much traffic can WordPress hosting handle?
  There is no single number. A cached blog and a busy WooCommerce store can use very different resources even with the same number of visitors. Check the host’s traffic, bandwidth, storage, CPU, memory, and other resource limits before choosing a plan.

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Designers, photographers, illustrators, and other creative professionals also have practical requirements. Staging makes it possible to test layout changes privately. Automatic backups protect client work, while collaboration tools can help freelancers and agencies manage several websites. Good support is also valuable when a technical problem interrupts a project or affects a live site.

This collection compares WordPress hosts based on the features that matter most to creatives. These include performance, media handling, storage, theme and plugin support, staging, backups, client management, and long term cost.

Some options are suited to a single portfolio, while others provide the tools needed to manage client websites or run a creative agency. The best choice depends on the type of work being presented and how much control the website owner needs.

Quick Recommendations

Each of these hosts suits a different type of creative website. WordPress.com offers the simplest overall package, while Kinsta and Pressable provide stronger tools for studios and client work. Cloudways gives experienced users more control, and Rocket.net is a good match for image heavy portfolios.

WordPress.com is the best choice for creatives who want managed hosting with little technical maintenance. Kinsta is better suited to established studios that place greater value on performance, staging, and developer tools. Pressable is worth considering when several client websites need to be built, tested, and managed from one account.

Cloudways offers more flexibility but requires greater technical confidence. Rocket.net offers managed performance and a built in CDN for visual websites with an international audience. SiteGround and Hostinger cost less at the start, although their renewal prices are considerably higher.

Prices shown are monthly equivalents based on the billing terms displayed by each provider.

What Creatives Should Look for in WordPress Hosting

Creative websites often contain large images, complex layouts, video, animation, and interactive features. These elements can place more pressure on hosting than a basic blog or small business website.

• Image and media performance: Look for server caching, CDN access, and enough storage for a growing media library. Image compression and support for WebP or AVIF can also help portfolio pages load faster.
• Theme and design tool support: The host should support custom themes and third party plugins. Designers using page builders, animation plugins, or Advanced Custom Fields may also benefit from higher PHP memory and stronger server resources.
• Staging and development tools: Staging allows design changes, theme updates, and new layouts to be tested privately. SFTP, SSH, WP CLI, Git, database access, and PHP controls are also useful for custom and client websites.
• Client and team management: Freelancers and agencies may need collaborator accounts, separate permissions, site cloning, and a central dashboard for managing several projects. Site transfer and billing handover tools can also simplify client delivery.
• Backups and site recovery: Automatic backups provide protection when an update, code change, or plugin conflict causes a problem. Check how often backups are created, how long they are stored, and whether the website can be restored with a single click.
• Pricing and renewal costs: Check the regular renewal rate rather than relying on the introductory price. Storage, traffic, staging, backups, email, and additional websites may depend on the plan, so the cheapest option is not always the best value.

Top WordPress Hosts for Designers & Creatives

1. Best Overall for Creatives: WordPress.com

WordPress.com is a good choice for creatives who want managed hosting without spending much time on server maintenance. Updates, security, backups, caching, and performance tools are handled for you, leaving designers to manage their website through a familiar WordPress dashboard.

The Business plan is the best fit for a professional portfolio or creative business. It includes 50GB of storage, a global CDN, staging, real time backups, and 250GB of separate VideoPress storage. Custom themes and third party plugins are supported, so designers are not limited to the themes and tools supplied. SFTP, SSH, WP CLI, Git commands, and GitHub deployments are also available for more advanced projects.

The main drawback is cost. Each website requires its own plan, which may not suit freelancers or agencies managing a large number of client sites. However, it remains a practical option for individual designers, photographers, illustrators, and creative businesses that want strong performance without dealing with the technical side of hosting.

• Recommended plan: Business.
• Best for: Professional portfolios and creative business websites.
• Key strength: Managed hosting with strong media and development tools.
• Main drawback: A separate plan is required for each website.

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2. Best for Creative Studios: Kinsta

Kinsta is well suited to established designers and creative studios that need dependable managed hosting for a professional portfolio, company website, or online store. Its platform handles caching, security, backups, and server maintenance, while the MyKinsta dashboard provides access to staging, analytics, redirects, and other WordPress tools.

Every plan includes a global CDN, automatic daily backups, free migrations, application performance monitoring, and malware removal. Designers can create a staging copy before changing a layout, installing a new plugin, or updating a custom theme. Site transfer and user access controls are also useful when a studio works with outside developers or needs to hand a project over to a client.

The main limitation for visual websites is storage. The entry plan includes 10GB, which may be restrictive for a large photography or video portfolio. Visitor, bandwidth, storage, and CDN limits can also lead to additional charges. Kinsta costs more than shared hosting, but its performance and management tools make it a strong option when the website supports an established creative business.

• Recommended plan: Single 20GB
• Best for: Established creative studios and professional portfolios
• Key strength: Managed performance and development tools
• Main drawback: Limited storage on the entry plan

Further Information:

3. Best for Freelance Designers: Cloudways

Cloudways is a good option for freelance designers who want more control than a typical managed WordPress plan provides. Websites run on cloud servers, with Cloudways supplying the dashboard, security tools, caching, backups, staging, and technical support.

The Flexible Micro plan includes 2GB of RAM, one virtual CPU, 50GB of storage, and 2TB of transfer bandwidth. It can host multiple WordPress installations, although every site shares the same server resources. Designers can create staging sites, clone existing projects, change the PHP version, and access files through SFTP or SSH. Monthly and hourly billing also make it possible to increase server resources as client work grows.

Cloudways requires more technical knowledge than WordPress.com or Kinsta. The dashboard includes server settings that may be unfamiliar to less experienced users. Cloudflare Enterprise is a paid addition on Flexible plans, while offsite backup storage is charged separately. Business email is not included. These extra costs need to be considered, but the flexible setup remains useful for freelancers managing several client websites.

• Recommended plan: Flexible Micro
• Best for: Freelance designers managing several websites
• Key strength: Flexible cloud resources and server controls
• Main drawback: More technical than fully managed alternatives

Further Information:

4. Best for Managing Client Websites: Pressable

Pressable is designed for WordPress professionals who build and maintain websites for clients. Its dashboard provides access to production, staging, and sandbox environments, giving designers separate spaces for development, testing, and client review.

The Signature plans include automatic backups, a global CDN, edge caching, free migrations, malware monitoring, and WordPress support. Granular collaborator permissions allow developers, designers, and clients to receive different levels of access. Pressable also provides site templates through DupliKits, which can save time when several projects use the same plugins, theme, or starting configuration.

The Signature 1 plan is suitable for a single client website, while higher plans support several installations. Storage and monthly visits are limited, and additional usage can result in extra charges. Pressable is also more expensive than placing several sites on one shared hosting account. However, its staging, sandbox, cloning, and collaboration tools make it a practical choice for designers who include hosting and maintenance in their client services.

• Recommended plan: Signature 1
• Best for: Designers building and maintaining client websites
• Key strength: Staging, sandbox, and collaboration tools
• Main drawback: Additional charges for storage and visitor overages

Further Information:

5. Best for Small Creative Studios: SiteGround

SiteGround is a practical choice for designers and small creative studios that need to host several websites without paying for each installation separately. Its custom Site Tools dashboard provides access to domains, email, backups, caching, security, and development tools.

The GrowBig plan supports unlimited websites and includes 50GB of storage, a CDN, daily backups, on demand backups, collaborators, and WordPress staging. Designers can test theme and layout changes before publishing them, while collaborator accounts allow clients or team members to access individual websites. Email hosting is included, which can reduce the need for another paid service.

The introductory price is low, but the renewal rate is much higher. SiteGround also uses shared hosting, so demanding WooCommerce sites or portfolios with heavy media libraries may require a more powerful plan. Even so, GrowBig offers a useful balance of storage, development tools, and multiple site support for small studios. Agencies needing Git, white label access, and priority support should consider the GoGeek plan.

• Recommended plan: GrowBig
• Best for: Small creative studios hosting several websites
• Key strength: Multiple websites with staging and collaboration tools
• Main drawback: A large price increase at renewal

Further Information:

6. Best for Portfolio Performance: Rocket.net

Rocket.net is a managed WordPress host built around Cloudflare Enterprise. Its global CDN caches website content closer to visitors, which can improve the delivery of portfolio images, fonts, scripts, and other visual assets to an international audience.

The Starter plan includes one WordPress website, unmetered visitors, 10GB of storage, 50GB of bandwidth, unlimited PHP workers, and a 99.99% uptime guarantee. Security features include a web application firewall, malware protection, and automatic updates. Daily backups are retained for 30 days, while free migrations are available for existing websites.

The 10GB storage allowance is the main concern for photographers and other creatives with large media libraries. Storage overages cost $2 per GB, so images should be resized and compressed before being uploaded. Video should usually be hosted through a separate video platform. Rocket.net is not the cheapest option for a single website, but its CDN, managed security, and straightforward pricing make it a strong choice when portfolio performance matters more than storage capacity.

• Recommended plan: Starter
• Best for: Portfolios serving visitors in several countries
• Key strength: Cloudflare Enterprise CDN included with every plan
• Main drawback: Only 10GB of storage on the Starter plan

Further Information:

7. Best Budget Option: Hostinger

Hostinger offers low cost managed WordPress hosting for designers creating a first portfolio, personal website, or small creative business site. Its custom dashboard handles WordPress installation, updates, backups, domains, email, and other common hosting tasks.

The Unlimited plan is the better choice for creative work. It includes 50GB of NVMe storage, daily backups, a CDN, and support for unlimited websites. One click staging allows designers to test theme updates and layout changes before publishing them. Email accounts, ecommerce support, and AI tools are also included, although some features are only available during the initial contract period.

The advertised monthly price requires payment for 48 months in advance. The plan renews at a much higher rate, so the full contract cost should be considered before signing up. Unlimited website claims are also governed by a fair usage policy, which means the account still has resource limits. Hostinger does not provide the same level of server control or advanced management tools found as other hosts, but it offers useful features for creatives working with a smaller budget.

• Recommended plan: Unlimited
• Best for: New portfolios and budget conscious creative businesses
• Key strength: Generous storage and multiple website support
• Main drawback: The lowest price requires a four year commitment

Further Information:

Performance Tips for Creative WordPress Websites

Good hosting can only do so much if images, video, fonts, and animation are not properly managed. The following steps can help keep a visual WordPress website responsive.

• Resize images to match the largest dimensions at which they will be displayed.
• Use WebP or AVIF files where browser support and image quality allow.
• Compress portfolio images before uploading them to WordPress.
• Use lazy loading so images outside the visible area are not loaded immediately.
• Serve images, scripts, and stylesheets through a CDN.
• Host long or high resolution videos through a dedicated video platform.
• Limit unnecessary font files, background videos, and complex animations.
• Test important portfolio pages on mobile devices and slower connections.
• Monitor storage and bandwidth use as the media library grows.

How the Hosts Were Assessed

Each host was assessed using its published plan details, feature documentation, pricing, and suitability for creative WordPress websites. The recommendations place particular weight on the following factors:

• Performance when serving images and other visual content.
• Storage, bandwidth, and visitor limits.
• Support for custom themes, plugins, and design tools.
• Staging, backups, site cloning, and recovery options.
• Developer access through SFTP, SSH, WP CLI, or Git.
• Tools for managing team members and client websites.
• Introductory pricing, renewal rates, and additional charges.

The final rankings reflect how useful each hosting service is for designers, photographers, freelancers, and creative studios. Pricing and plan limits were checked against the information published by each provider while this article was being prepared.

Final Verdict

WordPress.com is the best overall choice for designers and creatives who want managed hosting with strong media support. The Business plan includes 50GB of storage, separate VideoPress storage, staging, backups, a CDN, and developer tools. It is particularly well suited to professional portfolios and creative business websites.

Kinsta is a stronger option for established studios that value managed performance and detailed site controls. Pressable provides useful staging, sandbox, cloning, and collaboration features for designers maintaining client websites. Cloudways offers more server control and flexible resources, although it requires greater technical confidence.

SiteGround works well for small studios that want to host several websites from one account. Rocket.net is suited to portfolios with an international audience, but its storage limits need careful management. Hostinger is the most affordable option at the start and provides enough storage for a new portfolio, though its long contract and higher renewal rate should be considered.

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Today, we are announcing the general availability of AWS Glue 6.0, delivering 30% lower pricing than previous AWS Glue versions and introducing full support for Apache Iceberg v3 features. AWS Glue 6.0 is built on a fully modernized runtime, Apache Spark 4.1, Python 3.12, and Scala 2.13, delivering faster performance.

With this release, AWS Glue provides the most complete Iceberg v3 implementation on any fully serverless managed Spark service, along with new capabilities that simplify ETL authoring, improve PySpark performance, and enable real-time streaming with single-digit millisecond latency.

What is new in AWS Glue 6.0
AWS Glue 6.0 delivers the complete Apache Iceberg v3 specification, built on Iceberg 1.11.0. The headline feature is the VARIANT data type with shredding support, which achieves faster query read performance compared to traditional string data type columns for semi-structured data.

With VARIANT shredding, you can store and query JSON, logs, and event data without flattening schemas, eliminating duplicate data copies, custom parsing code, and pipeline breakage when schemas change. This capability transforms how teams handle semi-structured data at scale.

Additional Iceberg v3 capabilities include:

• Geometry and Geography data types: Enable native spatial processing for GIS analytics, location intelligence, and geospatial data pipelines directly on managed Spark.
• Nanosecond-precision timestamps: Support IoT sensor data, scientific computing, and high-frequency financial workloads that require precision beyond standard milliseconds.
• Unknown type handling: Process data with unexpected or evolving schemas without pipeline failures, providing resilience against upstream schema changes.

AWS Glue 6.0 also includes most significant upgrade in Spark 4.1, the modern runtime engine:

• Spark declarative pipelines: Spark Declarative Pipelines introduces a simplified approach to ETL authoring. Data engineers declare transformations, specifying what data should look like, while the engine automatically determines execution order and optimization. This reduces the complexity of pipeline development and eliminates manual orchestration overhead.
• Arrow-native Python UDFs and UDTFs: AWS Glue 6.0 introduces Arrow-native execution for Python User-Defined Functions (UDFs) and User-Defined Table Functions (UDTFs). This eliminates serialization overhead between Python and the JVM, improving PySpark performance for complex transformations.
• Real-time streaming mode: For stateless streaming use cases, AWS Glue 6.0 introduces a real-time streaming mode that achieves single-digit millisecond latency. Built on Spark 4.1’s Real-Time Mode with Glue-optimized execution, this capability supports real-time event processing, low-latency data transformation pipelines, and time-sensitive data routing.

Getting started with AWS Glue 6.0
No API changes are required to use AWS Glue 6.0. You can select the new version using the existing --glue-version parameter in the create-job or update-job APIs through AWS Command Line Interface (AWS CLI), AWS SDK, AWS Glue Studio, Amazon SageMaker Unified Studio, and your preferred IDE.

To get started with AWS Glue 6.0 jobs in the AWS Glue Studio console, open the AWS Glue job and on the Job Details tab, choose the version Glue 6.0 – Supports Spark 4.1, Scala 2, Python 3. You can create new AWS Glue jobs on AWS Glue 6.0 to get the benefit from the improvements, or migrate your existing AWS Glue jobs.

To start using AWS Glue 6.0 on an AWS Glue Studio notebook or an interactive session through a Jupyter notebook, set 6.0 in the %glue_version magic. You can also upgrade existing jobs to Glue 6.0 using the Spark upgrade agent on AWS Glue Studio or use the auto-upgrade feature in their existing Glue jobs to automatically upgrade them to Glue 6.0.

To learn more, visit the AWS Glue 6.0 version detail and Migrating AWS Glue for Spark jobs to AWS Glue version 6.0 in the AWS documentation.

Now available
AWS Glue 6.0 is generally available today in all AWS Regions where AWS Glue operates. For Regional availability and a future roadmap, visit the AWS Capabilities by Region. If you want to call APIs, search documentation, find regional availability, and check troubleshooting about this new feature, try using the AWS MCP Server and plugins with your preferred AI tool.

You pay an hourly rate, billed by the second, for crawlers (discovering data) and extract, transform, and load (ETL) jobs (processing and loading data). For the AWS Glue Data Catalog, you pay a simplified monthly fee for storing and accessing the metadata. The first million objects stored are free, and the first million accesses are free. To learn more, visit AWS Glue Pricing page.

Give it a try in the AWS Glue Studio console, and send feedback to AWS re:Post for AWS Glue or through your usual AWS support contacts.

— Channy

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