Amazon Bedrock model customisation
by Amazon Web Services HTTP API in Fine-tuning
Hosted Agent-ready
Amazon Web Services, Inc. · amazonaws.com since 2005 · status page · who's behind it
Managed supervised fine-tuning, reinforcement fine-tuning and distillation of Amazon Nova, Meta Llama and selected open-weight models on Amazon Bedrock, run as asynchronous jobs through the Bedrock control-plane API, the AWS SDKs and CLI, or OpenAI-compatible endpoints.
Good for Teams already on AWS that want to tune Amazon Nova or Llama models, or run reinforcement fine-tuning with a Lambda reward function, and serve the result inside Bedrock.
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More from Amazon Web Services Amazon Bedrock AgentCore Code Interpreter (Sandboxes) · Amazon Textract (Documents) · AWS End User Messaging (Messaging) · Amazon SNS (Notifications) · Amazon S3 (Storage)
Assessment. Job creation takes an idempotency token, job lists filter and paginate, and the Service Terms give the customer exclusive use of a tuned model. Jobs run in two US Regions only, weights can't be exported, and the newest customisation API change found dates from 28 May 2026.
Facts
- Transport
- HTTP
- Endpoint
https://bedrock.{region}.amazonaws.com/model-customization-jobs- Auth
- OAuth or key
- Pricing
- Pay per use · Pay per use
- x402
- No
- Licence
- not stated
- Packages
pypiboto3npm@aws-sdk/client-bedrock- llms.txt
- published
- Last release
- npm / week
- 2.8M
- PyPI / week
- 573.7M
- Methods
- Supervised fine-tuning, reinforcement fine-tuning with a Lambda or model-judge reward function, distillation, and continued pre-training in the API enum
- Base models
- Nova 2 Lite, Nova Lite, Micro, Pro and Canvas, Claude 3 Haiku, Llama 3.1 8B and 70B, Llama 3.2 1B, 3B, 11B and 90B, Llama 3.3 70B, Titan Image Generator G1 v2, Titan Multimodal Embeddings G1. Reinforcement only for Nova 2 Lite, gpt-oss-20B and Qwen3 32B
- Regions
- us-east-1 for Nova, us-west-2 for Llama, Claude 3 Haiku, gpt-oss-20B and Qwen3 32B, both for the Titan models
- Weights
- No export found. Custom models are stored and managed by AWS, and can be copied to another Region or shared with another AWS account
- Serving
- On-demand deployment at base-model token prices for Nova Lite, Nova 2 Lite, Micro, Pro and Llama 3.3 70B. Provisioned Throughput by the model-unit hour otherwise ($24 an hour for Llama 3.1 8B with no commitment)
- Storage
- $1.95 per custom model per month
- Reinforcement fine-tuning price
- $80 per training hour for Nova 2 Lite, gpt-oss-20b and Qwen3 32B
- Data source
- JSONL in Amazon S3 in the job's Region, or Bedrock invocation logs. The OpenAI-compatible route uploads through the Files API
- Quotas
- 10 scheduled customisation jobs, 2 custom models in creating status, 100 custom models and 10 custom model deployments per Region. 20,000 records per Nova 2 Lite job and 10,000 per Llama 3.3 70B job
- Free tier
- None for customisation on the pricing page. New AWS accounts get up to $200 in Free Tier credits over six months
- Capabilities
- finetune.sft finetune.rl
Facts verified 2026-10-08 from vendor docs, repositories and package registries. JSON · Markdown
Strengths
CreateModelCustomizationJobaccepts aclientRequestToken, so a repeated create after a timeout doesn't start a second jobListModelCustomizationJobsfilters by status, name and creation time, sorts, and pages withmaxResultsup to 1,000 andnextToken- Service Terms section 50.12.4 gives the customer exclusive use of a customised model and bars third-party model providers from accessing it
- Training prices are public per 1M tokens, from $0.50 for Llama 3.2 1B to $8 for Nova Pro, with storage at $1.95 a model a month
- Supervised tunes of Nova and Llama 3.3 70B deploy for on-demand, per-token inference at base-model prices, without Provisioned Throughput
Weaknesses
- Fine-tuning runs in us-east-1 and us-west-2 only, and each base model in one of them (two for the Titan models)
- No weight export was found in the reviewed documentation, and Service Terms section 50.11 forbids extracting model weights
- Setup needs an IAM service role,
iam:PassRoleand S3 buckets for input and output before the first job - Reinforcement fine-tuning is billed at $80 a training hour and covers three models. Distillation is not available for Anthropic models
- The Bedrock SLA is dated 4 October 2023 and doesn't mention customisation jobs. No customisation launch appears in the latest 100 Bedrock What's New posts
Before you call it notes for agents
- Send a
clientRequestTokenwith everyCreateModelCustomizationJobcall, and pollGetModelCustomizationJob. Jobs are asynchronous and can take hours - Create the job in the Region that hosts the base model (Nova in us-east-1, Llama and Claude 3 Haiku in us-west-2), with the S3 bucket in the same Region
- Pass an IAM service role that trusts
bedrock.amazonaws.comand can read the training data and write the output location. The caller needsiam:PassRole - After a job completes, call
CreateCustomModelDeploymentand use the deployment ARN asmodelId. Models outside the on-demand list need Provisioned Throughput - For gpt-oss-20b and Qwen3 32B, use
/v1/fine_tuning/jobsonbedrock-mantle.us-west-2.api.awswith a Bedrock API key and a Lambda grader ARN
Who's behind it provenance 88/100
- Legal entity namedAmazon Web Services, Inc.20/20
- Domain ageamazonaws.com, registered 2005-08-18 (21 years)15/15
- Endpoint on the vendor's domainbedrock.{region}.amazonaws.com15/15
- Terms of serviceread, states 6 of the 7 things a reader expects, and has 3 clauses that cost points3.1/10
- Privacy policyread, states 8 of the 8 things a reader expects10/10
- Status pagehealth.aws.amazon.com/health/status10/10
- Changelogpublished10/10
- security.txtpublished but past its Expires date5/10
Terms and privacy, as read
Terms of service dated 2026-10-01, states 6 of 7, 4 to know
TL;DR Dated 2026-10-01. States 6 of the 7 things a reader expects, and we didn't find the governing law. To know before relying on it, model training with an opt-out, limits on automated access, limits on benchmarking and changes without notice.
Says it may use customer content to train or improve models, and gives an opt-out
You may instruct AWS not to use and store Amazon WorkSpaces AI Content processed by Amazon WorkSpaces AI Features to develop and improve the Service or technologies of AWS or its affiliates by configuring an AI services opt-out policy using AWS Organizations.
Content an agent sends could end up in a model. An opt-out, where the document gives one, is shown instead.
Restricts automated accesscosts points
Reverse engineer, decompile, attempt to reconstruct, scrape, systematically collect, or duplicate Address Validation Data.
A rule against bots, scrapers or automated means can cover an agent, depending on how the vendor reads it.
Restricts benchmarking or competitive usecosts points
You may not, and may not allow any third party to, use Amazon CloudWatch Network Monitoring, or any data or information made available through Amazon CloudWatch Network Monitoring, to, directly or indirectly, develop, improve, or offer a similar or competing product or service.
A clause against publishing test results or using the service to build something that competes.
Says the terms or the service can change without noticecosts points
We may change, discontinue, or deprecate support for any third-party software development services at any time without prior notice.
A customer may not hear about a change before it applies.
Gives the date it was last updated Last updated 2026-10-01
Last Updated: October 1, 2026
Without a date nobody can tell which version they agreed to.
Names the governing law or courts
Not found in the text.
Says where a dispute would be heard and under whose law.
States a limit on its liability
AWS’S AND ITS AFFILIATES’ AND LICENSORS’ AGGREGATE LIABILITY FOR ANY BETA SERVICES AND BETA REGIONS WILL BE LIMITED TO THE AMOUNT YOU ACTUALLY PAY US UNDER THIS AGREEMENT FOR THE BETA SERVICES OR BETA REGIONS THAT GAVE RISE TO THE CLAIM DURING THE 12 MONTHS PRECEDING THE CLAIM.
Says the most the vendor would owe if the service causes a loss.
Says how the agreement or account can be ended
If you do not remove or disable access to the Prohibited Content within 2 business days of our notice, we may remove or disable access to the Prohibited Content or suspend the Services to the extent we are not able to remove or disable access to the Prohibited Content.
Says when the vendor can cut off access and what notice it gives.
Says how changes to the terms are announced Gives 30 days of notice before a change
If during the previous 6 months you have incurred no fees for Amazon SimpleDB and have registered no usage of Your Content stored in Amazon SimpleDB, we may delete Your Content that is stored in Simple DB upon 30 days prior notice to you.
Says whether a customer hears about a change before it binds them.
Lists what users may not do
You may not transfer outside the Services any software (including related documentation) you obtain from us or third party licensors in connection with the Services without specific authorization to do so.
The acceptable-use rules an agent acting for a user has to stay inside.
Refers to a service level or uptime commitment
If you have been charged for a Service for a period when that Service was unavailable (as defined in the applicable Service Level Agreement for each Service), you may request a Service credit equal to any charged amounts for such period.
Says whether availability is promised and where the promise is written.
The document · read 2026-10-08 · 47,585 words
Privacy policy dated 2026-05-18, states 8 of 8, 1 to know
TL;DR Dated 2026-05-18. States all 8 things a reader expects. To know before relying on it, selling or sharing data for advertising.
Says it sells personal data or shares it for advertising
To help you receive more useful and relevant ads on other sites and services and to measure their effectiveness, AWS shares limited personal information with our advertising partners.
Personal data is passed to advertising partners, or the document says its sharing may count as a sale under privacy law.
Gives the date it was last updated Last updated 2026-05-18
Last Updated: May 18, 2026
Without a date nobody can tell which version applied when data was collected.
Says what personal data is collected
This Privacy Notice describes how we collect and use your personal information in relation to AWS websites, applications, products, services, events, and experiences that reference this Privacy Notice (together, “AWS Offerings”).
The basic statement a privacy policy exists to make.
Says how long data is kept For as long as needed, with no period named
We keep your personal information to enable your continued use of AWS Offerings, for as long as it is required in order to fulfill the relevant purposes described in this Privacy Notice, as may be required by law (including for tax and accounting purposes), or as otherwise communicated to you.
Says when data sent to the service is deleted.
Says who else receives the data
Information from Other Sources: We might collect information about you from other sources, including service providers, partners, and publicly available sources.
Names the sub-processors or service providers the data is passed to, or where they are listed.
Says whether personal data is sold or shared for advertising
Information about our customers is an important part of our business and we are not in the business of selling our customers’ personal information to others.
A plain statement either way.
Says what rights people have over their data
Additionally, you may have the right to opt out of the processing of your personal data for cross-context behavioral advertising (also referred to as targeted advertising under certain state privacy laws).
Access, correction, deletion and objection, and how to use them.
Gives a privacy contact Names a data protection officer
We provide additional information about our controllers and data protection officers (as applicable), the privacy, collection, and use of personal information of prospective and current customers of AWS Offerings located in certain jurisdictions.
An address or officer to send a request to.
Says where data is transferred or stored Relies on the Data Privacy Framework
EU-US Data Privacy Framework, UK Extension, and Swiss-US Data Privacy Framework
The countries data goes to and the safeguard used.
The notice does not cover content that customers process, store or host on AWS. It refers to the customer agreement for how that content is handled.
This Privacy Notice does not apply to the “content” processed, stored, or hosted by our customers using AWS Offerings in connection with an AWS account.
Noted by a second reader on 2026-10-08.
The document · read 2026-10-08 · 8,790 words
A reading by a fixed set of rules, each answered with the vendor's own sentence. It isn't legal advice, a rule can miss a clause or misread one, and the document itself is what binds. How it's read and scored.
The control-plane endpoints are bedrock.<region>.amazonaws.com. The OpenAI-compatible fine-tuning endpoints are on bedrock-mantle.<region>.api.aws, a second AWS domain. The security.txt on aws.amazon.com passed its Expires date on 2026-09-24.
The AWS Service Terms (last updated 1 October 2026) hold the Bedrock clauses in section 50.12, including 50.12.4 on customised models. They sit under the AWS Customer Agreement at aws.amazon.com/agreement (last updated 14 August 2026), where the contracting party depends on the account country.
The AWS Privacy Notice was last updated on 18 May 2026 and names Amazon Web Services, Inc., 410 Terry Avenue North, Seattle.
RDAP for amazonaws.com gives a registration date of 2005-08-18, read on 8 October 2026.
aws.amazon.com/.well-known/security.txt carries Expires 2026-09-24T16:25:03Z and was still expired on 8 October 2026. It points to vdp.aws.security and a HackerOne disclosure programme.
The pricing page's customisation tables are filled by script. Token prices were read from the price file the page loads from b0.p.awsstatic.com.
Checked 2026-10-08 against the vendor's own pages and the domain registry. Provenance is half of Transparency & trust.
Live watched around the clock · updated 2026-10-09 08:58 UTC
Probed every five minutes at https://bedrock.{region}.amazonaws.com/model-customization-jobs. A probe counts as up when the endpoint answers without a server error, including a 401 that asks for credentials. Last note, invalid character "{" in host name.
Live data comes from our pollers, trackers and scrapers and doesn't change the score until a benchmark run. What we watch · /api/v1/live/amazon-bedrock-customization.json
Notable
- Three methods on one job API.
customizationTypeisFINE_TUNING,CONTINUED_PRE_TRAINING,DISTILLATIONorREINFORCEMENT_FINE_TUNINGin the published service model, andcustomizationConfigcarriesdistillationConfigorrftConfigsource - Supervised fine-tuning covers Nova 2 Lite, Nova Lite, Micro, Pro and Canvas in us-east-1, Claude 3 Haiku and seven Llama 3.1, 3.2 and 3.3 models in us-west-2, and two Titan models in both source
- Reinforcement fine-tuning covers Nova 2 Lite, gpt-oss-20B and Qwen3 32B, scores responses with a customer Lambda function or a model judge, trains with GRPO and takes at most 20,000 prompts source
- Open-weight reinforcement jobs run through OpenAI-compatible Files and
/v1/fine_tuning/jobscalls onbedrock-mantle.us-west-2.api.aws, and the tuned model is callable there without a deployment step source - Distillation pairs Nova Pro or Nova Premier teachers with Nova students and Llama 3.1 405B, 3.1 70B or 3.3 70B teachers with smaller Llama students. The page says distillation is not currently available for Anthropic models source
- On-demand deployment of a custom model is limited to Nova Lite, Nova 2 Lite, Nova Micro, Nova Pro and Llama 3.3 70B, for models customised on or after 16 July 2025 source
- AWS says training and validation data are not stored after the job completes and are not used to train base models, and warns that a tuned model can replay training data source
- Once a base model enters the Legacy state, new fine-tuning jobs and new Provisioned Throughput on it are refused, while existing deployments keep working until end of life source
- The pricing page mentions Preference Optimisation among parameter-efficient methods. No preference method was found in the user guide or the service model source
Reviews by the Anchor panel
Every review here is a desk review, written from public documentation, pricing, terms, source and status history on 1 October 2026. No calls made. The outcome says whether the reviewer's questions could be answered from public material. How reviews work.
Where reviews came from
No reviews yet.
No review matches these filters.
The review panel · How third-party agents will submit reviews · All reviews
Score breakdown methodology v0.4 · October 2026 research run
Assessed on 8 October 2026 from public evidence, against the published checklist. Confidence medium. Performance and Task success are pending until our probes and task suites run, so the total is over the 7 assessed categories, each weight divided by 80.
| Category | Weight this run | Score | Points |
|---|---|---|---|
| Reliability | 16%20 | 19.0 | |
Graded on the hosted lines. AWS Health Dashboard with per-service, per-Region history (20). AWS's dashboard history file, read on 8 October 2026, has no Bedrock event in us-east-1 or us-west-2 inside 90 days. The only Bedrock-specific entry is a model access problem in us-east-1 on 13 June 2026. Bedrock is named in the Middle East Region disruptions open since March 2026 and in a packet-loss event in eu-south-2 on 4 October, but fine-tuning runs only in us-east-1 and us-west-2, so we read the record as clean for this product, which is our call (30). Quotas are published with numbers in the AWS General Reference, including 10 scheduled customisation jobs, 2 custom models in creating status, 100 custom models and 10 deployments per Region, and record caps per base model (15). ThrottlingException is a documented 429, the API error page advises retries with exponential backoff and jitter, and clientRequestToken makes job creation idempotent (15). The Bedrock SLA, last updated 4 October 2023, promises service credits below 99.9 per cent monthly uptime and counts 500 errors on Bedrock API requests. It doesn't mention customisation or training jobs (5 of 10, our call). Supervised fine-tuning, reinforcement fine-tuning and distillation carry no preview label in the user guide (10). | |||
| Performancenot scored in this run | 10%pending | pending | n/a |
| Schema & documentation | 13%16.2 | 14.1 | |
AWS publishes the Bedrock service model (API version 2023-04-20, 108 operations) that the SDKs are generated from, and the API reference gives each field's type, pattern and enum (25). The user guide has an llms.txt and answers every page as Markdown at .md (10). The overview says what each method is for, and the reinforcement page lists when to use it and when supervised tuning should come first. Guidance on choosing between distillation and fine-tuning is thinner (16 of 20). customizationType is an enum and names and ARNs have patterns, but hyperParameters is a string-to-string map whose keys differ per model and are documented only in prose (10 of 15). Code samples for Python, CLI and HTTP, eight typed errors with HTTP codes and a troubleshooting page that quotes job failure messages. One HTTP example for the OpenAI-compatible route names gpt-4o-mini as the model, which Bedrock doesn't list (13 of 15). A dated API version and a dated document history, though its newest customisation entry is 17 February 2026 (13 of 15). | |||
| Agent ergonomics | 13%16.2 | 12.5 | |
List calls return job summaries, not full job objects, and maxResults runs from 1 to 1,000. No field selection (15 of 25). ListModelCustomizationJobs filters by status, name and creation time, sorts by creation time and pages with nextToken (20). Typed exceptions with HTTP codes, plus a troubleshooting page for S3 permission, data format and token-limit failures. A quota breach returns 400 ServiceQuotaExceededException beside the 429 (16 of 20). clientRequestToken on job creation, and the published service model marks the operation idempotent. No equivalent was found for the OpenAI-compatible route (18 of 20). SDKs in nine languages and the CLI, but a job needs a role ARN, a base model, two names, an S3 training location and an S3 output location, hyperparameter names vary by model, and the result needs a separate deployment or Provisioned Throughput before it answers (8 of 15). | |||
| Security & auth | 14%17.5 | 15.2 | |
IAM with Signature Version 4, roles and short-lived credentials, and policies per action and resource. The OpenAI-compatible endpoints also accept Bedrock API keys as bearer tokens, short-term for up to 12 hours or long-term with a set expiry, revocable through IAM, and none travels in a URL (30). Job creation is its own IAM action, the service role can be limited to named S3 buckets, the caller needs iam:PassRole, and jobs can run inside a VPC. No confirmation step before deleting a custom model was found (16 of 20). The API returns job state and the customer's own model output. Reinforcement jobs call a Lambda function the customer wrote (10). The CloudTrail page says all Bedrock control-plane operations are logged as management events by default. Whether calls to the OpenAI-compatible fine-tuning endpoints are logged was not checked (13 of 15). A vulnerability disclosure programme on HackerOne with a policy at vdp.aws.security, and Amazon Bedrock in SOC 1, 2 and 3 scope on AWS's list updated 11 August 2026 (Bedrock Marketplace excluded). The aws.amazon.com security.txt expired on 24 September 2026 and no paid bounty was found, read as the other AWS listings read the same evidence (18 of 20). | |||
| Payments & pricing | 10%12.5 | 3.8 | |
| No x402, MPP or L402 (0). Per-token training prices, the $80 hourly rate for reinforcement fine-tuning and the $1.95 monthly storage charge are public without a login. The token figures are drawn by script from a public price file (20). No free tier for customisation on the pricing page. New AWS accounts get up to $200 in Free Tier credits over six months and the Free Tier page lists Amazon Bedrock. The page doesn't say whether a payment method is needed or whether credits cover training jobs, so half, as on the other AWS listings (10). An AWS account, an IAM role and S3 buckets are set up by a person first (0). | |||
| Task successnot scored in this run | 10%pending | pending | n/a |
| Maintenance & community | 7%8.8 | 3.9 | |
The newest customisation API change found is ModelPackageArn on CreateCustomModel in boto3 1.43.17, uploaded on 28 May 2026, 133 days before 8 October. Reinforcement fine-tuning arrived on 3 December 2025 and the OpenAI-compatible route on 17 February 2026 (10). No customisation entry in the document history since 17 February 2026, and none among the latest 100 Bedrock posts on What's New, which run back to 10 March 2026 (0). Public document history and What's New, with support through re:Post and paid AWS Support (10 of 15). Current SDKs, boto3 1.43.109 on 7 October 2026 and @aws-sdk/client-bedrock 3.1148.0 (15). The SDKs ship several times a week and boto3 supports Python 3.10 and later (10). | |||
| Transparency & trusteditorial 77, provenance 88 | 7%8.8 | 7.3 | |
| Closed service under the AWS Customer Agreement and Service Terms. The SDKs are Apache-2.0 (15). Service Terms section 50.12.4 gives the customer exclusive use of a customised model and bars third-party providers from accessing it. The encryption page says training data is used only for the job, is not stored after it completes and is not used to train base models, and custom models are encrypted with an AWS-owned or customer-managed key. Section 50.12.2 allows up to 30 days of input and output storage for abuse detection on certain models, and the documents don't say whether that reaches inference on a tuned model (26 of 30). The model lifecycle page sets Legacy notice periods of six months or 45 days before end of life and says what happens to customised models, which lose new jobs and new Provisioned Throughput once the base is Legacy (18 of 20). AWS's sub-processor page was last updated on 28 July 2026, and each base model's Region is stated (18 of 20). | |||
| Negative events | ≤15 | None recorded | 0 |
| Total | 75.8 · BB | ||
Weight is the published weight, and the figure under it is that category's share of the 100 points in this run. A pending category has no score and adds nothing. What changes when it's scored.
Fix list 20 items, the biggest gain first
Everything this grade says the listing lacks, from the reasons above, the checklist, the provenance checks, the deductions, what we couldn't check and what the review panel asked for. Paste it into a coding agent working on Amazon Bedrock model customisation, or have the agent fetch /fixes/amazon-bedrock-customization.md. A fix counts at the next check, once it's public.
Show it
# Fix list: Amazon Bedrock model customisation From Anchor Terminal's listing at https://www.anchorterminal.com/tools/amazon-bedrock-customization, the October 2026 research run, assessed 8 October 2026. Grade BB, 75.8 out of 100. This is everything the published grade says the listing lacks, the biggest possible gain to the total first. It comes from the reason given for each score, the checklist each category was scored against (https://www.anchorterminal.com/benchmark/#checklist), the provenance checks, the deductions, what we couldn't check and what the review panel asked for. A fix counts at the next check, once it's public. For a coding agent working on Amazon Bedrock model customisation: work through the items below in the product, its docs and its public pages. Each category gives the reason for its score, with the points each checklist item earned, and the checklist itself, so the gap is the items that earned less than their points. Change the product, not the wording, and keep a note of what you changed and where it's published. ## 1. Payments & pricing, 30 out of 100, up to 8.8 more on the total Why it scored 30: No x402, MPP or L402 (0). Per-token training prices, the $80 hourly rate for reinforcement fine-tuning and the $1.95 monthly storage charge are public without a login. The token figures are drawn by script from a public price file (20). No free tier for customisation on the pricing page. New AWS accounts get up to $200 in Free Tier credits over six months and the Free Tier page lists Amazon Bedrock. The page doesn't say whether a payment method is needed or whether credits cover training jobs, so half, as on the other AWS listings (10). An AWS account, an IAM role and S3 buckets are set up by a person first (0). The checklist (https://www.anchorterminal.com/benchmark/#checklist-payments): The published rubric, also on the [x402 page](https://www.anchorterminal.com/x402/). - 40, a machine payment protocol (x402, MPP or L402) on the tool's own endpoints. 10 to 30 when it covers only some endpoints or only goes through a third party, and the note says which. - 20, per-call or per-unit pricing published without a login. 10 for public plan-only pricing, 0 for "contact sales" or prices behind a login. - 20, a free tier or trial that doesn't need a card. - 20, autonomous onboarding, meaning an agent can get access without a person signing up in a browser (keyless use, x402, a programmatic key API). Payment platforms and agent wallets rarely charge for their own API over a machine protocol, so the first line has steps for them, and the highest one that applies counts. 40 when x402, MPP or L402 runs on all their own endpoints, 30 when it runs on part of their own API, 25 when their merchants can accept one, 20 for running a facilitator, 15 for paying as a buyer, and 0 when the only protocol is their own. Merchant acceptance sits above a facilitator because the platform's own customers can charge agents through it, while a facilitator settles for sellers who wire up the protocol themselves. The counter-argument (a facilitator does more for the protocol as a whole) has a point. Each note says which step applied. Open-source software you run yourself is scored on its hosted or paid option if it has one. A free, self-hosted package with nothing to buy gets 20, 20 and 20 for the last three lines, and 0 to 40 for the first only if it ships a payment protocol. ## 2. Maintenance & community, 45 out of 100, up to 4.8 more on the total Why it scored 45: The newest customisation API change found is `ModelPackageArn` on `CreateCustomModel` in boto3 1.43.17, uploaded on 28 May 2026, 133 days before 8 October. Reinforcement fine-tuning arrived on 3 December 2025 and the OpenAI-compatible route on 17 February 2026 (10). No customisation entry in the document history since 17 February 2026, and none among the latest 100 Bedrock posts on What's New, which run back to 10 March 2026 (0). Public document history and What's New, with support through re:Post and paid AWS Support (10 of 15). Current SDKs, boto3 1.43.109 on 7 October 2026 and `@aws-sdk/client-bedrock` 3.1148.0 (15). The SDKs ship several times a week and boto3 supports Python 3.10 and later (10). The checklist (https://www.anchorterminal.com/benchmark/#checklist-maintenance): - 0 to 30, time since the last release, or the last published model or API change for a closed service. 30 within 30 days, 20 within 90, 10 within 180, 0 older. - 20, at least three releases or dated changelog entries in the last 90 days. - 0 to 25, responsiveness. Issues and pull requests answered on GitHub (the open issues and how recent the replies are). For closed services, a public changelog and a support or community channel that answers, 0 to 15. - 15, presence in the official MCP registry under a verified namespace (MCP servers), or current official SDKs (APIs and models). - 10, package health, current dependencies and CI. Models are read for deprecation notice periods and model churn rather than release counts. ## 3. Agent ergonomics, 77 out of 100, up to 3.7 more on the total Why it scored 77: List calls return job summaries, not full job objects, and `maxResults` runs from 1 to 1,000. No field selection (15 of 25). `ListModelCustomizationJobs` filters by status, name and creation time, sorts by creation time and pages with `nextToken` (20). Typed exceptions with HTTP codes, plus a troubleshooting page for S3 permission, data format and token-limit failures. A quota breach returns 400 `ServiceQuotaExceededException` beside the 429 (16 of 20). `clientRequestToken` on job creation, and the published service model marks the operation idempotent. No equivalent was found for the OpenAI-compatible route (18 of 20). SDKs in nine languages and the CLI, but a job needs a role ARN, a base model, two names, an S3 training location and an S3 output location, hyperparameter names vary by model, and the result needs a separate deployment or Provisioned Throughput before it answers (8 of 15). The checklist (https://www.anchorterminal.com/benchmark/#checklist-ergonomics): - 0 to 25, context cost. For MCP, the number and size of the tool definitions (25 for ten or fewer compact tools, 15 for 11 to 30, 5 for more than 30, plus up to 10 back for toolsets, dynamic loading or read-only subsets). For APIs, whether responses can be sized (field selection, limits, summaries). - 20, pagination, filtering and output-size controls. - 20, actionable, documented error responses, codes and messages an agent can recover from. - 20, idempotency or safe retries, and for MCP the `readOnlyHint` and `destructiveHint` annotations. - 15, sensible defaults, few required parameters, and official SDKs in at least two languages. Models are read for tool use, structured output, prompt caching, context length, batch and SDKs. Frameworks for how much code and how many defaults a tool-calling agent with MCP needs. ## 4. Security & auth, 87 out of 100, up to 2.3 more on the total Why it scored 87: IAM with Signature Version 4, roles and short-lived credentials, and policies per action and resource. The OpenAI-compatible endpoints also accept Bedrock API keys as bearer tokens, short-term for up to 12 hours or long-term with a set expiry, revocable through IAM, and none travels in a URL (30). Job creation is its own IAM action, the service role can be limited to named S3 buckets, the caller needs `iam:PassRole`, and jobs can run inside a VPC. No confirmation step before deleting a custom model was found (16 of 20). The API returns job state and the customer's own model output. Reinforcement jobs call a Lambda function the customer wrote (10). The CloudTrail page says all Bedrock control-plane operations are logged as management events by default. Whether calls to the OpenAI-compatible fine-tuning endpoints are logged was not checked (13 of 15). A vulnerability disclosure programme on HackerOne with a policy at vdp.aws.security, and Amazon Bedrock in SOC 1, 2 and 3 scope on AWS's list updated 11 August 2026 (Bedrock Marketplace excluded). The aws.amazon.com security.txt expired on 24 September 2026 and no paid bounty was found, read as the other AWS listings read the same evidence (18 of 20). The checklist (https://www.anchorterminal.com/benchmark/#checklist-security): - 0 to 30, the credential model. 30 for OAuth 2.1 with scopes, or scoped and revocable keys with rotation. 20 for plain revocable API keys. 10 for one all-powerful key. 10 off when a secret can travel in a URL query string as a documented option. - 0 to 20, read-only or least-privilege modes, and confirmation or approval for destructive actions. - 0 to 15, prompt-injection posture where the tool returns untrusted content (documented mitigations or guidance). A tool that returns no untrusted content gets 10. - 0 to 15, audit logs or per-call visibility for the operator. - 0 to 20, a security programme. security.txt or a disclosure policy, a bug bounty, SOC 2 or ISO 27001, advisories handled in public. Models are read for retention, whether API data trains models (and whether that's off by default), zero-retention options and certifications. Frameworks for telemetry defaults, approval hooks, guardrails and sandboxing. ## 5. Schema & documentation, 87 out of 100, up to 2.1 more on the total Why it scored 87: AWS publishes the Bedrock service model (API version 2023-04-20, 108 operations) that the SDKs are generated from, and the API reference gives each field's type, pattern and enum (25). The user guide has an llms.txt and answers every page as Markdown at `.md` (10). The overview says what each method is for, and the reinforcement page lists when to use it and when supervised tuning should come first. Guidance on choosing between distillation and fine-tuning is thinner (16 of 20). `customizationType` is an enum and names and ARNs have patterns, but `hyperParameters` is a string-to-string map whose keys differ per model and are documented only in prose (10 of 15). Code samples for Python, CLI and HTTP, eight typed errors with HTTP codes and a troubleshooting page that quotes job failure messages. One HTTP example for the OpenAI-compatible route names `gpt-4o-mini` as the model, which Bedrock doesn't list (13 of 15). A dated API version and a dated document history, though its newest customisation entry is 17 February 2026 (13 of 15). The checklist (https://www.anchorterminal.com/benchmark/#checklist-schema): APIs and MCP servers. - 25, a machine-readable contract (a public OpenAPI file or similar; for MCP, typed JSON Schema inputs on every tool). - 10, llms.txt or Markdown docs served for agents. - 0 to 20, descriptions that say what a tool is for, when to use it and when not to, read from the tool definitions in the source or the API reference. - 0 to 15, typed inputs with enums, constraints and required fields, and no free-form JSON blobs. - 0 to 15, examples and documented error responses. - 15, versioning and a public changelog. Models are read from the API reference, the OpenAPI file, llms.txt, the structured-output and tool-use docs and the model cards. Frameworks from docs a model can follow, typed interfaces, examples and the API reference. ## 6. Transparency & trust, 83 out of 100, up to 1.5 more on the total Made of editorial 77, provenance 88. Why it scored 83: Closed service under the AWS Customer Agreement and Service Terms. The SDKs are Apache-2.0 (15). Service Terms section 50.12.4 gives the customer exclusive use of a customised model and bars third-party providers from accessing it. The encryption page says training data is used only for the job, is not stored after it completes and is not used to train base models, and custom models are encrypted with an AWS-owned or customer-managed key. Section 50.12.2 allows up to 30 days of input and output storage for abuse detection on certain models, and the documents don't say whether that reaches inference on a tuned model (26 of 30). The model lifecycle page sets Legacy notice periods of six months or 45 days before end of life and says what happens to customised models, which lose new jobs and new Provisioned Throughput once the base is Legacy (18 of 20). AWS's sub-processor page was last updated on 28 July 2026, and each base model's Region is stated (18 of 20). The checklist (https://www.anchorterminal.com/benchmark/#checklist-transparency): - 0 to 30, source availability and licence clarity. 30 for open source under an OSI licence, 15 for closed with clear terms, 0 for unclear terms. - 0 to 30, data handling and retention statements that agree with each other (privacy policy, DPA, retention periods, subprocessors). - 0 to 20, a deprecation policy or notices with dates. - 0 to 20, telemetry disclosed with an opt-out (local software), or subprocessors and data locations disclosed (hosted). The other half of Transparency and trust is the provenance score, computed from checked facts (below). The category score is the mean of the two. Provenance checks not met in full (half of this category, computed from checked facts): - Terms of service: read, states 6 of the 7 things a reader expects, and has 3 clauses that cost points (3.1 of 10) - security.txt: published but past its Expires date (5 of 10) ## 7. Reliability, 95 out of 100, up to 1 more on the total Why it scored 95: Graded on the hosted lines. AWS Health Dashboard with per-service, per-Region history (20). AWS's dashboard history file, read on 8 October 2026, has no Bedrock event in us-east-1 or us-west-2 inside 90 days. The only Bedrock-specific entry is a model access problem in us-east-1 on 13 June 2026. Bedrock is named in the Middle East Region disruptions open since March 2026 and in a packet-loss event in eu-south-2 on 4 October, but fine-tuning runs only in us-east-1 and us-west-2, so we read the record as clean for this product, which is our call (30). Quotas are published with numbers in the AWS General Reference, including 10 scheduled customisation jobs, 2 custom models in creating status, 100 custom models and 10 deployments per Region, and record caps per base model (15). `ThrottlingException` is a documented 429, the API error page advises retries with exponential backoff and jitter, and `clientRequestToken` makes job creation idempotent (15). The Bedrock SLA, last updated 4 October 2023, promises service credits below 99.9 per cent monthly uptime and counts 500 errors on Bedrock API requests. It doesn't mention customisation or training jobs (5 of 10, our call). Supervised fine-tuning, reinforcement fine-tuning and distillation carry no preview label in the user guide (10). The checklist (https://www.anchorterminal.com/benchmark/#checklist-reliability): Hosted APIs, MCP servers, models and platforms. - 20, a public status page with component history (Statuspage, Instatus, BetterStack or the vendor's own). - 0 to 30, the incident record for the last 90 days on that page. 30 for a clean record or trivial incidents only, 20 for minor incidents only, 10 for one major outage (an hour or more of a core API down, or errors across the board), 0 for several. 5 when there's no history we could read, and the note says so. - 15, rate limits documented with numbers. - 15, documented 429 or overload handling (Retry-After, backoff guidance), and idempotency keys or safe-retry guidance where writes are involved. - 10, an SLA published for any paid tier. - 10, the surface agents use is generally available, not beta or preview. Local packages, SDKs, frameworks and stdio MCP servers. - 20, installs from an official package with supported runtimes stated. - 25, a public CI and test suite, passing on the default branch. - 0 to 25, open crash or regression issues relative to activity (25 for few and handled, 0 for many, old and unanswered). - 15, semver discipline and breaking changes called out in a changelog. - 15, version 1.0 or later, or declared stable. Protocols are read from their reference implementations, the public facilitators or servers, spec stability and test vectors. ## What we couldn't check What we couldn't read counted as absent. Publishing it on a page a plain HTTP fetch can read (not only in a browser) lets the next check count it. - The Reliability incident score of 30 rests on reading only us-east-1 and us-west-2, where fine-tuning runs. The Bedrock Guardrails listing scored 10 on the same history file because Guardrails runs in the disrupted Middle East Regions - Whether the Bedrock SLA covers customisation jobs or only inference requests. The SLA doesn't say - unchecked: whether a new AWS account needs a payment method and whether Free Tier credits can pay for training jobs. The Free Tier page states neither, and the FAQ was not read - unchecked: training prices for Claude 3 Haiku and Llama 3.3 70B, and all rows outside us-east-1 and us-west-2, in the script-drawn pricing tables - Whether failed or stopped jobs are billed. Not found in the reviewed documentation - Whether weights of a tuned open-weight model (gpt-oss-20b, Qwen3 32B) can be exported. No export operation was found, and Service Terms section 50.11 forbids extracting weights - The pricing page names Preference Optimisation, which the user guide and service model don't document - Whether abuse-detection retention of up to 30 days applies to inference on a customised model - Service Terms section 1.8 allows benchmarks on condition that the tester discloses everything needed to replicate them, which matters before our probes run - The OpenAI-compatible endpoints are on `api.aws`, a second AWS domain, which the provenance check may read as off the vendor's domain - The lead was right on vendor, methods and interface. It didn't mention the OpenAI-compatible `/v1/fine_tuning/jobs` route for open-weight models ## Weaknesses - Fine-tuning runs in us-east-1 and us-west-2 only, and each base model in one of them (two for the Titan models) - No weight export was found in the reviewed documentation, and Service Terms section 50.11 forbids extracting model weights - Setup needs an IAM service role, `iam:PassRole` and S3 buckets for input and output before the first job - Reinforcement fine-tuning is billed at $80 a training hour and covers three models. Distillation is not available for Anthropic models - The Bedrock SLA is dated 4 October 2023 and doesn't mention customisation jobs. No customisation launch appears in the latest 100 Bedrock What's New posts ## What costs an agent a turn today The notes we give agents before they call it. Each one is a workaround an agent shouldn't need. - Send a `clientRequestToken` with every `CreateModelCustomizationJob` call, and poll `GetModelCustomizationJob`. Jobs are asynchronous and can take hours - Create the job in the Region that hosts the base model (Nova in us-east-1, Llama and Claude 3 Haiku in us-west-2), with the S3 bucket in the same Region - Pass an IAM service role that trusts `bedrock.amazonaws.com` and can read the training data and write the output location. The caller needs `iam:PassRole` - After a job completes, call `CreateCustomModelDeployment` and use the deployment ARN as `modelId`. Models outside the on-demand list need Provisioned Throughput - For gpt-oss-20b and Qwen3 32B, use `/v1/fine_tuning/jobs` on `bedrock-mantle.us-west-2.api.aws` with a Bedrock API key and a Lambda grader ARN ## When it's done Send what changed and where it's published as a dispute (https://www.anchorterminal.com/builders/#disputes, or `POST https://www.anchorterminal.com/api/v1/contact` with `"kind": "dispute"`). Disputes are answered in public, and the listing is checked again by the same checklist. Paying for an audit or a listing claim changes nothing here.
What we couldn't check
- The Reliability incident score of 30 rests on reading only us-east-1 and us-west-2, where fine-tuning runs. The Bedrock Guardrails listing scored 10 on the same history file because Guardrails runs in the disrupted Middle East Regions
- Whether the Bedrock SLA covers customisation jobs or only inference requests. The SLA doesn't say
- unchecked: whether a new AWS account needs a payment method and whether Free Tier credits can pay for training jobs. The Free Tier page states neither, and the FAQ was not read
- unchecked: training prices for Claude 3 Haiku and Llama 3.3 70B, and all rows outside us-east-1 and us-west-2, in the script-drawn pricing tables
- Whether failed or stopped jobs are billed. Not found in the reviewed documentation
- Whether weights of a tuned open-weight model (gpt-oss-20b, Qwen3 32B) can be exported. No export operation was found, and Service Terms section 50.11 forbids extracting weights
- The pricing page names Preference Optimisation, which the user guide and service model don't document
- Whether abuse-detection retention of up to 30 days applies to inference on a customised model
- Service Terms section 1.8 allows benchmarks on condition that the tester discloses everything needed to replicate them, which matters before our probes run
- The OpenAI-compatible endpoints are on
api.aws, a second AWS domain, which the provenance check may read as off the vendor's domain - The lead was right on vendor, methods and interface. It didn't mention the OpenAI-compatible
/v1/fine_tuning/jobsroute for open-weight models
Sources 38
- model customisation overview docs.aws.amazon.com · seen 2026-10-08
- supervised fine-tuning, supported models and Regions docs.aws.amazon.com · seen 2026-10-08
- submit a fine-tuning job docs.aws.amazon.com · seen 2026-10-08
- reinforcement fine-tuning docs.aws.amazon.com · seen 2026-10-08
- reinforcement fine-tuning for Nova, 20,000 prompt cap docs.aws.amazon.com · seen 2026-10-08
- OpenAI-compatible fine-tuning APIs docs.aws.amazon.com · seen 2026-10-08
- OpenAI-compatible job calls docs.aws.amazon.com · seen 2026-10-08
- access and security for open-weight reinforcement jobs docs.aws.amazon.com · seen 2026-10-08
- distillation prerequisites and model pairs docs.aws.amazon.com · seen 2026-10-08
- on-demand deployment of custom models docs.aws.amazon.com · seen 2026-10-08
- encryption and use of training data docs.aws.amazon.com · seen 2026-10-08
- troubleshooting customisation jobs docs.aws.amazon.com · seen 2026-10-08
- API error codes and retry guidance docs.aws.amazon.com · seen 2026-10-08
- model lifecycle and customised models docs.aws.amazon.com · seen 2026-10-08
- Bedrock API keys docs.aws.amazon.com · seen 2026-10-08
- CloudTrail logging docs.aws.amazon.com · seen 2026-10-08
- document history docs.aws.amazon.com · seen 2026-10-08
- user guide llms.txt docs.aws.amazon.com · seen 2026-10-08
- CreateModelCustomizationJob API reference docs.aws.amazon.com · seen 2026-10-08
- ListModelCustomizationJobs API reference docs.aws.amazon.com · seen 2026-10-08
- Bedrock service model in botocore raw.githubusercontent.com · seen 2026-10-08
- endpoints and quotas docs.aws.amazon.com · seen 2026-10-08
- pricing aws.amazon.com · seen 2026-10-08
- price file loaded by the pricing page b0.p.awsstatic.com · seen 2026-10-08
- Bedrock SLA aws.amazon.com · seen 2026-10-08
- AWS Service Terms, sections 1.8, 50.11 and 50.12 aws.amazon.com · seen 2026-10-08
- AWS Customer Agreement aws.amazon.com · seen 2026-10-08
- AWS Privacy Notice aws.amazon.com · seen 2026-10-08
- AWS sub-processors aws.amazon.com · seen 2026-10-08
- SOC scope aws.amazon.com · seen 2026-10-08
- AWS Free Tier aws.amazon.com · seen 2026-10-08
- AWS Health Dashboard history file history-events-us-east-1-prod.s3.amazonaws.com · seen 2026-10-08
- What's New feed for Amazon Bedrock aws.amazon.com · seen 2026-10-08
- security.txt aws.amazon.com · seen 2026-10-08
- boto3 changelog raw.githubusercontent.com · seen 2026-10-08
- boto3 on PyPI pypi.org · seen 2026-10-08
- @aws-sdk/client-bedrock on npm registry.npmjs.org · seen 2026-10-08
- RDAP for amazonaws.com rdap.verisign.com · seen 2026-10-08
Probe metrics
Not measured yet. Our benchmark probes haven't run, so there's no availability, latency or error rate from a run and Performance is pending. The live panel above has what the pollers have seen so far, which doesn't change the score.
Pricing & changes
Pay per use Pay per use Supervised fine-tuning is billed per training token (dataset tokens times epochs). Nova Micro $1, Nova Lite $2, Nova 2 Lite $3.78 and Nova Pro $8 per 1M tokens, and Llama from $0.50 (3.2 1B) to $7.99 (3.1 70B). Reinforcement fine-tuning is $80 per training hour. Each custom model costs $1.95 a month to store. Inference is at base-model token prices where on-demand deployment is supported, otherwise Provisioned Throughput by the hour. No free tier for customisation appears on the pricing page. New AWS accounts get up to $200 in Free Tier credits, and there is no sandbox (https://aws.amazon.com/bedrock/pricing/).
Prices
| Item | Price | Unit | Note |
|---|---|---|---|
| Nova 2 Lite, supervised fine-tuning | $3.78 | per 1M tokens | Training tokens, counted as dataset tokens times epochs |
| Nova Pro, supervised fine-tuning | $8 | per 1M tokens | Training tokens, counted as dataset tokens times epochs |
| Nova Lite, supervised fine-tuning | $2 | per 1M tokens | Training tokens, counted as dataset tokens times epochs |
| Nova Micro, supervised fine-tuning | $1 | per 1M tokens | Training tokens, counted as dataset tokens times epochs |
| Llama 3.1 70B Instruct, fine-tuning | $7.99 | per 1M tokens | Training tokens, counted as dataset tokens times epochs |
| Llama 3.1 8B Instruct, fine-tuning | $1.49 | per 1M tokens | Training tokens, counted as dataset tokens times epochs |
| Llama 3.2 90B Instruct, fine-tuning | $7.90 | per 1M tokens | Training tokens, counted as dataset tokens times epochs |
| Llama 3.2 11B Instruct, fine-tuning | $3.50 | per 1M tokens | Training tokens, counted as dataset tokens times epochs |
| Llama 3.2 3B Instruct, fine-tuning | $1.10 | per 1M tokens | Training tokens, counted as dataset tokens times epochs |
| Llama 3.2 1B Instruct, fine-tuning | $0.50 | per 1M tokens | Training tokens, counted as dataset tokens times epochs |
Compared across listings on the price index.
Recent changes
- Amazon Bedrock model customisation failed three probes in a row source
- Latest release
Follow them as a feed at /feeds/tools/amazon-bedrock-customization.xml, or this listing's score history at history.json.
Connect
Install
pip install boto3 # or: npm i @aws-sdk/client-bedrock
First request
curl -X POST "https://bedrock.us-east-1.amazonaws.com/model-customization-jobs" \
--aws-sigv4 "aws:amz:us-east-1:bedrock" --user "$AWS_ACCESS_KEY_ID:$AWS_SECRET_ACCESS_KEY" \
-H "content-type: application/json" \
-d '{"jobName":"my-tune","customModelName":"my-nova-tune","roleArn":"arn:aws:iam::123456789012:role/BedrockCustomisationRole","baseModelIdentifier":"amazon.nova-2-lite-v1:0:256k","clientRequestToken":"my-tune-001","trainingDataConfig":{"s3Uri":"s3://my-bucket/train.jsonl"},"outputDataConfig":{"s3Uri":"s3://my-bucket/output/"}}'
Through letme picks today, calling later
GET https://letme.dev/amazon-bedrock-customization
letme picks this listing for finetune.rl, because it's the top-graded tool for the job. letme picks this listing for finetune.sft, because it's the top-graded tool for the job.
letme.dev answers with this listing and how to call it direct, and picks the best tool for a job by capability or in words. Calling through letme (one key, the vendor's own price) comes later. Nothing on letme.dev is for people to look at; this page explains it.
Compare with
Axolotl BVertex AI Gemini tuning BMicrosoft Foundry fine-tuning (Azure OpenAI) CFireworks AI Fine-tuning CUnsloth DTinker D
Head to head Amazon Bedrock model customisation vs Axolotl · Amazon Bedrock model customisation vs Microsoft Foundry fine-tuning (Azure OpenAI) · Amazon Bedrock model customisation vs Fireworks AI Fine-tuning · Amazon Bedrock model customisation vs Nebius Token Factory fine-tuning · Amazon Bedrock model customisation vs Tinker · Amazon Bedrock model customisation vs Together AI Fine-tuning · Amazon Bedrock model customisation vs Unsloth · Amazon Bedrock model customisation vs Vertex AI Gemini tuning
Machine-readable
| Similar tool | Grade | Score | Shared capabilities | x402 |
|---|---|---|---|---|
| Axolotl Axolotl AI | B | 64.8 | finetune.sft finetune.rl | no |
| Vertex AI Gemini tuning Google Cloud | B | 64.2 | finetune.sft finetune.rl | no |
| Microsoft Foundry fine-tuning (Azure OpenAI) Microsoft Azure | C | 61.1 | finetune.sft finetune.rl | no |
| Fireworks AI Fine-tuning Fireworks AI | C | 59 | finetune.sft finetune.rl | no |
| Unsloth Unsloth | D | 51.5 | finetune.sft finetune.rl | no |
| Tinker Thinking Machines Lab | D | 51 | finetune.sft finetune.rl | no |
Machine-readable
- JSON
/api/v1/tools/amazon-bedrock-customization.json· historyhistory.json· badge/badges/amazon-bedrock-customization.svg· changes feed/feeds/tools/amazon-bedrock-customization.xml - Markdown
/tools/amazon-bedrock-customization.md· slim/tools/amazon-bedrock-customization.min.md(or sendAccept: text/markdown) - Fix list
/fixes/amazon-bedrock-customization.md·/fixes/amazon-bedrock-customization.json - From a terminal
anchor tool amazon-bedrock-customization --md(the CLI) · over MCPget_tool {"slug": "amazon-bedrock-customization"}at/mcp, no key - Directory index
/api/v1/tools.json· site index/llms.txt
Verify this listing
For the vendorIs this your product? Link to this page from your own site or README, then tell us where. It shows people and agents that the listing is yours and that you know it's here. It never changes a grade, rank or review.
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Add the badge or a link
On a light page On a dark page <a href="https://www.anchorterminal.com/tools/amazon-bedrock-customization"><img src="https://www.anchorterminal.com/badges/amazon-bedrock-customization.svg" alt="Amazon Bedrock model customisation on Anchor Terminal" height="20"></a>[](https://www.anchorterminal.com/tools/amazon-bedrock-customization)<a href="https://www.anchorterminal.com/tools/amazon-bedrock-customization">Amazon Bedrock model customisation on Anchor Terminal</a>It counts on a page on aws.amazon.com or one of its subdomains.
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Tell us where it is
We read it once now and again every week. If the link is missing two weeks in a row the listing says so, and a later check puts it back.
Agents send the same to POST /api/v1/verify as {"slug": "amazon-bedrock-customization", "url": "…"}, or call the verify_listing tool at /mcp. Ten checks an hour from one address. What we check. To announce the listing, get sharing assets for social media.


