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To survive 429 Too Many Requests and temporary overloads, your client needs a bounded, provider-aware retry policy—not a loop that keeps firing requests. Classify errors using HTTP status and provider code, honor a valid Retry-After header, otherwise apply capped exponential backoff with jitter, and stop on an attempt or time budget. Smooth concurrency so work does not arrive in bursts, and treat streamed responses differently once output has started. This approach works across OpenAI, Anthropic, Gemini and Amazon Bedrock while leaving room for each service’s quota model and SDK behavior.
What causes 429 and overload errors in LLM APIs?
A 429 means the service is refusing the request for now, but the reason can differ. A provider may be enforcing requests-per-minute, tokens-per-minute, daily quotas, an acceleration limit after a sudden traffic increase, or a temporary capacity safeguard. A 503 or a provider-specific overload code usually indicates serving capacity rather than your account’s quota. Your retry decision must preserve that distinction because quota and billing problems will not be fixed by waiting.
OpenAI documents several independent limit dimensions, including requests per minute (RPM), requests per day (RPD), tokens per minute (TPM) and tokens per day (TPD). It separates 429 rate_limit_error and slow_down from 503 service_unavailable_error and server_is_overloaded; a slow_down response can occur even when published RPM and TPM values have not been exceeded. Anthropic uses 429 rate_limit_error for rate limiting and 529 overloaded_error for temporary overload, and warns that sharp usage increases can trigger acceleration limits. Google Gemini commonly reports 429 RESOURCE_EXHAUSTED and 503 UNAVAILABLE. Amazon Bedrock distinguishes 429 ThrottlingException from 529 overloaded_error.
Because unsuccessful requests still count toward OpenAI per-minute limits, an unbounded retry loop can make the original throttling last longer. The same traffic-amplification problem exists with any provider: every retry consumes connection, quota and server capacity.
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How should a client decide whether an error is retryable?
Build one classifier in your application and feed it normalized fields from every provider adapter. Retry only conditions documented as transient; return permanent failures immediately with an actionable error.
Capture the fields that matter
- HTTP status, provider error type and provider code.
- Request or correlation ID returned by the service.
- Model, region and endpoint used.
- Whether a response body or stream has already started.
- Attempt number, elapsed time and the selected delay.
Typical retryable conditions
429rate limits, throttling and acceleration limits.408request timeouts when the operation can safely be repeated.- Transient
5xxresponses such as500or503. - Documented overload codes such as Anthropic
529 overloaded_errorand Bedrock529 overloaded_error. - Connection resets or temporary DNS and transport failures, provided no non-idempotent side effect has been committed.
Permanent conditions that should not be retried
- Invalid authentication or authorization.
- Malformed JSON, unsupported parameters, invalid model names or context-length violations.
- Billing, credit, spend-limit and usage-limit failures. OpenAI explicitly directs customers to correct the account or quota rather than repeat the request.
- Policy or content errors that will produce the same result for identical input.
Keep the original provider error alongside your normalized category. Operators need the vendor code to diagnose a quota issue, while application code needs a stable category such as retryable_rate_limit, retryable_overload or permanent_client_error.
What is the correct backoff algorithm for 429s?
First honor a valid Retry-After value. If it is absent, malformed or unreasonably large for your deadline, use capped exponential backoff with jitter. Never let the delay or total operation time grow without a bound.
Step-by-step policy
- Classify. Parse status and provider code before deciding to retry.
- Read the server hint. A numeric
Retry-Afteris a delay in seconds; an HTTP-date value is a time at which retrying is allowed. Sleep at least that long, then add only a bounded amount of random jitter. - Calculate a fallback. A portable schedule is
delay = min(cap, base × 2^attempt) + jitter. Selectbase,capand jitter limits from measurements of your workload rather than treating any one number as universal. - Check the deadline. If the delay would exceed the remaining operation budget, stop and return a timeout-classified failure.
- Sleep cancellably. Propagate request cancellation, process shutdown and user deadlines while waiting.
- Retry once. Rebuild or reuse the request according to your SDK’s rules, increment the attempt counter and record the decision.
Full jitter chooses a random delay between zero and the exponential ceiling; equal jitter keeps half the ceiling and randomizes the other half. Either prevents synchronized clients from waking together. A fixed sleep, or exponential delay without randomness, can create a thundering herd when many workers fail at once.
Example pseudocode
attempt = 0
start = monotonic_time()
while true:
result = call_provider(request)
if result.success:
return result
category = classify(result)
if not category.retryable:
raise result.error
if attempt >= MAX_ATTEMPTS:
raise RetryExhausted(result.error)
remaining = deadline - monotonic_time()
delay = retry_after_seconds(result.headers)
if delay is None:
ceiling = min(CAP, BASE * (2 ** attempt))
delay = random_between(0, ceiling) # full jitter
if delay >= remaining:
raise DeadlineExceeded(result.error)
sleep_cancellable(delay)
attempt += 1The loop is intentionally bounded by both attempts and elapsed time. An attempt limit protects a queue from pathological responses; a deadline protects interactive requests when individual delays are large.
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How do OpenAI, Anthropic, Gemini and Bedrock differ?
The algorithm is portable, but provider codes, SDK defaults and failover options differ. Use the following distinctions when writing adapters and runbooks.
| Provider | Rate-limit and overload signals | Documented retry guidance | Operational detail |
|---|---|---|---|
| OpenAI | 429 rate_limit_error, slow_down; 503 service_unavailable_error, server_is_overloaded |
Honor Retry-After when present, reduce traffic, then ramp gradually |
Limits can include RPM, RPD, TPM and TPD. Official SDKs retry eligible 429 and 503 responses subject to settings. Unsuccessful requests count toward per-minute limits. |
| Anthropic | 429 rate_limit_error; 529 overloaded_error |
Use gradual, consistent ramping after sharp usage increases; honor retry-after |
Official SDK retries transient failures with exponential backoff twice by default. |
| Google Gemini | 429 RESOURCE_EXHAUSTED; 503 UNAVAILABLE |
Exponential backoff with jitter and a maximum retry count | Python SDK documentation describes automatic retries for transient failures. |
| Amazon Bedrock | 429 ThrottlingException; 529 overloaded_error |
Exponential backoff with random jitter; honor Retry-After |
Cross-Region inference can provide another serving-capacity path where supported. |
Do not infer that a provider’s automatic retry policy covers your whole operation. SDK retries usually cover one HTTP call; your application may also retry a queue job, tool invocation or workflow. Include SDK attempts in a single end-to-end budget, or disable one layer to avoid multiplicative traffic.
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Retries are only one part of load control. Smooth the traffic that reaches the API and ensure every worker shares a budget.
Control concurrency
Use a semaphore or worker pool sized to your measured quota and latency. When throttling rises, reduce permits; restore them gradually after a quiet period. A token-bucket or leaky-bucket limiter can enforce request and token budgets before calls leave your process.
Queue and coalesce work
Put non-interactive jobs on a durable queue with visibility timeouts and a dead-letter path. Coalesce duplicate prompts, cache deterministic results where appropriate, and prioritize user-facing work over batch refreshes. A queue absorbs short outages without causing every caller to retry simultaneously.
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Ramp deployments gradually
Anthropic’s acceleration-limit warning and OpenAI’s gradual-ramp advice apply to new models, regions and large releases. Increase concurrency in steps, watch error rates and token usage, and pause the ramp when throttling or overload codes rise.
Use regional alternatives carefully
Bedrock documents cross-Region inference for supported configurations. A region switch is not a license to multiply traffic: preserve the same global concurrency and token budgets, and record the selected region so operators can correlate behavior.
What changes when an LLM response is streamed?
Before any bytes or events arrive, normal HTTP retry handling applies. Once a 200 response and an SSE or other stream have begun, replaying the entire prompt can duplicate work or side effects.
Before the stream starts
A connection failure, timeout or retryable status before headers are received can usually be retried under the same classifier, subject to idempotency and the operation deadline.
After output has started
Providers may send an error event inside the stream. Preserve the partial output and event metadata, then decide whether to ask the user or application to continue. Do not automatically replay a request that may have triggered tools, writes or payments unless the operation is explicitly idempotent and duplicate effects are acceptable.
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Design resumable workflows
Separate generation from side effects: have the model produce a proposed action, validate it, then commit it through an idempotent operation with a unique key. If a stream ends early, you can resume generation or show partial text without repeating the committed action.
How should SDK retries be configured?
Inspect the SDK’s current defaults, retryable status list, maximum attempts, jitter behavior and timeout interaction. Then choose one owner for the overall policy.
- Single-layer policy: let the SDK handle transport-level transient errors and keep application code responsible for deadlines, queue redelivery and idempotency.
- Application-owned policy: disable or minimize SDK retries when you need one global budget across multiple providers, tools or workflow steps.
- Combined policy: permit a small SDK retry count, but subtract those attempts from the application’s remaining budget and log both layers.
Anthropic states that its official SDK retries transient failures twice by default and honors retry-after. Gemini SDKs document automatic transient-error retries, and OpenAI says its official SDKs retry eligible 429 and 503 responses subject to settings. Defaults can change with SDK versions, so pin versions, review release notes and test the effective behavior rather than assuming the documentation for another language applies unchanged.
What should observability record?
Every retry decision should be explainable from logs and metrics. Record:
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- HTTP status, provider type/code and request ID.
- Whether
Retry-Afterwas present, parsed and applied. - Attempt number, SDK-attempt count, selected delay and actual sleep.
- Input and output token counts when available, plus the quota dimension implicated.
- Stream state: no response, headers received, partial output or completed output.
- Final outcome, total elapsed time and cancellation reason.
Aggregate retry rate, throttling by model and region, queue age, deadline expirations and token consumption. Alert on sustained increases, not on a single 429, and sample prompt content carefully to avoid logging sensitive data.
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How do I test a retry implementation safely?
Inject deterministic failures instead of waiting for a production outage. Your test harness should return each provider’s representative status and code, malformed and valid Retry-After values, connection resets, stream errors before headers and stream errors after partial output.
- Verify permanent 4xx errors make exactly one provider call.
- Verify a valid
Retry-Afteris treated as a minimum delay and that cancellation interrupts the sleep. - Verify exponential delays are capped and jitter stays inside its bound.
- Verify the operation stops at both maximum attempts and the overall deadline.
- Verify nested SDK and application retries cannot exceed the configured global budget.
- Verify idempotency keys prevent duplicate side effects when a response is lost after the server committed.
Load-test the queue and limiter with synchronized failures. Measure your own success rate and latency under realistic concurrency; there is no provider-neutral success or latency percentage that can be safely substituted for those measurements.
Which retry policy should I choose for interactive and batch workloads?
Interactive requests need a short deadline and a small attempt budget; batch work can wait longer but must protect the queue from starvation.
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| Workload | Policy emphasis | Failure handling |
|---|---|---|
| Interactive chat or completion | Strict end-to-end deadline, cancellable sleeps, low concurrency per user | Return a clear temporary-unavailable response when the deadline is reached; preserve partial stream output. |
| Background indexing or summarization | Durable queue, adaptive concurrency, token-aware rate limiter | Redeliver with bounded attempts, then move the job to a dead-letter queue for inspection. |
| Tool or transactional workflow | Explicit idempotency key, side-effect isolation, provider and region logging | Never replay after an uncertain commit without checking operation status or using an idempotent API. |
What is the practical checklist for production?
- Normalize status and provider codes in one classifier.
- Retry only documented transient errors.
- Honor valid
Retry-After; otherwise use capped exponential backoff with jitter. - Bound attempts, elapsed time and total concurrency.
- Count SDK retries inside the application budget.
- Use queues and gradual ramping instead of synchronized client loops.
- Handle pre-stream and post-stream failures differently.
- Protect side effects with idempotency and unique operation keys.
- Log request IDs, quota signals, delays, model and region.
- Test every provider code path with injected failures before release.
Frequently Asked Questions
Should I retry a 429 immediately if no Retry-After header is returned?
No. Treat the missing header as a signal to use your capped exponential schedule with jitter, and check the remaining deadline before sleeping. An immediate replay can consume more quota and extend the throttling window.
Can I switch models when an API keeps returning overload errors?
You can implement a fallback only when the alternative model, region and data handling meet your product requirements. Apply the same global concurrency and token budgets to the fallback, and log the switch so cost, quality and latency changes remain visible.
How do I avoid duplicate tool calls after a network timeout?
Assign each side-effecting operation a unique idempotency key and have the tool layer return the prior result when that key is seen again. If the tool cannot provide idempotency, require an explicit status check or human confirmation before replaying.
What does a 529 mean for an LLM request?
It is a provider-specific overload signal rather than a universal HTTP standard for LLMs. Anthropic and Amazon Bedrock document 529 overload codes, so classify the provider code along with the status and apply the same bounded, jittered policy used for other transient overloads.
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