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Identify the failure before retrying
Every Claude API error should be handled from its structured response, not from a string match in the message. Anthropic’s error envelope contains a top-level error object with a type and message; responses also provide a request_id that is valuable when you need support. Official SDKs expose typed exceptions, so catch those exception classes instead of testing whether a message happens to contain a word such as “overloaded.”
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| Signal | What it usually means | Correct first action |
|---|---|---|
429, rate_limit_error, with retry-after |
A temporary request-rate or capacity limit | Honor the delay, then retry with capped exponential backoff and jitter while lowering concurrency. |
429 without retry-after, with spending-cap context |
A monthly usage-tier cap or Claude Code workspace spending limit | Stop blind retries and check usage, billing, and account limits. |
529, overloaded_error |
Anthropic’s service is temporarily overloaded | Queue the work and retry with bounded exponential backoff and jitter. |
500, api_error |
An unexpected internal API failure | Retry with exponential backoff; retain the request_id if failures persist. |
504, timeout_error |
Processing exceeded the request’s time window | Consider Messages API streaming for long responses and review request size and workflow. |
| Bedrock model, inference-profile, or payload error | A provider-path configuration or request-size problem | Use the required inference profile and keep the request below Bedrock’s 20 MB maximum payload. |
| Deprecated or retired model ID | The selected model is less reliable or no longer supported | Migrate to Anthropic’s documented active replacement instead of retrying. |
Capture a complete failure record
Make the failed request diagnosable before changing behavior. Log these fields in a privacy-safe form:
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error.type - The complete
error.message, with secrets and user content redacted request_id, model ID, endpoint, and API or provider path- Attempt number, start and end timestamps, and the selected retry delay
- Input and output token counts, when returned, plus the limits your account reports
- Queue depth, in-flight request count, and the service or worker that made the call
Record the response headers as well, especially retry-after. Do not log API keys, authorization headers, or entire prompts by default. A request ID that is retained with the status and model lets you distinguish a provider incident from a local retry storm.
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Apply a safe retry policy
Retry only transient conditions
Retry 529 responses and transient 500 responses. A 429 is retryable only when it represents a temporary request-rate or capacity limit. If the response has no retry-after and your account is at a spending cap, repeated attempts will not release the cap. Treat that case as a hard stop for the queue and alert an operator or billing owner.
A 504 is a timeout, not proof that the request was rejected before processing. Decide whether your operation is safe to replay, and make that decision explicit in your application. For long generations, switch to Messages API streaming so the response can be consumed as it arrives rather than waiting for one large completion.
Honor the provider’s delay, then add bounded backoff
- Parse
retry-afterwhen it is present and wait at least that long. - If it is absent on a retryable error, calculate an exponential delay from the attempt number.
- Add random jitter so many workers do not wake at the same instant.
- Cap both the per-attempt delay and the total retry window in configuration.
- After the cap is reached, move the job to a durable retry queue or a dead-letter path rather than looping in the request thread.
The delay should be selected once per attempt and recorded in your logs. Exponential backoff without jitter can synchronize a fleet into repeated bursts; unlimited backoff can leave user requests hanging indefinitely.
Use typed SDK errors and preserve request identity
Configure your SDK’s exception handling for the documented status and error types. Do not classify an error by searching the human-readable message. Keep the original request_id on every retry record and attach a separate application job ID so operators can see whether several provider requests belong to one logical operation.
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for (let attempt = 0; attempt < retryPolicy.maxAttempts; attempt += 1) {
try {
return await callClaude(request);
} catch (error) {
const status = error.status;
const type = error.error?.type;
const retryAfter = error.headers?.get("retry-after");
if (status === 429 && !retryAfter && isSpendingCap(error)) {
throw new SpendingCapError(error);
}
if (![429, 500, 529].includes(status)) {
throw error;
}
const delay = retryAfter
? parseRetryAfter(retryAfter)
: boundedExponentialDelay(attempt, retryPolicy);
await sleep(addJitter(delay, retryPolicy.jitter));
}
}
throw new RetryWindowExceededError();The example deliberately leaves the policy values in configuration. Choose limits according to your user-facing latency target, job priority, and provider terms; do not copy a delay that was tuned for a different workload.
Reduce pressure at the caller
Limit concurrency across the whole application
A limiter inside one process does not protect you when several web workers, cron jobs, and background consumers share an organization limit. Put the concurrency budget in a shared queue or centralized worker pool, and count both interactive and batch traffic. Use separate reservations for latency-sensitive work if batch jobs otherwise consume every available slot.
- Set a maximum number of in-flight Claude requests for each workload.
- Make workers acquire a permit before sending a request and release it on every success, failure, timeout, or cancellation.
- Reduce permits when 429 or 529 rates rise, then restore them gradually after a quiet period.
- Bound queue age and return a clear “temporarily busy” result instead of allowing unbounded memory growth.
Queue bursts instead of launching them together
Batch imports, scheduled jobs, and fan-out requests often create a synchronized spike. Place those jobs in a durable queue, drain at a controlled rate, and spread scheduled work over time. A queue also gives you a place to pause work when a spending cap is reached, rather than generating thousands of guaranteed failures.
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Request and response tokens both contribute to pressure and cost. Set an application-level output-token ceiling appropriate to each task, truncate or summarize oversized context, and avoid sending the same large retrieval set to every parallel request. A smaller prompt can reduce processing time and make a 504 less likely, but it does not override an account’s published request or spending limits.
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Prevent retry storms
Retries must consume the same shared concurrency budget as first attempts. Otherwise, every failed request creates another request while the original wave is still active. Add jitter, cap attempts, and use a circuit breaker that pauses new calls after a sustained run of 529 or 500 responses. Let health checks close the circuit gradually rather than releasing the entire backlog at once.
Check organization, tier, and workspace limits
Open the Anthropic console and compare the failing workload with the limits shown for your organization. A 429 can represent three different controls:
- An organization request or capacity limit.
- A monthly usage-tier spending cap.
- A Claude Code workspace spending limit.
Check current request and token consumption, the account’s usage tier, monthly spend cap, and any workspace-specific limit. Limits vary by organization, so a value copied from another account is not a reliable target. If the response is a spending-cap 429, changing the retry interval cannot make the request succeed; access resumes only when the account’s limit or billing state permits it.
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Alert on approaching limits, not only on rejected requests. Useful signals include requests per minute, input and output tokens, concurrent calls, 429s by error subtype, 529s, retry volume, queue age, and the percentage of work abandoned after the retry window.
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Choose where to enforce controls
For a single small service, an in-process limiter may be enough. For several services or environments sharing one organization, a gateway centralizes controls and makes the limits visible to every caller.
| Control location | Strengths | Limitations |
|---|---|---|
| Client or service process | Fast to add; can apply task-specific token budgets, queues, and user-facing timeouts. | Each process sees only its own traffic unless you add shared coordination; separate workers can still exceed the organization limit together. |
| Central gateway | Centralizes authentication, usage tracking, budgets, rate limits, audit logs, and model routing across services. | Adds an operational component and requires careful security, availability, and failure-mode review. |
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Keep a local timeout and fallback even when a gateway is present. A gateway cannot fix a provider spending cap, and it should not turn a provider outage into an unbounded internal queue.
Diagnose the Bedrock path separately
Requests made through Amazon Bedrock have provider-path rules in addition to Anthropic’s model behavior. Confirm that the selected model is available in the account and region, and use the inference profile required for that model. Choose a global or regional endpoint according to availability and your data-residency requirements.
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Keep the complete request payload below Bedrock’s 20 MB maximum. Measure the serialized payload, not only the text length: messages, images, tool definitions, and other JSON fields all count toward the limit. A payload-size failure will not be repaired by retrying the same body; reduce or restructure the request first.
| Decision | Direct Anthropic API | Amazon Bedrock path |
|---|---|---|
| Endpoint and routing | Anthropic’s API endpoint and its model availability rules. | A Bedrock endpoint with global or regional routing selected for availability and data-residency needs. |
| Authentication and limits | Anthropic organization credentials and limits. | AWS/Bedrock access controls plus Bedrock quotas and inference-profile requirements. |
| Payload ceiling | Use the limits returned for the Anthropic API request. | 20 MB maximum request payload. |
| Model lifecycle | Follow Anthropic’s active model IDs and retirement dates. | Confirm that the model and inference profile are offered in the chosen Bedrock region or route. |
Verify the model ID and lifecycle state
Retry logic cannot revive a retired model. Anthropic states that deprecated models are likely to be less reliable than active models and publishes retirement dates and replacements. When failures consistently identify a deprecated or retired model, update the model ID, test the replacement’s output and token behavior, and deploy the change through your normal release process.
Keep model IDs in configuration rather than scattering them through source code. Monitor deprecation notices, maintain a tested fallback model where your product can tolerate one, and record the selected model in every request log so a lifecycle migration is visible in your metrics.
Build a production runbook
- Classify: capture status, structured type, message, headers, request ID, endpoint, model, and usage counters.
- Protect: stop retries for a spending-cap 429; pause or shed low-priority work when the queue or circuit-breaker threshold is reached.
- Retry: honor
retry-after; otherwise use bounded exponential backoff with jitter for 529 and transient 500 responses. - De-pressure: reduce shared concurrency, spread queued work, cap output tokens, and remove redundant context.
- Check account state: verify organization tier, monthly cap, workspace limit, and current usage in the console.
- Check the path: for Bedrock, verify model availability, inference profile, endpoint choice, and the 20 MB payload ceiling.
- Check the model: replace deprecated or retired IDs with the documented active replacement.
- Escalate with evidence: provide persistent failures with request IDs, timestamps, statuses, model IDs, and a redacted description of the workload.
Test the failure modes before production
Exercise each branch in a staging environment with a mock or controlled failure response. Confirm that a temporary 429 honors its delay, a spending-cap 429 stops the queue, a 529 uses jittered backoff, a 500 preserves the request ID, and a 504 follows the streaming or timeout path. Test several workers at once; a limiter that works in one process can still fail when deployed across a fleet.
Verify that cancellation releases concurrency permits, dead-lettered jobs are visible, retries do not duplicate a non-idempotent side effect, and logs redact credentials and sensitive prompt content. For streamed responses, test disconnects and partial output so a client retry cannot accidentally apply the same downstream action twice.
Quick Recap
What a healthy implementation looks like
- Every response is classified by status and structured error type.
- Retryable failures use provider-directed delays, bounded exponential backoff, and jitter.
- Spending-cap failures stop quickly and produce an account-level alert.
- Concurrency and queues are shared across services that use the same organization.
- Token budgets, queue age, retry counts, and request IDs are observable.
- Bedrock requests meet inference-profile, endpoint, residency, and 20 MB payload requirements.
- Model IDs are monitored against deprecation and retirement dates.
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