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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallStart with the Bedrock error type, not just the HTTP status. A 403 usually sends you to the caller’s IAM permissions or account-level model conditions; 400 points to request validation; 404 points to a model, endpoint, profile, or Region mismatch; 429 usually means an account quota or rate limit; and 503 or 529 indicates temporary serving capacity. Capture the complete response and request context first, then follow the branch that matches the actual error.
GPT-6 model names, invoke-ready identifiers, supported APIs, and Regions can change. AWS quota references currently list GPT-6 Luna and GPT-6 Sol entries, but those entries do not by themselves establish a copyable Bedrock model ID or availability in your account. Select the model from the live Bedrock catalog for the target Region and endpoint instead of copying an identifier from an old example.
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Capture the failure before changing anything
Changing several settings at once destroys the evidence needed to distinguish authorization, configuration, quota, and capacity problems. Save one complete failed request and its response, then make one controlled change at a time.
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Record the request context
- HTTP status code.
- Exact Bedrock error type, such as
AccessDeniedException,ValidationError,ResourceNotFound,ThrottlingException,ServiceUnavailable, oroverloaded_error. - The complete error message and any response headers, including
Retry-After. - AWS Region, endpoint, model ID or inference-profile ID, and invocation API.
- Bedrock request ID and the UTC time of the failure.
For a throughput problem, also record input and output token rates, request rate, concurrent requests, max_tokens, and the time and shape of any traffic ramp. Those measurements let you compare the workload with the quota that actually applies and give AWS Support enough information to investigate a persistent service issue.
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Use the error type to choose the first diagnostic branch
| Signal | What it usually means | First checks |
|---|---|---|
AccessDeniedException / 403 |
The active caller is not authorized, or an account/model condition is blocking access. | Active principal, allowed Bedrock action, temporary-credential validity, and restrictive identity, resource, permissions-boundary, session, or organization policies. |
ValidationError / 400 |
The request shape or a field value is invalid for the selected operation. | Required fields, data types, value ranges, provider-specific fields, and whether the model supports that API. |
ResourceNotFound / 404 |
The named model, endpoint, inference profile, or Region resource is wrong or unavailable. | Current catalog entry, exact identifier, endpoint, profile, and Region. |
ThrottlingException / 429 |
An account, model, or endpoint quota was exceeded. | The exact endpoint’s Service Quotas entries, token and request rates, concurrency, and request token limits. |
ServiceUnavailable / 503 |
Bedrock temporarily cannot serve the request because of demand or a capacity constraint. | Retry with exponential backoff and jitter, honor Retry-After, and reduce concurrency if it continues. |
overloaded_error / 529 |
Temporary model-serving overload. | Apply bounded retries with jitter, avoid synchronized retry bursts, and consider supported alternate routing. |
An HTTP code alone is insufficient. For example, a 403 caused by an account agreement or model-access condition needs a different remedy from a missing IAM action, while a 503 is not evidence that your account’s 429 quota was exceeded.
Fix access and authorization failures
Confirm the identity that is really making the call
Inspect the credentials in the same process, container, Lambda function, notebook, or host that sends the request. A shell profile can use one role while a deployed workload uses another. Check the account and ARN associated with the active credentials, and verify that temporary credentials have not expired or been replaced by an incomplete credential set.
Verify the required Bedrock action
Review the identity policy for the specific invocation operation and resource pattern used by the call. A policy that permits one Bedrock API or one resource type does not automatically permit another. Check identity policies, permissions boundaries, session policies, service-control policies, and any resource-based restrictions that apply to the workload. For a role, confirm that the trust relationship allows the intended principal to assume it and that the runtime actually uses that role.
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Separate IAM denial from model-account conditions
Not every 403 is solved by adding a broader IAM statement. AWS error responses can also describe model-specific account, agreement, or marketplace conditions. Read the exact error name and message and follow the remedy for that condition. Do not grant administrator-level access as a diagnostic shortcut; first identify the denied action and the policy statement that should authorize it.
Re-test with the same Region and endpoint
After correcting the caller or policy, retry the original request without changing the model, Region, endpoint, or request body. This confirms that authorization was the blocking factor. If the error changes to 404 or 400, authorization is no longer the first problem and the next branch is model or request validation.
Resolve model, endpoint, Region, and request-shape errors
When the response is ResourceNotFound (404)
- Open the current Bedrock model catalog in the account and target Region.
- Copy the model’s current identifier exactly, including punctuation and case.
- Check whether the request names a base model, an inference profile, or an endpoint-specific resource. Do not substitute one identifier type for another.
- Confirm that the SDK or CLI is pointed at the intended Region and endpoint.
- Verify that the model is available to the account and that the chosen invocation route supports it.
A stale local list is a common cause of 404 responses. The presence of a GPT-6 Luna or GPT-6 Sol quota entry in a general reference does not prove that an invoke-ready identifier exists in every Region or that the model is supported by every Bedrock API. Use the live catalog and the model’s compatibility information for the exact account and Region.
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When the response is ValidationError (400)
Compare every request field with the reference for the operation you are calling. Check required fields, JSON nesting, string and numeric types, enumerated values, token limits, message/content structure, and provider-specific parameters. Remove fields copied from another model or API until the smallest valid request succeeds, then add optional settings back one at a time.
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Also verify that the selected model supports the endpoint and API. A valid-looking payload can still fail validation when sent through an invocation surface the model does not implement. Do not infer compatibility from a model’s name or from a different Region.
Keep endpoint and API selection consistent
Record whether the client is using the Bedrock runtime endpoint or the Bedrock Mantle endpoint, and which invocation operation the SDK calls. Endpoint support, request schemas, quotas, and model availability are not interchangeable. A model may be listed in one catalog view but unavailable through the endpoint or operation selected by the application.
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Understand the two quota systems before treating a 429
A ThrottlingException (429) generally means that the account exceeded a quota or rate limit. The relevant limit depends on the endpoint, model, Region, and workload shape. Check Service Quotas for the exact combination rather than applying a generic requests-per-minute assumption.
| Endpoint | Quota characteristics | Operational consequence |
|---|---|---|
bedrock-runtime |
Per-model token-rate quotas and, for some models, request-rate quotas. Multiple supported inference APIs can draw from the same runtime model quota. | Switching invocation APIs on this endpoint may not increase capacity; calls still consume the shared model allocation. |
bedrock-mantle |
Scaling guidance emphasizes input and output tokens per minute and the request’s max_tokens. The endpoint does not enforce RPM quotas. |
Shape token demand and max_tokens; do not diagnose it with runtime RPM assumptions. |
Measure the demand that consumed the quota
- Input tokens per minute and output tokens per minute.
- Requests per minute, when the runtime quota includes an RPM limit.
- Peak concurrency and queue depth.
- The requested
max_tokens, especially on Mantle. - Whether several applications or APIs share the same model and endpoint allocation.
Reduce concurrency, smooth bursts, shorten prompts where appropriate, and set a realistic output ceiling. A lower max_tokens can reduce reserved demand on a token-based endpoint, but it cannot make an invalid request valid or remove a separate request-rate limit.
Request an increase only for a sustained, eligible need
If the workload consistently exceeds the account allocation, use the Service Quotas workflow or AWS Support process for the exact model and Region. Quota adjustability, capacity options, and approval limits vary by model and Region. Check the model’s lifecycle status before spending time on an increase request; a retiring or unavailable model may require migration instead.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Handle 503 and 529 as transient capacity conditions
A 503 ServiceUnavailable means the service is temporarily unable to handle a request because of high demand or a capacity constraint. A 529 overloaded_error similarly indicates temporary model-serving load. These signals differ from a 429 account quota error: immediately raising a quota does not cure a short-lived capacity event.
Implement bounded exponential backoff with jitter
- Retry only transient 503 or 529 responses, plus throttling responses when your policy deliberately treats them as retryable.
- If the response includes
Retry-After, wait at least that long before the next attempt. - Otherwise use exponential delays, for example a base delay multiplied by the attempt number, capped at a maximum, and add random jitter so clients do not retry together.
- Set a maximum attempt count or total retry time. Return a clear failure to the caller when the bound is reached.
- Do not retry non-idempotent work blindly if the application cannot tolerate duplicate effects; use request tracking or an application-level idempotency design where needed.
for attempt in range(max_attempts):
response = invoke(request)
if response.ok:
return response
if response.status not in (429, 503, 529):
raise response.error
wait = response.retry_after if response.retry_after else min(cap, base * (2 ** attempt))
sleep(wait + random_jitter())
raise TemporaryCapacityError()The code illustrates the control flow, not a model-specific SDK call. Use the retry and exception types supplied by your language SDK, and ensure that your client does not have a second retry layer that multiplies attempts.
Stop a retry storm
Retries can worsen overload when every worker retries at once. Put a concurrency limit and queue in front of invocation, use a circuit breaker after repeated failures, and let workers drain before increasing traffic. If failures persist after backoff, lower concurrency and compare results in another supported Region or with supported cross-Region inference. Do this only after confirming that the model, data-residency requirements, and workload are eligible for that route.
A repeatable end-to-end runbook
- Freeze the evidence. Save status, error type and message, request ID, UTC time, Region, endpoint, model or profile ID, API, and workload rates.
- Classify the signal. Use the error type rather than the status alone to choose authorization, validation, resource, quota, or capacity troubleshooting.
- Verify identity and policy for 403. Confirm the active principal, valid temporary credentials, allowed action, role trust, and restrictive policies.
- Reconcile catalog and routing for 404. Check the live model entry, exact identifier, endpoint, inference profile, Region, and account availability.
- Minimize and validate the payload for 400. Match the operation’s schema and the model’s supported request surface, then add optional fields gradually.
- Inspect the applicable quota for 429. Determine whether the call uses runtime or Mantle, identify the model and Region quota, and compare it with measured token, request, and concurrency demand.
- Shape or increase capacity. Smooth bursts and reduce demand; request a quota increase only when the model and Region support it and the need is sustained.
- Back off for 503 or 529. Honor server retry guidance, add jitter, bound attempts, and prevent synchronized retries.
- Validate the fix with a controlled load. Re-run a small request, then increase concurrency gradually while recording the same metrics.
- Escalate with a complete packet. Include endpoint, Region, model or inference-profile ID, status and error type, request IDs, UTC timestamps, token and request rates, concurrency, and the traffic ramp timeline.
Monitoring and escalation
CloudWatch exposes Amazon Bedrock metrics when the account and monitoring role have the required permissions. Use those metrics alongside application logs; a metric spike without the request’s model, endpoint, or Region is rarely enough to identify the limiting quota.
What to attach to an AWS Support case
- Several representative request IDs and their UTC timestamps.
- Exact endpoint, Region, model or inference-profile identifier, and invocation API.
- HTTP status, Bedrock error type, full message, and response headers.
- Input and output token rates, request rate, concurrency, queue depth, and
max_tokens. - A timeline showing when traffic increased and when errors began.
- The retry policy, attempt counts, and any concurrency controls.
- Whether the failure is limited to one model, Region, endpoint, account, or workload.
Redact prompts, credentials, authorization headers, and personal or confidential data before sharing logs. Retain the identifiers and timing information needed to correlate the failure.
What not to assume about GPT-6 in Bedrock
- A quota table entry is not an invocation identifier.
- A model visible in one Region is not automatically available in another.
- Support for one Bedrock endpoint or API does not establish support for every endpoint or API.
- Changing from one supported runtime inference API to another does not necessarily bypass a shared per-model runtime quota.
- A 503 or 529 is not proof that the account’s quota is too small.
- A 403 is not proof that adding broad IAM permissions is the correct fix.
Before deploying, resolve the current model ID, endpoint, API compatibility, and Region from the live catalog and account-specific documentation. Keep those values configurable so a model or Region change does not require rewriting the invocation code.
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