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Reliable LLM integrations treat throttling, timeouts, and provider outages as normal operating conditions. The practical pattern is to classify each error, retry only transient failures with one bounded controller, enforce both per-attempt timeouts and an end-to-end deadline, and replay a request only when duplicate side effects are impossible or protected by idempotency. Provider failover is a separate decision: the alternate route must be approved, healthy, compatible with your model features and data controls, and tested at recovery load. Circuit breakers, recovery-traffic caps, and gradual failback keep an incident from becoming a retry storm.
What should an LLM API reliability design do first?
Start by separating four decisions that are often incorrectly combined: whether the error is transient, whether another attempt fits the remaining deadline, whether replay is safe, and whether another provider is an approved substitute. A 429 caused by temporary throttling may merit a delayed retry; an exhausted credit balance does not. A timeout on a read-only classification can sometimes be replayed, while a tool-using agent that may have sent an email requires reconciliation instead of blind replay.
Apply the same policy at every layer. If an SDK retries twice, your application retries three times, and a gateway retries again, one user request can produce many upstream calls. Use one shared retry and failover budget, pass the remaining deadline downward, and make every layer report its attempt rather than independently deciding how long to wait.
Which limits can produce a 429 response?
Rate limits are multidimensional. A request can be below a requests-per-minute ceiling and still exceed a token, daily, model, project, or organization limit. Diagnose the dimension before changing traffic.
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OpenAI limit dimensions
OpenAI documents requests per minute (RPM), tokens per minute (TPM), requests per day (RPD), tokens per day (TPD), image requests per minute, and audio minutes per minute. Limits are applied at organization and project levels, vary by model, and can be shared by some model families. A 429 therefore needs the provider error code and response headers, not just the HTTP status.
Anthropic limit dimensions
Anthropic’s Messages API measures requests per minute (RPM), input tokens per minute (ITPM), and output tokens per minute (OTPM). Responses may include a retry-after value and separate request and token reset headers, allowing a client to wait for the specific exhausted bucket.
Headers to capture
For OpenAI calls, record Retry-After, x-ratelimit-limit-requests, x-ratelimit-remaining-requests, x-ratelimit-reset-requests, and the corresponding token-limit, remaining, and reset headers when present. Store the equivalent headers from other providers. Capture them on every failed attempt, because a later response can identify a different limiting dimension.
| Signal | What it can mean | Correct response |
|---|---|---|
| HTTP 429 with temporary rate-limit code | A request, token, or shared model bucket is temporarily exhausted | Honor the reset information or retry after bounded backoff with jitter |
| HTTP 429 with exhausted credits, spend limit, or organization-usage error | Account or billing remediation is required | Stop retries and alert an owner; repeated calls add load without fixing access |
| HTTP 503 or overload response | Provider capacity or a model instance is temporarily unavailable | Retry within the deadline, then use an approved alternate route if eligible |
| HTTP 408 or connection reset | The request did not complete in the transport path | Retry only if the operation is replay-safe and the deadline allows it |
OpenAI’s 2026 guidance gives a concrete ramp example: at traffic around 1 million input tokens per minute, increase traffic by no more than 50% every 15 minutes. The exact threshold varies by model, so treat this as a model-specific operating rule rather than a universal quota.
How do I decide whether an error is retryable?
Classify the HTTP status, provider error code, and headers together. Retry only a temporary condition whose cause can clear without changing the request.
Retry candidates
- Temporary 429 rate-limit or slow-down errors.
- 503 overload or unavailable responses.
- 408 responses, connection resets, and eligible transport timeouts.
- Transient gateway or network failures where the provider may not have accepted the request.
Do not retry automatically
- Malformed JSON, invalid parameters, unsupported model features, or an overlong context.
- 401 or 403 authentication and authorization failures.
- Safety refusals or policy blocks.
- Exhausted credits, quota, spend limits, or organization usage limits.
- Unknown errors when you cannot establish that the operation is safe and transient.
Unsuccessful requests can still consume per-minute capacity. Continuous resubmission can therefore extend the incident. Reduce avoidable token pressure by removing repeated prompt context and setting output-token allowances close to the expected response size; OpenAI notes that estimated token allowances contribute to rate-limit calculations.
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What retry algorithm should an LLM client use?
Use a single controller with a maximum attempt count, a total retry budget, and an absolute deadline. Prefer the provider’s wait instruction; otherwise use capped exponential backoff with random jitter.
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- Honor
Retry-After. Validate that it is a sensible duration or date. Wait at least that long; do not replace a longer provider instruction with a shorter local delay. - Calculate a fallback delay. If the header is absent or invalid, use exponential backoff such as an initial delay near one second, doubling to a configured cap, then add random jitter so workers do not wake together.
- Check the deadline before sleeping. If the remaining time cannot accommodate another attempt and its expected response window, stop rather than starting work that will certainly exceed the user’s deadline.
- Consume one shared budget. The budget covers SDK, gateway, application, and provider failover attempts. A downstream retry must not silently create a second budget.
- Record the attempt. Log the route, model, status, provider code, delay, elapsed time, token usage, and remaining deadline.
Google’s Gemini guidance recommends exponential backoff with jitter for transient 429, 408, and 5xx responses. Its official Python SDK retries transient errors up to four times, with an initial delay of approximately one second and a maximum delay of 60 seconds. If you use that SDK, account for those attempts in your application-wide budget instead of adding four more on top.
deadline = now() + workflow_timeout
attempt = 0
while attempt < max_attempts:
remaining = deadline - now()
if remaining <= minimum_attempt_window:
return deadline_exceeded()
response = call_provider(timeout=min(per_attempt_timeout, remaining))
if response.success:
return accept_once(response)
if not is_transient(response) or not replay_safe(request):
return handle_nonretryable(response)
delay = validated_retry_after(response) or jittered_exponential(attempt)
if now() + delay + minimum_attempt_window > deadline:
return deadline_exceeded()
sleep(delay)
attempt += 1
return retry_budget_exhausted()How should I set timeouts and deadlines?
Give each network attempt a short explicit timeout and give the logical operation a larger absolute deadline. The per-attempt timeout limits one connection; the deadline limits all retries, queueing, backoff, and failover together. Without the second value, a request can keep retrying long after the user or transaction has expired.
Choose the per-attempt timeout
Base it on the model, prompt size, streaming behavior, and the workflow’s latency objective. Leave room for connection setup, response transfer, and your own processing. A timeout should be long enough for a normal completion but short enough that another route remains useful before the workflow deadline.
Propagate remaining time
Pass an absolute deadline or remaining-duration field through application, gateway, and SDK layers. Each layer should subtract time already spent, including backoff. If only 1.5 seconds remain, a downstream component must not start a 10-second attempt.
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A client timeout means only that the client stopped waiting. The provider may have completed the request, may still be processing it, or may have executed a tool action before the connection failed. Treat the completion state as unknown until you can reconcile it.
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When is replay after a timeout safe?
Replay only self-contained, read-only work when the first attempt’s result and side effects are known to be unused. A deterministic text classification whose response was never consumed is a stronger replay candidate than an agent request that can modify a database, send a message, place an order, or call an external tool.
Use an explicit replay-safety classification
- Safe: read-only generation or analysis with no external writes, no tool calls, and no consumer that could act on a late result.
- Conditionally safe: an operation with an idempotency key, deduplication record, or provider-supported idempotency mechanism that prevents duplicate effects.
- Unsafe until reconciled: stateful sessions, tool-using agents, payment or fulfillment actions, or any request whose first attempt may have changed external state.
Accept exactly one result
Assign one logical request identifier to every attempt. If a later attempt succeeds, ignore a late response from an earlier attempt unless reconciliation shows that the earlier result is the authoritative one. Store the winning attempt and action-reconciliation state so downstream consumers cannot process two completions.
Reconcile uncertain actions
For a timed-out action, query the external system by the idempotency key or business identifier before issuing another command. If the system cannot report whether the action happened, pause for human review or use a compensating workflow. Do not “retry and hope” when a duplicate can cause financial, legal, or customer harm.
How do I fail over from one LLM provider to another?
Provider failover is a workflow decision, not a generic HTTP fallback. Route to a secondary only after the primary error is eligible, the alternate route passes pre-approved checks, and the request can be replayed safely.
Approve the secondary route before an incident
- Confirm the required model features: tool calling, structured output, vision, streaming, context length, and safety controls.
- Pin a compatible model version. Microsoft gateway guidance warns that round-robin or failover endpoints should use the same model at the same version; changing versions can create unexpected client behavior.
- Verify data residency, retention, logging, encryption, and contractual controls for the deployment region.
- Test credentials, network connectivity, DNS, proxy, gateway, and identity paths from every production region.
- Reserve enough capacity for recovery traffic and confirm the budget for potentially higher per-token or per-request cost.
- Run load and failure tests at the volume expected during a primary outage, not only a small health check.
Fail over only for eligible causes
Temporary throttling, overload, and qualifying transport failures can be candidates. Do not fail over automatically for malformed input, unsupported parameters, 401 or 403 errors, safety refusals, exhausted quota or credits, spend limits, or an unknown error. A malformed request usually fails at every provider and multiplies cost and noise.
Check shared dependencies
A common gateway, retrieval service, identity system, DNS provider, or network path can make both providers fail together. Test those dependencies before switching routes. If no secondary qualifies, queue the work, choose an approved reduced-functionality path, hand off to a human, or stop safely with an actionable error.
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Preserve semantic compatibility
Different providers can tokenize prompts differently, enforce different safety policies, format tool calls differently, or return different structured-output details. Keep provider-specific adapters, validate the output schema after failover, and avoid silently treating a different model’s answer as equivalent when the workflow depends on exact behavior.
How do circuit breakers prevent retry storms?
A circuit breaker stops sending new traffic to a route that is persistently throttling or timing out. Use separate thresholds by provider, model, region, and workflow so a failure in one route does not disable healthy traffic elsewhere.
Open the circuit on sustained failure
Trip after a sustained error-rate or latency threshold, not one isolated 429. While open, reject or queue new work locally and prevent synchronized retries. Microsoft’s gateway guidance specifically advises breaking the circuit for a model instance that continuously returns 429 instead of continuing to hit it.
Limit recovery traffic
When the alternate route receives failover traffic, cap that traffic as a percentage or fixed rate. Otherwise the secondary can become the next bottleneck. Keep a manual disable switch for operators who need to stop an unsafe or unexpectedly expensive route immediately.
Fail back gradually
Do not return all traffic to the primary as soon as one health check passes. Wait until error and latency measures remain within recovery thresholds, then send representative traffic in small increments. Pause the ramp immediately if either measure regresses.
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A single-provider design is simpler; a multi-provider design can reduce dependence on one control plane but adds compatibility, compliance, testing, and operational work. Compare the options against the workflow, not a generic availability claim.
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| Decision axis | Single provider with disciplined retries | Multi-provider failover |
|---|---|---|
| Replay safety | One provider simplifies request identity and reconciliation | Requires consistent idempotency and result arbitration across adapters |
| End-to-end deadline | More time remains for one route’s retries | Failover consumes additional connection and validation time |
| Model and feature compatibility | One API contract and version family | Adapters and contract tests are required for tools, schemas, streaming, and safety behavior |
| Data residency and logging | Fewer data paths to approve | Every provider and region needs separate controls and review |
| Recovery capacity | Depends on the provider’s spare capacity and quota increase process | Can spread load, but the secondary must be capacity-tested and funded |
| Cost | Usually simpler to forecast | Requires standby capacity, duplicated integration work, and possibly different prices |
| Observability | One set of metrics and error codes | Needs normalized metrics plus provider-specific diagnostics |
| Operational ownership | Fewer runbooks and contracts | More teams, credentials, policy reviews, and failover drills |
A cheaper secondary that has not been validated under load, is slower than the deadline allows, or cannot execute required tools is not a dependable fallback.
What should I log and measure?
Make every logical request traceable across attempts and providers. At minimum, record:
- Logical request ID and idempotency key, if used.
- Provider, model, model version, region, gateway route, and SDK version.
- HTTP status, provider error code, response classification, and relevant rate-limit headers.
- Attempt number, retry or failover reason, backoff delay, per-attempt elapsed time, and remaining deadline.
- Input and output token counts, estimated token allowance, and whether cached input was used when the provider exposes it.
- Circuit state, recovery-traffic bucket, and manual route overrides.
- Replay-safety classification, external action status, reconciliation result, and the single accepted result.
Build dashboards for error rate, p95 and p99 latency, timeout rate, remaining request and token capacity, retry amplification, failover volume, circuit-open duration, and cost by route. Alert on a rising retry-to-success ratio as well as raw failures; a service can appear healthy while wasting capacity on repeated attempts.
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How do I implement this as an end-to-end request flow?
The following sequence keeps classification, deadlines, replay safety, and failover in one place:
- Create a logical request ID and, for side-effecting work, an idempotency key tied to the business operation.
- Classify the request as read-only, deduplicated write, or unsafe-to-replay. Attach required model features, region, and data-control constraints.
- Set the absolute workflow deadline and a shorter per-attempt timeout. Pass the remaining time to the gateway and SDK.
- Send to the primary route while recording provider, model, version, and token budget.
- On success, validate the schema and accept exactly one result. Persist the winning attempt before triggering downstream work.
- On failure, classify status, provider code, and headers. Stop immediately for permanent errors.
- For an eligible transient error, honor
Retry-Afteror use capped jittered backoff. Recheck replay safety and remaining deadline before each attempt. - If the primary circuit is open or the shared retry budget is exhausted, evaluate the approved secondary against feature, region, data, capacity, and budget constraints.
- On failover, use the same logical request ID and idempotency key. Validate the alternate response with the same business rules and record the route change.
- If no route can complete safely, queue, reduce functionality, escalate to a human, or return a clear failure. Reconcile any uncertain external action before allowing a new command.
What maintenance keeps the policy reliable?
Re-test whenever an SDK, model, model version, region, gateway policy, prompt format, or authentication path changes. Reconfirm provider limits and error codes, run controlled 429 and timeout drills, verify circuit and failback behavior, and check that dashboards still capture new headers and token fields. A failover plan that has not been exercised under realistic load is a design document, not a recovery capability.
Frequently Asked Questions
How should streaming responses be retried?
Treat a stream that stops after emitting content as a partially delivered result, not as an ordinary failed request. Preserve the request ID and received tokens, then either resume through a provider-supported continuation mechanism or restart only when the consumer can discard the partial output and the operation has no side effects.
How can batch jobs avoid wasting quota during an outage?
Pause or slow the producer when retryable errors cross a threshold, and persist unfinished jobs in a queue with their next eligible attempt time. Give interactive traffic a separate budget so a large batch cannot consume all request or token capacity.
What is the safest way to test failover in production?
Use a controlled percentage of traffic or a dedicated tenant, inject provider errors at the gateway, and cap recovery volume. Verify schema compatibility, deadlines, billing, data controls, deduplication, and failback before expanding the test.
How do safety policies affect cross-provider fallback?
A secondary provider may refuse content that the primary would answer, or apply different tool and data-handling rules. Document those differences, expose a policy-specific outcome to the caller, and never route around a safety refusal merely to obtain a different answer.
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