AWS Security Agent adds threat modeling, Kiro power and Claude Code plugin, and more

At re:Invent 2025, we previewed AWS Security Agent (now part of AWS Continuum), a frontier agent that proactively secures your applications throughout the development lifecycle across all your environments. You can perform on-demand penetration testing customized to your application, discovering and reporting security risks verified through exploitability testing.

Since the preview, we announced general availability for on-demand penetration testing and the preview of full repository code review that performs deep, context-aware security analysis of your entire codebase.

Today, we’re introducing more features based on customer feedback:

• Code review updates (Preview) — You can now use pull request scanning with remediation, security requirements packs, and simulated validation. New integrations support GitHub, GitLab, Bitbucket, and Confluence.
• Threat modeling (Preview) — AWS Security Agent analyzes your design documents or application source code, understands the full context of your application architecture and identifies threats with recommended mitigations using the STRIDE framework.
• Kiro power, Claude Code plugin, and MCP integration — You can run code reviews, generate threat models, and remediate findings directly from your IDE, CLI, or any AI-powered IDE through an open MCP integration, with results surfacing inline without any context switching.

Let’s take a closer look at each launch!

Code review updates
You can now connect to GitLab and Bitbucket in addition to GitHub— supporting both SaaS and self-hosted versions, so you can trigger scans regardless of where code lives. You can also integrate Confluence to reference your existing documentation as context for reviews.

To get started, choose Enable code review or update your code review setting in the Security Agent console.

AWS Security Agent introduces deep, reasoning-based analysis on every pull request as well as full repository to identify complex vulnerabilities that go beyond pattern-matching. It checks against your organizational security requirements and common security risks to catch what other tools can’t. To get started, access the Security Agent web application and run your code review.

You’ll receive fix commits and remediation guidance directly in your GitHub, GitLab, or Bitbucket workflow, while your security teams configure the repositories to be monitored and intervene on critical issues. AWS Security Agent validates findings in simulated environments to demonstrate proof of exploitability. This embeds security expertise across all repositories, reducing security-related delays in the development pipeline.

To learn more about new code review features, visit Create a code review in the AWS Security Agent User Guide.

Design review updates
You can continuously validate your security requirements across every design and code review with managed compliance packs: AWS WAF, NIST CSF, PCI DSS, and AWS best practices, or import your own organizational requirements directly from internal documents or Confluence. Every finding maps back to your compliance posture, so teams stay audit-ready as they build.

To learn more, visit the design review documentation.

Threat modeling
AWS Security Agent generates threat models based on your design documentation or code repository, creates and build context about the application, including data flows, architecture, and trust boundaries. It maps out all components of your application, identifies potential threat actors and attack vectors, determines where weaknesses may exist, and prioritizes threats so you know what to address first.

To get started, choose Enable threat model and Connect source code repository in the Security Agent console.

To learn more, visit the threat modeling documentation.

Kiro power and Claude Code plugin for Security Agent
AWS Security Agent introduces a new Kiro power and Claude Code plugin (coming soon) and can be integrated with any AI IDE through an open MCP integration to secure your applications. You can trigger threat models and code reviews directly from your IDE, with results surfacing inline without any context switching.

To get started, install the Kiro power, and run your prompts. The Kiro power uses the AWS Security Agent MCP server. You can get started with the power by asking “Set up AWS Security Agent“. Kiro will check if you have an Agent Space and ask if you would like to use the existing one or create a new one.

With the Kiro power for Security Agent, you can catch vulnerabilities on every pull request as you build and scan an entire repository to surface accumulated risk by asking “Run a full security scan on this repo“. The Security Agent power includes an Agent hook to evaluate if a code review diff scan should be started after the Kiro agent has completed its turn. Before deploying to production, you can run a penetration test from your CLI to find what most scanners miss. Security Agent closes the loop by validating every finding and generating ready-to-implement code fixes.

You can pull the findings back into your development environment by asking “help me remediate my findings“. The Kiro power for AWS Security Agent will download findings to your local workspace, prioritize the most critical finding, and offer to start a bugfix spec session. You can iterate on fixing the findings using their familiar IDE with their existing tooling, steering, powers, and MCP servers.

You can also run threat models through the Kiro power in the IDE by asking “Build a threat model for this application“. The generated threat model is saved to .security-agent/threat_model.md. 

To learn more, visit the Kiro power for Security Agent.

Now available
AWS Security Agent understands the full security context across your software development lifecycle by covering design-time security (design reviews and threat modeling in preview), development-time security (code review in preview), and deployment-time security (penetration testing in GA), in a single, unified agentic offering. To learn more, visit the AWS Security Agent product page and the technical documentation.

These features are now available in AWS commercial Regions where AWS Security Agent is available. For Regional availability and the future roadmap, visit the AWS Capabilities by Region. For detailed pricing information and to access our 2-month free trial offer, please visit the AWS Security Agent pricing page.

Give it a try in the Security Agent console and send feedback to AWS re:Post for Security Agent or through your usual AWS Support contacts.

— Channy

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Amazon S3 annotations: attach rich, queryable context directly to your objects

Today, we’re announcing a new metadata capability for Amazon Simple Storage Service (Amazon S3) called annotations, enabling you to attach rich, large-scale business context directly to your objects. You can store up to 1,000 named annotations per object, each up to 1 MB in size, totaling up to 1 GB per object, in flexible formats like JSON, XML, YAML, or plain text. You can modify or delete an annotation at any time, without re-writing your objects, making it easy to keep your object context current.

Organizations are building AI agents and autonomous workflows that need to find, understand, and act on data without human intervention. To support these agentic workflows, you need metadata that can evolve alongside the data, scale to petabytes of objects, and remain queryable without expensive retrieval.

With S3 annotations, you can store context such as AI-generated transcripts, content ratings, or technical specifications directly alongside your objects. Your context moves automatically with the object during copy, replication, and cross-region transfers, and S3 removes it when you delete the object. When you enable S3 Metadata, annotations automatically flow into fully managed annotation tables that you can query with Amazon Athena and other analytics engines.

Common use cases
Annotations solve complex metadata challenges across industries:

• Media & Entertainment: Track transcripts, content moderation results, subtitle files, and licensing metadata as separate annotations on video assets, eliminating the need to synchronize metadata across multiple media asset management systems.
• Financial Services: Attach AI-generated investment summaries and sentiment analysis to research documents, enabling autonomous research agents to discover relevant datasets through natural-language queries without maintaining separate metadata databases.
• Life Sciences: Annotate clinical trial data with regulatory status, patient cohort details, and approval chains, making compliance audits faster while keeping full context accessible for archived data in Amazon S3 Glacier storage classes without retrieval charges.

How annotations address metadata challenges
Amazon S3 already supports several ways to describe your objects. System-defined metadata captures properties like size and storage class. Object tags support operational tasks like access control and lifecycle management. User-defined metadata lets you add small amounts of custom information at upload time.

While these capabilities work well for their intended purposes, they have limitations when you need to attach much richer context without building and maintaining separate metadata systems. Annotations address these needs by providing metadata capabilities at a fundamentally different scale and flexibility, offering mutable, queryable context per object compared to 10 immutable tags or 2 KB of headers.

Capability	Max size	Mutable?	Best for
System-defined metadata	Fixed	No	Object properties (size, storage class, creation time)
User-defined metadata	2 KB	No (set at upload)	Small custom key-value pairs
Object tags	10 tags, 128/256 characters per key/value	Yes	Access control, lifecycle rules, cost allocation
Annotations	1 GB (1,000 × 1 MB)	Yes	Rich business context (JSON, XML, YAML, plain text)

Today, metadata describing S3 objects often lives in separate databases or sidecar files, requiring complex synchronization workflows that can exceed data storage costs. When you enable S3 Metadata annotation tables, this context becomes queryable at scale through Amazon Athena. AI agents can discover your data through natural language with the S3 Tables MCP server, which provides a standardized interface for AI models to query your annotations. You can query annotations for objects in any storage class, without restoring the objects or paying retrieval charges.

Getting started with annotations
To start using annotations, make sure your AWS Identity and Access Management (IAM) policy or bucket policy grants permissions for the s3:PutObjectAnnotation and s3:GetObjectAnnotation actions. You can then add annotations to any existing or new S3 object using the PutObjectAnnotation API.

For example, a media company can attach technical specifications and AI-produced summaries to a video asset using the AWS Command Line Interface (AWS CLI):

# Create a JSON file with technical metadata
cat > mediainfo.json << 'EOF'
{"codec":"H.265","resolution":"3840x2160","audio_tracks":8,"frame_rate":29.97}
EOF

# Attach it as an annotation
aws s3api put-object-annotation \
  --bucket my-media-bucket \
  --key videos/documentary-2026.mp4 \
  --annotation-name mediainfo \
  --annotation-payload ./mediainfo.json

# Attach a plain-text AI-generated summary as a separate annotation
echo "A 90-minute nature documentary covering wildlife migration patterns across three continents, featuring aerial footage and underwater sequences. Languages: English, Spanish, Portuguese." > ai_summary.txt

aws s3api put-object-annotation \
  --bucket my-media-bucket \
  --key videos/documentary-2026.mp4 \
  --annotation-name ai_summary \
  --annotation-payload ./ai_summary.txt

These commands attach two separate annotations to the same video object. The mediainfo annotation stores structured technical specifications as JSON, while the ai_summary annotation stores a text description. Each annotation is identified by a unique name, and you can read and modify each one independently. With unique names for each annotation, you can use different annotations to support multiple concurrent enrichment workflows, for example, one team adding technical metadata while another team adds content classifications, without interfering with each other.

Retrieve a specific annotation using the GetObjectAnnotation API:

aws s3api get-object-annotation \
  --bucket my-media-bucket \
  --key videos/documentary-2026.mp4 \
  --annotation-name mediainfo \
  ./mediainfo-output.json

To see all annotations attached to an object, use the ListObjectAnnotations API:

aws s3api list-object-annotations \
  --bucket my-media-bucket \
  --key videos/documentary-2026.mp4

When you no longer need a specific annotation, remove it using the DeleteObjectAnnotation API:

aws s3api delete-object-annotation \
  --bucket my-media-bucket \
  --key videos/documentary-2026.mp4 \
  --annotation-name mediainfo

You can update an existing annotation at any time by calling PutObjectAnnotation again with the same annotation name. For large objects uploaded using multipart upload, attach annotations after completing the multipart upload using the PutObjectAnnotation API.

Querying annotations at scale with S3 Metadata tables
Attaching annotations to individual objects is useful, but the real power comes when you query across all your annotations at scale. When you enable S3 Metadata annotation tables on your bucket, S3 automatically indexes your annotations into a fully managed Apache Iceberg table, called an annotation table. You can query annotation tables with Amazon Athena or any Iceberg-compatible engine.

To enable annotation tables, use the S3 console or the CreateBucketMetadataConfiguration API. The following example creates a new metadata configuration with annotation tables enabled while keeping journal tables for change tracking and disabling the live inventory table:

{
  "JournalTableConfiguration": {
    "RecordExpiration": { "Expiration": "DISABLED" }
  },
  "InventoryTableConfiguration": { "ConfigurationState": "DISABLED" },
  "AnnotationTableConfiguration": {
    "ConfigurationState": "ENABLED",
    "Role": "arn:aws:iam::123456789012:role/S3MetadataAnnotationRole"
  }
}

This configuration tells S3 to automatically capture all your annotations in a queryable table. Once applied, any annotation you attach to objects in this bucket will appear in the table within approximately one hour.

If the bucket already has a metadata configuration, use the UpdateBucketMetadataAnnotationTableConfiguration API:

aws s3api update-bucket-metadata-annotation-table-configuration \
  --bucket my-media-bucket \
  --annotation-table-configuration '{"ConfigurationState":"ENABLED","Role":"arn:aws:iam::123456789012:role/S3MetadataAnnotationRole"}'

Once enabled, your annotations automatically flow into the annotation table. Journal tables update in near real time, while annotation tables refresh within an hour. Unlike traditional metadata tables that require predefined schemas, annotation tables automatically adapt to any JSON, XML, or YAML structure you write. Each annotation becomes a row in the table with its content stored in a text_value column, letting you query across all annotations without schema migrations.

If you enable annotation tables on a bucket that already has annotated objects, S3 automatically backfills existing annotations into the table. The backfill process runs in the background and can take several hours to days depending on the number of objects.

For example, to find all video assets with more than 8 audio tracks across your entire bucket using Amazon Athena:

SELECT DISTINCT bucket, object_key
FROM "s3tablescatalog/aws-s3"."b_my_media_bucket"."annotation"
WHERE name = 'mediainfo'
AND CAST(json_extract_scalar(text_value, '$.audio_tracks') AS INTEGER) > 8

This query scans the annotation table for all annotations named mediainfo, extracts the audio_tracks field from the JSON content, and returns objects where the count exceeds 8.

Or to find all objects that received new annotations in the last 24 hours through the journal table:

SELECT bucket, key, version_id, record_timestamp, annotation.name
FROM "s3tablescatalog/aws-s3"."b_my_media_bucket"."journal"
WHERE record_timestamp >= (current_date - interval '1' day)
AND annotation.name IS NOT NULL
AND record_type IN ('CREATE_ANNOTATION', 'DELETE_ANNOTATION')

This query uses the journal table to track annotation changes in near real time, which is ideal for building event-driven workflows that respond to new or deleted annotations.

You can also use natural language to search objects by their annotations using agents in Amazon SageMaker Unified Studio or any IDE with the S3 Tables MCP server. For example, asking “find all PG-rated movies with Spanish subtitles from 2023” returns results in seconds instead of the hours it would take querying multiple disconnected systems.

Get started today
You can start using Amazon S3 annotations today in all AWS Regions, including the AWS China Regions. Annotation tables are available in all AWS Regions where S3 Metadata is available.

Whether you’re building AI agents that need to discover data autonomously, managing petabytes of media assets with complex metadata, or tracking compliance context for archived datasets, annotations give you the scale and flexibility to attach rich metadata directly to your objects without managing separate systems.

Annotation storage is always billed at S3 Standard rates, even if the parent object is in S3 Glacier or another storage class. For full pricing details, visit the Amazon S3 pricing page.

To learn more and get started, visit the Amazon S3 Metadata overview page and the Amazon S3 documentation. Send feedback to AWS re:Post for S3 or through your usual AWS Support contacts.

Daniel Abib

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AWS WAF adds AI traffic monetization capability to help content owners charge AI bots for content access

AWS WAF now includes AI traffic monetization capability that gives digital content owners and publishers a way to charge AI bots and agents for access to protected web content directly at the network edge. The capability helps content owners and publishers set per-request pricing by content path, bot category, or verification tier without modifying their origin infrastructure or writing application code. Content owners can define granular access policies per agent type, collect payments in stablecoins to their preferred wallet, and monitor revenue and bot activity from a single dashboard.

AI bot traffic now accounts for more than 50% of web traffic for many content providers, with AI-specific crawlers growing more than 300% year-over-year. Unlike traditional search engine crawlers, which index content and return measurable referral traffic back to publisher websites, AI bots consume the same content to generate summaries and responses in AI interfaces, with little to no traffic sent back to the original source. Publishers bear the infrastructure costs of serving that traffic without the page views, ad impressions, or subscription conversions that typically offset those costs. AWS WAF Bot Control already gives customers visibility into bot activity and the ability to block or rate-limit traffic, but setting pricing and collecting payment from AI agents has not been possible until now. AI traffic monetization is a new Bot Control capability that closes that gap, giving content owners and publishers a way to configure pricing rules directly through the AWS WAF console and collect payments from AI agents through third-party payment integrations, without building custom payment infrastructure or negotiating individual licensing agreements. Payment settlement and verification flows are provided by Coinbase’s x402 Facilitator. Integration with Stripe for direct account payments and Machine Payments Protocol (MPP) support is coming soon.

Getting Started with AI Traffic Monetization
Before configuring monetization, confirm that AWS WAF Bot Control is enabled at Common or Targeted level on the web ACL associated with your CloudFront distribution. Bot Control provides the agent classification that monetization rules depend on. If you have not set this up yet, visit Adding the AWS WAF Bot Control managed rule group to your web ACL documentation. In the AWS Management Console, go to WAF & Shield and choose Protection packs (web ACLs) in the left navigation pane to get started.

A protection pack is the core configuration unit for AI traffic monetization. It defines which content paths are monetized, what each agent verification tier is charged, which payment methods you accept, and what license terms apply. To create one, choose Create protection pack (web ACL).

In Tell us about your app, select one or more app categories that describe your content (for example, Content & publishing systems, E-commerce & transaction platforms, or Enterprise & business applications), and choose an App focus. AWS WAF uses these selections to recommend suitable security protections for your configuration.

In Select resources to protect, choose Add resources to associate regional or global resources such as CloudFront distributions with this protection pack. You can skip this step and add resources later.

In Choose initial protections, select from AWS WAF managed rule packages based on your app category and resource selections. You can also choose individual rules instead of packages.

In Name and describe, provide a name and optional description for the protection pack.

Optionally, expand Customize protection pack (web ACL) to configure additional settings including pricing tiers, payment methods, content scope, and license terms.

When finished, choose Create protection pack (web ACL).

Once your protection pack is in place, review the AI traffic analysis dashboard to understand the impact of AI bot traffic on your content before setting your pricing strategy. In the WAF & Shield console, go to AI traffic analysis in the left navigation pane. Select your protection pack (web ACL) from the dropdown to populate the dashboard.

The AI traffic analysis dashboard breaks down traffic into four categories visible in the bot traffic overview panel: All bot requests, AI bot requests, Verified AI bot traffic, and Unverified AI bot traffic. The dashboard surfaces infrastructure impact metrics including bandwidth consumed, estimated monthly cost, and peak request rates. A per-path heatmap shows which content paths receive the most AI bot activity by hour, giving you the data you need to make informed pricing decisions.

AWS WAF Bot Control classifies over 650 distinct AI bot and agent types including GPTBot, Claude-Web, and Perplexity-Bot, and assigns each a verification tier:

• Verified — Agent identity confirmed through Web Bot Auth (WBA) Ed25519 cryptographic signature, or sourced from a documented IP range with a known set of user-agents and domain names.
• Unverified — Agent recognized through user-agent matching, behavioral fingerprinting, and IP reputation, but identity not cryptographically confirmed.

Once you have reviewed your traffic patterns, return to Protection packs (web ACLs), select your protection pack from the list, and choose Configure AI monetization from the right panel to set pricing and access policies. Each protection pack defines the pricing, agent policies, accepted payment methods, and license terms that apply to a defined set of content paths. You can create multiple protection packs and apply different pricing to different content zones within the same distribution. Once created, associate the protection pack with your web ACL by opening the web ACL and choosing Add protection pack.

For each agent verification tier within the pack, you can assign one of six actions: Monetize (return a 402 with pricing), Allow (grant free access), Block (deny access entirely), Count (log without charging), CAPTCHA (present a puzzle to verify a human sender), or Challenge (run a silent check to verify the client is a browser, not a bot).

In the Edit monetization configuration page, configure the following:

Under Payment settlement, select one or more blockchain networks for stablecoin payments. Any wallet address on the supported networks is accepted, whether self-managed or hosted by a wallet provider such as Coinbase. For each network, provide your wallet address and set a Base price per page in USDC. You can add multiple networks using Add network. AWS does not process payments or take a fee on content revenue; disbursement is self-managed or managed by your wallet provider.

When a Monetize rule matches an incoming request, AWS WAF returns an HTTP 402 Payment Required response. The response body contains a machine-readable price manifest in JSON format using the x402 open protocol for machine-to-machine payments. The manifest includes the content price in USDC, accepted blockchain networks such as Base and Solana, the destination wallet address, the maximum payment timeout, and the payment scheme.

Any x402-compatible agent runtime can complete this flow autonomously. The client submits a signed payment authorization on their payment network of choice. AWS WAF verifies it, fetches the content, integrates with third-party facilitator services for settling the payment on-chain, and serves the response.

Note that the Monetize action is supported exclusively for web ACLs associated with Amazon CloudFront distributions. Adding a Monetize rule to a regional web ACL is not supported.

Since the Currency mode toggle is available directly in the monetization configuration page, you can switch between Real and Test mode at any time. Before going live, use test mode on non-production traffic to validate pricing, wallet configuration, and x402 payment flows. Note that test mode still enforces x402 payments, but those payments can be made on testnets such as Base Sepolia or Solana Devnet using test funds obtained from faucets such as faucet.circle.com. To activate test mode, toggle Currency mode to Test in your protection pack configuration. AWS WAF returns real price manifests and runs the full payment flow identically to production on the configured test chain. All events are logged with CurrencyMode: TEST. When satisfied with the configuration, toggle Currency mode back to Real to begin processing real payments.

Once you have switched Currency mode to Real, navigate to AI access monetization in the left navigation pane to track monetization outcomes in real time. Note that the AI access monetization dashboard only reflects activity from real currency mode and does not display test transactions.

The Revenue dashboard shows Total revenue, revenue broken down by Verified bots and Unverified bots, and Avg. per request. The Top revenue sources panel groups earnings by bot category, and the AI access patterns panel ranks content paths by revenue generated. Use the Settlements tab to reconcile payments by provider and review payment method distribution and failed payment attempts.

Now Available
AI traffic monetization is available now for Amazon CloudFront customers at no additional charge beyond standard AWS WAF pricing. The capability is available in all edge locations where AWS WAF web ACLs are associated with Amazon CloudFront distributions.

To learn more about AI traffic monetization, see the AWS WAF Developer Guide.

— Esra

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AWS Weekly Roundup: AWS FinOps Agent in preview, Gemma 4 on Bedrock, Kiro Pro Max, and more (June 15, 2026)

This week, New York City is hosting AWS Summit, bringing together builders, customers, and AWS teams for a full day of announcements, demos, and technical sessions at the Javits Center. I wrote blog posts for some of the Summit launches, so I am excited to see them go live this week. I just won’t be watching from the Javits Center. I’ll be at a four-day music festival, following the launches on my phone while trying to figure out how to put up a tent. If you weren’t able to attend in person like me, the keynote livestream is available on June 17, with Dr. Swami Sivasubramanian, VP of Agentic AI, and Chet Kapoor, VP of Security Services and Observability, covering new capabilities across developer tools, AI infrastructure, and security.

Here’s what happened this week.

Headlines
How frontier teams are reinventing AI-native development — Swami published a detailed post this week drawing on data from experiments across hundreds of Amazon engineering teams. The findings are worth reading carefully if you are thinking about how to structure AI adoption on your own team.

A six-engineer team rebuilt the Amazon Bedrock inference engine in 76 days, a project originally scoped for 30 developers over 12 to 18 months. The median productivity gain across structured pilots with Amazon Stores teams was 4.5x in normalized deployment velocity, with some teams exceeding 10x. Perfect Order Experience went from a two-week feature cycle to shipping in an afternoon. WW Grocery cut design document creation from five days to a few hours.

The post distills these results into five practices for becoming a frontier team. First, invest in agent context: build steering files, coding standards, and structured repositories before writing production code. Second, expect an initial slowdown while workflows are restructured, and push through it. Third, maintain a steady backlog of well-scoped tasks so agents can run in parallel without constant supervision. Fourth, make intent explicit through structured specifications before code generation begins. Fifth, shift testing left so agents can self-correct before code reaches the pipeline.

The post closes with a note that commit velocity is only part of the picture, and that a follow-up will cover release management, operations, security operations, and EOL upgrades.

AWS FinOps Agent is now available in preview — AWS FinOps Agent is a new agent for FinOps practitioners and engineering teams that answers cost questions, surfaces optimization opportunities, investigates cost anomalies, and runs recurring FinOps workflows on a defined schedule. You can use it to query your AWS costs, generate cost reports for finance and engineering teams, and surface rightsizing, idle resource, and Savings Plans recommendations from AWS Cost Optimization Hub and AWS Compute Optimizer. The agent can open Jira tickets on your behalf based on those recommendations. When a cost anomaly is detected, FinOps Agent can automatically investigate the root cause and post findings to a Slack channel.

Last week’s launches
I’ll start with one I wrote this week, then cover the other launches that caught my attention:

• Amazon EC2 M9g and M9gd instances are now generally available — Powered by AWS Graviton5 processors and built on the sixth-generation AWS Nitro System, M9g instances deliver up to 25% better compute performance compared to Graviton4-based instances, with up to 35% faster performance for web applications, up to 35% for machine learning inference, and up to 30% for databases. Graviton5 is the first processor in the AWS fleet to support PCIe Gen6 and DDR5-8800 memory, and includes a 5x larger L3 cache compared to the previous generation. M9g and M9gd instances offer up to 15% higher network bandwidth and 20% higher Amazon EBS bandwidth on average across sizes compared to M8g. This release also introduces the Nitro Isolation Engine, an enhancement to the Nitro System that uses formal verification to provide mathematically proven isolation between virtual machines — establishing Nitro as the first formally verified cloud hypervisor. M9gd instances add up to 11.4 TB of NVMe SSD local storage with 30% higher IOPS compared to M8gd. Both instance types support Instance Bandwidth Configuration (IBC) for adjusting bandwidth allocation between EBS and VPC networking by up to 25%.
• Anthropic Claude Fable 5 on Amazon Bedrock — Claude Fable 5 launched on Amazon Bedrock on June 9, bringing extended asynchronous task execution, advanced vision capabilities across diagrams, charts, and PDFs, and proactive self-verification. Access requires opting into data sharing via the Data Retention API before invoking the model; Anthropic requires 30-day retention of inputs and outputs for Mythos-class models. Important note on availability: On June 12, Anthropic asked AWS to revoke access to Claude Fable 5 and Claude Mythos 5 for all users to support compliance with a US Government export control directive. All other models, including Opus 4.8, are unaffected. Read the Anthropic statement for details. AWS will share further updates as they become available.
• Gemma 4 models are now available on Amazon Bedrock — The Gemma 4 family from Google DeepMind is now available on Amazon Bedrock across three variants: Gemma 4 31B (dense, 256K-token context window, suited for reasoning and coding workloads), Gemma 4 26B-A4B (mixture-of-experts architecture, targeting cost- and latency-sensitive workloads), and Gemma 4 E2B (smallest variant, designed for low-latency interactive use cases). All three support native function calling, structured output, reasoning, response streaming, multimodal input across text, image, video, and audio, and more than 35 languages.
• Amazon OpenSearch Service launches MCP Apps for agentic observability — Amazon OpenSearch Service now supports MCP Apps, enabling observability workflows inside compatible agentic IDEs including Claude Desktop and VS Code. An AI agent in your local environment can investigate incidents using logs, traces, metrics, and alerts stored in OpenSearch domains, collections, and Amazon Managed Service for Prometheus. Each MCP App tool call returns a dual response: a text summary for the agent to reason over and an interactive visualization rendered in the same conversation thread. Available MCP App tools cover log, metrics, and trace investigation; service performance; topology; dynamic visualizations; agent health; cluster health; and instrumentation scoring.

Other AWS news
Here are some additional posts and updates you may find useful:

• AWS CLI v1 enters maintenance mode — When CLI v1 enters maintenance mode, the botocore and s3transfer dependencies will be vendored directly into the CLI v1 codebase rather than installed as separate packages. This means upgrading CLI v1 will no longer update the standalone botocore or s3transfer packages, and installing those packages independently will have no effect on the versions used by CLI v1. Environments with both CLI v1 and boto3 installed will contain separate copies of these libraries. New CLI v1 releases will be limited to critical bug fixes and security issues. The recommended path is to migrate to AWS CLI v2.
• AWS Workload Credentials Provider is now available — AWS has launched a new Workload Credentials Provider that enables workloads to obtain short-term AWS credentials without requiring long-term access keys. This supports credential management for applications running outside of AWS, giving teams a way to follow least-privilege access patterns for workloads in third-party or on-premises environments.
• Kiro Pro Max is now available — Kiro has introduced a new Pro Max tier, adding higher usage limits, access to the latest frontier models, and additional agentic capabilities for development teams. Kiro Pro Max is designed for professional developers who need sustained, high-volume use across coding, specification generation, and agent-driven tasks.

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

• AWS Summits — AWS Summits are free in-person events covering cloud and AI. Coming up: New York City (June 17), Hong Kong (June 17), Shanghai (June 23-24), Japan (June 25), Washington, D.C. (June 30 – July 1), Taipei (July 15), and Bogotá (July 30).
• AWS Community Days — Community-led conferences planned and delivered by community leaders. Upcoming events include Montreal, Canada (June 20), Indianapolis, USA (June 24), Hangzhou, China (June 28), Bengaluru, India (July 11), and Yaoundé, Cameroon (July 25).

Visit the AWS Builder Center to meet other builders, contribute solutions, and find resources that help you keep building. You can also browse upcoming AWS-led in-person and virtual events, plus developer-focused sessions.

— Esra

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

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Update: June updates to listing attribute usage and enumeration values

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Now available: Amazon EC2 M9g and M9gd instances powered by new AWS Graviton5 processors

AWS Graviton processors have improved steadily across generations, with each iteration delivering advances in compute performance, price-performance, and energy efficiency. At re:Invent 2025, we announced Amazon EC2 M9g, the first Graviton5-powered instances, in preview. Since then, customers have tested M9g across a wide range of workloads and shared their results. ClickHouse saw a 36% performance boost compared to M8g, with zero code changes. Honeycomb achieved 36% better throughput per core compared to Graviton4, across a 6-month A/B test of production observability workloads. HubSpot deployed M9g for MySQL databases and saw query duration drop by up to 60%. Today, M9g instances are generally available, alongside the new M9gd instances for customers who need high-speed, low-latency local NVMe SSD storage. Both are powered by Graviton5, the most powerful and most energy efficient processor AWS has ever built.

While many Arm-based instances have been introduced across the industry, no one comes close to the breadth and depth of the AWS Graviton footprint. After five generations of custom silicon and eight years of continuous investment, Graviton powers over 350 instance types serving more than 120,000 customers, from startups to large enterprises, a robust ISV partner ecosystem, and a broad set of managed services. You can use Graviton for a broad variety of workloads, including web applications, microservices, analytics, databases, machine learning (ML) inference, electronic design automation (EDA), gaming, and video encoding. As workloads grow more compute-intensive and data-driven, many have asked for more processing power, along with greater network and storage bandwidth to move more data and complete workloads faster. We’ve also designed these instances to efficiently package compute, memory, and I/O to maximize energy utilization.

As AI shifts from answering questions to taking actions, running code, using tools, evaluating results, and orchestrating multi-step tasks, the demand for CPU compute is growing rapidly. Graviton5 is built for this shift. With 192 cores, a 5x larger L3 cache, up to 33% lower inter-core latency, and DDR5 memory delivering high bandwidth, Graviton5 helps agents spend less time waiting on CPU-bound steps, processing more instructions, handling large numbers of concurrent environments, and keeping accelerators moving.

Meta is deploying Graviton at scale starting with tens of millions of cores to support its agentic AI efforts, making Meta one of the largest Graviton customers in the world. Agentic AI workloads, including real-time reasoning, code generation, and the orchestration of multi-step tasks, are CPU-intensive and benefit from the higher compute performance, larger caches, higher memory bandwidth, and core density in Graviton5.

What’s new in M9g and M9gd
Built on the sixth-generation AWS Nitro System, M9g instances are powered by AWS Graviton5 processors that deliver higher compute performance, larger caches, and improved memory and I/O scalability compared to Graviton4 processors. Graviton5 offers up to 25% better compute performance compared to Graviton4-based instances, with up to 35% faster performance for web applications, up to 35% for machine learning inference, and up to 30% for databases. As the first CPU in the AWS fleet to support the latest generation of PCIe Gen6 and DDR5-8800 memory, AWS Graviton5 instances deliver the fastest memory of any processor instances in the cloud, and 5 times more L3 cache compared to the previous generation. These improvements also come with better energy efficiency, helping you meet sustainability targets without compromising capability.

Networking and storage bandwidth have been expanded to keep pace with compute growth. M9g and M9gd instances offer up to 15% higher network bandwidth and 20% higher Amazon Elastic Block Store (Amazon EBS) bandwidth on average across sizes, with up to twice the network bandwidth for the largest instance size. M9g and M9gd instances also support Instance Bandwidth Configuration (IBC), a feature that helps you adjust the allocation of bandwidth between Amazon EBS and Amazon Virtual Private Cloud (Amazon VPC) networking for an Amazon EC2 instance by up to 25%. IBC can help optimize performance for workloads with specific bandwidth requirements, such as database read and write performance, query processing, and logging. These enhancements support faster data movement and improved throughput for workloads that rely on high I/O performance.

Security and isolation are foundational requirements for running workloads in the cloud. Within the Nitro System, the AWS Nitro Hypervisor is designed to isolate instances from each other as well as AWS operators. With M9g and M9gd instances we are raising the bar on security even further with the introduction of Nitro Isolation Engine. Nitro Isolation Engine is an enhancement to the Nitro System, 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.

M9g instances provide one vCPU for every four GiB of memory and are well suited for a broad range of general-purpose workloads, including application servers, microservices, midsize data stores, gaming servers, caching fleets, containerized applications, large-scale Java applications, code repositories, web applications, and agentic AI.

For workloads that need high-speed, low-latency local storage, M9gd instances provide up to 11.4 TB of NVMe SSD storage and 30% higher IOPS and storage performance compared to Graviton4-based M8gd instances. M9gd instances are well suited for general-purpose workloads that require a balance of compute and memory with high-speed, low-latency local storage, including application servers, microservices, gaming servers, midsize key-value data stores, caching fleets, data logging, media processing, batch and log processing, and applications that need temporary storage such as caches and scratch files.

Here are the key specifications across the family:

M9g	vCPUs	Memory (GiB)	Network bandwidth (Gbps)	EBS bandwidth (Gbps)
medium	1	4	Up to 15	Up to 12
large	2	8	Up to 15	Up to 12
xlarge	4	16	Up to 15	Up to 12
2xlarge	8	32	Up to 17	Up to 12
4xlarge	16	64	Up to 17	Up to 12
8xlarge	32	128	17	12
12xlarge	48	192	25	18
16xlarge	64	256	34	24
24xlarge	96	384	50	36
48xlarge	192	768	100	72
metal-48xl	192	768	100	72

M9gd instances include local NVMe SSD storage. The table below shows the instance storage for each size. Compute, memory, network, and EBS bandwidth specifications are the same as M9g.

M9gd	vCPUs	Memory (GiB)	Instance storage (GB)	Network bandwidth (Gbps)	EBS bandwidth (Gbps)
medium	1	4	1 x 59 NVMe SSD	Up to 15	Up to 12
large	2	8	1 x 118 NVMe SSD	Up to 15	Up to 12
xlarge	4	16	1 x 237 NVMe SSD	Up to 15	Up to 12
2xlarge	8	32	1 x 475 NVMe SSD	Up to 17	Up to 12
4xlarge	16	64	1 x 950 NVMe SSD	Up to 17	Up to 12
8xlarge	32	128	1 x 1900 NVMe SSD	17	12
12xlarge	48	192	3 x 950 NVMe SSD	25	18
16xlarge	64	256	1 x 3800 NVMe SSD	34	24
24xlarge	96	384	3 x 1900 NVMe SSD	50	36
48xlarge	192	768	3 x 3800 NVMe SSD	100	72
metal-48xl	192	768	3 x 3800 NVMe SSD	100	72

Now available
M9g and M9gd instances are available in the US East (N. Virginia), US East (Ohio), US West (Oregon), and Europe (Frankfurt) Regions. M9g and M9gd instances are available for purchase through Savings Plans, On-Demand, Spot Instances, Dedicated Instances, or Dedicated Hosts. For more information, visit Amazon EC2 pricing.

To get started with M9g and M9gd instances, several resources are available. The AWS Graviton Getting Started Guide is a technical guide covering how to build, run, and optimize workloads on Graviton-based instances. The Graviton Savings Dashboard helps you track and measure the cost savings from running workloads on Graviton-based instances. And AWS Transform is an AI-powered service that automates code transformations for migrating Java applications from x86 to Graviton-based Amazon EC2 instances, handling compatibility analysis, automated recompilation, dependency updates, and validation.

To learn more about Graviton-based instances, visit AWS Graviton Processors or Level up your compute with AWS Graviton.

— Esra

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Anthropic Claude Fable 5 on AWS: Mythos-class capabilities with built-in safeguards now available

Today, we’re announcing the availability of Claude Fable 5 on Amazon Bedrock and Claude Platform on AWS. Claude Fable 5 makes Mythos-level capabilities available to all customers, with strong safeguards designed to make it safe for broader use. Fable 5 is state-of-the-art on nearly all tested benchmarks and delivers exceptional performance in software engineering, knowledge work tasks, and vision – built for ambitious, long running work.

With Claude Fable 5 on Bedrock, you can build within your existing AWS environment and scale inference workloads. You can also use Claude Fable 5 through the Claude Platform on AWS, giving you Anthropic’s native platform experience.

According to Anthropic, Claude Fable 5 represents a step-change in what you can accomplish with AI models. Here is what makes this model different:

• Long-running, asynchronous execution — Claude Fable 5 handles complex tasks that previous models could not sustain, executing coding and knowledge work tasks for extended periods without intervention.
• Advanced vision capabilities — Claude Fable 5 understands diagrams, charts, and tables nested in files and PDFs. This opens up research and document-heavy work in finance, legal, analytics, architecture, and gaming. In coding, the model implements designs with high fidelity and uses vision to critique its output against goals.
• Proactive self-verification — The model self-updates skills based on learnings, develops its own harnesses and evaluations.

Claude Fable 5 includes safeguards that limit its performance in specific areas where misuse risk is elevated. Harmful prompts related to cybersecurity, biology, chemistry, and health fall back to receive a response from Opus 4.8 instead. Anthropic is able to expand access to nearly all of Claude Fable 5’s state-of-the-art capabilities by developing more powerful safeguards. The same model without these limits is Claude Mythos 5 and it will only be available to a small group of vetted customers.

Claude Fable 5 model in action
You can use Claude Fable 5 in both Amazon Bedrock and Claude Platform on AWS. This post will cover guidance on how to access and use on Amazon Bedrock. For guidance on the Claude Platform on AWS, visit the documentation to learn more.

To get started with Amazon Bedrock, you can only access the model programmatically now using the Anthropic Messages API to call the bedrock-runtime or bedrock-mantle endpoints through Anthropic SDK. You can sole keep using the Invoke and Converse API on bedrock-runtime through the AWS Command Line Interface (AWS CLI) and AWS SDK. The console support is coming soon.

In order to access Claude Fable 5 model, you must opt into data sharing by using the Data Retention API and setting provider_data_sharing before you can invoke the models. There is no console user interface for this setting at launch.

curl -X PUT https://bedrock-mantle.us-east-1.api.aws/v1/data_retention \
  -H "x-api-key: <your-bedrock-api-key>" \ 
  -H "Content-Type: application/json" \
  -d '{ "mode": "provider_data_share" }'

This mode allows Amazon Bedrock to retain and share your inference data with model providers per their requirements. Anthropic requires 30-day inputs and outputs retention, as well as human review. To learn more, visit the Amazon Bedrock abuse detection.

Let’s start with Anthropic SDK for Python using the Messages API on bedrock-mantle endpoint. Install Anthropic SDK.

pip install anthropic

Here is a sample Python code to call Claude Fable 5 model:

import anthropic

client = anthropic.Anthropic(
    base_url="https://bedrock-mantle.us-east-1.api.aws/anthropic",
    api_key= <your-bedrock-api-key>
)

message = client.messages.create( 
     model="anthropic.claude-fable-5", 
	 max_tokens=4096, 
	 messages=[ 
	     { "role": "user", 
		   "content": "Design a distributed architecture on AWS in Python that should support 100k requests per second across multiple geographic regions", 
		 }, 
	 ], 
)

print(message.content[0].text)

To learn more, check out Anthropic Messages API code examples and notebook examples for multiple use cases and a variety of programming languages.

You can also use Claude Fable 5 with the Invoke API and Converse API on bedrock-runtime endpoint. Here’s a example to call Converse API for a unified multi-model experience using the AWS SDK for Python (Boto3):

import boto3 
bedrock_runtime = boto3.client("bedrock-runtime", region_name="us-east-1") 
response = bedrock_runtime.converse( 
    modelId="us.anthropic.claude-fable-5", 
    messages=[ 
        { 
            "role": "user", 
            "content": [ 
                { 
                    "text": "Design a distributed architecture on AWS in Python that should support 100k requests per second across multiple geographic regions." 
                } 
            ] 
        } 
    ], 
    inferenceConfig={ 
        "maxTokens": 4096 
    } 
) 
print(response["output"]["message"]["content"][0]["text"]) 

To learn more, visit code examples that show how to use Amazon Bedrock Runtime with AWS SDKs.

Things to know
Let me share some important technical details that I think you’ll find useful.

• Model access — Claude Fable 5 access is gradually expanding for all AWS accounts. If your account doesn’t have access yet, it will be enabled soon depending on your Bedrock usage. If you want to get access to this model quickly, contact your usual AWS Support.
• Pricing — When a harmful prompt is routed to Opus 4.8 instead of Fable 5, you pay only Opus prices. If a request is blocked mid-conversation, initial tokens are charged at Fable rates and subsequent tokens at Opus rates. To learn more, visit the Amazon Bedrock pricing page.
• Data retention — For Fable 5, Mythos 5, and future models on Bedrock with similar or higher capability levels, Anthropic will require 30-day retention for all traffic on Mythos-class models. Retaining data for a limited period allows Anthropic to detect patterns of misuse that are not visible from a single exchange. Once you opt into data retention, your data will leave AWS’s data and security boundary.
• Claude Mythos 5 on Bedrock (Limited Preview) – You can also use Anthropic’s most capable model for cybersecurity and life sciences, including vulnerability discovery, drug design, and biodefense screening. Access is currently limited due to the dual-use nature of these domains. To learn more, visit the model card documentation.

Now available
Anthropic’s Claude Fable 5 model is available today on Amazon Bedrock in the US East (N. Virginia) and Europe (Stockholm) Regions; check the full list of Regions for future updates. Claude Fable 5 is also available on the Claude Platform on AWS in North America, South America, Europe, and Asia Pacific.

Give Claude Fable 5 a try with the Amazon Bedrock APIs, in the Claude Platform on AWS, and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

— Channy

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AWS Weekly Roundup: BYOM for Amazon RDS for SQL Server, AWS IoT Device SDK for Swift, and more (June 8, 2026)

This week, the AWS IoT Device SDK for Swift reached general availability. As a member of the Swift Server Workgroup (SSWG), this one caught my attention. The SDK brings production-ready MQTT 5 connectivity, Device Shadow, Jobs, and fleet provisioning to Swift developers on macOS, iOS, tvOS, and Linux.

I’m curious to see what you will build with it. Swift on the server has matured over the past few years, and now it reaches IoT devices too. This connects to a broader trend of running Swift at the edge. WendyOS, for example, is an open-source operating system for physical AI that offers first-class Swift support for deploying apps to NVIDIA Jetson and Raspberry Pi hardware. Between server-side Swift, IoT, and edge computing, the language is showing up in places that would have surprised most people a few years ago.

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

Headlines
Amazon RDS for SQL Server supports Bring Your Own Media — Customers who migrate SQL Server applications from on-premises environments can now reuse their existing Microsoft SQL Server licenses, including Software Assurance, through Microsoft’s License Mobility program on Amazon RDS. BYOM is integrated with AWS License Manager for tracking license usage and compliance. Read more.

Amazon Cognito now supports multi-Region replication — You can now synchronize user and machine identity data, including credentials, user pool configurations, and federation setups, to a secondary user pool in a standby Region in near real-time. In the event of a disruption in the primary Region, signed-in users continue accessing their applications without re-authenticating, and registered users can sign in with their existing credentials. Multi-Region replication is available as an add-on for user pools in Essentials or Plus feature tiers across 16 Regions. Read more.

GPT-5.5, GPT-5.4, and Codex from OpenAI are now generally available on Amazon Bedrock — You can now use GPT-5.5 and GPT-5.4 in production workloads on Amazon Bedrock and build with Codex for AI-powered software development, with the same security, governance, and operational controls you already use across AWS. GPT-5.5 is the most capable model from OpenAI, excelling at agentic coding, data analysis, and multi-step autonomous tasks. Codex is available through the Codex App, the Codex CLI, and IDE integrations with Visual Studio Code, JetBrains, and Xcode. Pricing matches OpenAI first-party rates, and usage counts toward existing AWS commitments. Read more.

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

• Amazon Bedrock adds CloudWatch metrics for OpenAI- and Anthropic-compatible APIs — You can now monitor inference traffic to the bedrock-mantle endpoint with CloudWatch metrics, including inference counts, input and output token totals, and client error counts at account, project, model, and project-and-model granularity.
• Amazon Bedrock launches a redesigned console optimized for OpenAI- and Anthropic-compatible APIs — A refreshed console workflow with a model catalog, side-by-side comparison, project-based organization, and project-aware documentation with pre-filled code snippets.
• Amazon Bedrock AgentCore Identity now supports bring-your-own secrets with AWS Secrets Manager — Customers can now reference existing AWS Secrets Manager secret ARNs in AgentCore Identity Credential Providers, enabling full ownership of secrets governance including custom CMKs, tagging strategies, and automatic rotation.
• AWS Step Functions adds AgentCore-powered agentic reasoning step — You can now add AI agent reasoning steps to your Step Functions workflows through an integration with the managed harness in Amazon Bedrock AgentCore. Run multiple agents in parallel or sequence, add human approval, and trace every agent decision.
• Amazon EKS and Amazon EKS Distro now support Kubernetes version 1.36 — Kubernetes 1.36 promotes User Namespaces to GA, introduces Mutating Admission Policies, In-Place Pod-Level Resources Vertical Scaling, and Resource Health Status reporting. Available in all Regions where EKS is available.
• Amazon ECS Managed Instances now supports AWS Trainium and AWS Inferentia — You can now create an ECS Managed Instances capacity provider with Inferentia2, Trainium1, and Trainium2 instance types and have Amazon ECS automatically allocate all accelerator resources to your workload.
• Amazon Quick now supports VPC connectivity for MCP connections — Enterprise customers can now connect privately hosted Model Context Protocol (MCP) servers to Amazon Quick through VPC, enabling secure access to proprietary applications and internal tools without exposing them to the internet.
• AWS Cost and Usage Report 2.0 now supports Athena and Redshift integration — CUR 2.0 exports are automatically delivered in the optimal format for your chosen query engine, with supporting infrastructure templates, table definitions, and data loading instructions.
• Amazon Location Service announces public transit and intermodal routing — The Routes API now supports two new travel modes, Transit and Intermodal, to plan journeys combining public transportation with walking, driving, taxi, and rental segments across 13 Regions.

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

Upcoming AWS events
Learn more about AWS, browse and join upcoming AWS-led in-person and virtual events, startup events, and developer-focused events as well as AWS Summits and AWS Community Days. Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development.

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

— seb

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Try the new console experience in Amazon Bedrock, optimized for Anthropic- and OpenAI-compatible APIs

Today, we’re announcing a new console experience in Amazon Bedrock for you to experiment, iterate, and scale with the latest AI models on Amazon Bedrock’s next-generation inference engine built for high performance, reliability, and security. This console has a refreshed workflow optimized for bedrock-mantle endpoint, which supports the latest GPT, Claude, and open-weight models with the OpenAI Responses API, OpenAI Chat Completions API, and the Anthropic Messages API.

The new console experience makes it simple to find the right model and move quickly from evaluation to production.

• New model card – You can browse the full model catalog, compare them side by side on capabilities, modality support, context window, and applicable service quotas in a single view, removing the need to stitch together documentation, and limit calculators.
• Project-based work – You can make a project to run evaluations and review usage insights in one streamlined workflow that mirrors the lifecycle of building a generative AI application.
• Live documentation – You can use project-aware live documentation: code samples, SDK snippets, and API references are automatically prefilled with your project variables. You can copy a snippet straight from the console into your application and run it without modification.

How to get started
You can try a new experience by choosing Try the Bedrock Mantle Console from within the Amazon Bedrock console, or by using the new console link directly.

You can find a project-based dashboard to show inference requests and error by range of recent dates, recently used models, and the project list. You can create a project, assign models, configure API keys, and start making inference requests in minutes.

A new model catalog shows the latest GPT, Claude, and open-weight models that are supported on the bedrock-mantle engine. You can see the details of features, tokens, pricing, input/output, pricing information, and Regional availability. You can also compare up to 3 models in a single view.

When you choose the project dashboard, you can see the models used in the project, the distribution of your token usage such as total token usage, token usage per minute, inference requests per minute, and tokens per inference request. This can inform your model selection, prompt optimization, and workload consistency decisions.

You can select up to 3 models to start evaluating to compare responses side by side with the same prompt.

To build your application in the project, choose Getting started. You can migrate existing code, build a new app with the Anthropic or OpenAI SDK, or connect an AI coding assistant to Bedrock.

Choose the API & SDK, your SDK (either Anthropic or OpenAI), your preferred programming language, and your authentication method. It shows your environment code to run these in your terminal for a quick test, or save to a .env file for your application. You can also send your first request with sample code snippets to verify your setup.

When you choose Clients, you can select the AI coding agent source such as Claude Code, Cline, Codex, Cursor, or OpenCode that you want to connect to the bedrock-mantle engine. It provides instructions on how to install the AI agent, use your AWS IAM credentials or use a Bedrock API key, set environment variables, and route requests from each AI agent through Bedrock.

To learn about Anthropic- and OpenAI-compatible APIs, choose Live API docs. You can choose Anthropic API Protocol for access to Claude model features like the Messages API or OpenAI API Protocol for access to features like Responses API.

For example, when you choose OpenAI Response API, it retrieves a model response with the given model ID. These API references are automatically prefilled with the project’s selected model ID, Region, bedrock-mantle endpoint URL, and API key reference, and they update in place as you change models or settings.

You can also choose the existing Bedrock console to manage fully-managed features such as Agents, Knowledge Bases, Guardrails, fine-tuning, or the InvokeModel and Converse APIs to run on the bedrock-runtime endpoint.

Now available
The new console experience is available in all AWS Regions where the bedrock-mantle endpoint is offered: US East (N. Virginia, Ohio), US West (Oregon), Asia Pacific (Jakarta, Mumbai, Sydney, Tokyo), Europe (Frankfurt, Ireland, London, Milan, Stockholm), and South America (São Paulo). Check the full list of Regions for future updates.

Give the new console experience a try in the new Amazon Bedrock console and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

— Channy

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