What this error means

anthropic.claude-3-haiku-20240307-v1:0 with on-demand throughput isn't supported. Retry your request with the ID or ARN of an inference profile is a Anthropic API failure pattern reported for developers trying to fix aws bedrock anthropic claude haiku on-demand throughput not supported error. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

AWS Bedrock InvokeModel throws ValidationException: 'Invocation of model ID anthropic.claude-3-haiku-20240307-v1:0 with on-demand throughput isn't supported.' Must use inference profile ARN instead. Migration guide needed.

Common causes

  • Developers migrating to AWS Bedrock for Claude models get ValidationException because model ID format changed; requires inference profile ARN instead of model ID
  • AWS Bedrock InvokeModel throws ValidationException: 'Invocation of model ID anthropic.claude-3-haiku-20240307-v1:0 with on-demand throughput isn't supported.' Must use inference profile ARN instead. Migration guide needed.

Quick fixes

  1. Confirm the exact error signature matches anthropic.claude-3-haiku-20240307-v1:0 with on-demand throughput isn't supported. Retry your request with the ID or ARN of an inference profile.
  2. Check the Anthropic API account, local tool state, and provider configuration involved in the failing workflow.
  3. Compare the failing environment with a known working setup, then change one configuration value at a time.

Platform/tool-specific checks

  • Verify the command, editor, extension, or API client that produced the error.
  • Compare local settings with CI, deployment, or editor-level settings when the error appears in only one environment.
  • Avoid deleting credentials, local model data, or project settings until the failing scope is clear.

Step-by-step troubleshooting

  1. Capture the exact error message and the command, editor action, or request that triggered it.
  2. Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
  3. Review the source evidence below and compare it with your environment.
  4. Apply one change at a time and rerun the smallest failing action.
  5. Keep the working fix documented for the team or deployment environment.

How to prevent it

  • Keep provider/tool configuration documented.
  • Record non-secret diagnostics such as tool version, provider name, model name, and command path.
  • Add a lightweight check before CI or production workflows depend on the tool.