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When an AI API fails, first identify whether the cause is transient or a request, credential, permission, or quota problem. Retry transient failures only within a bounded time and attempt budget; then route to a healthy, authorized deployment or a deliberately chosen fallback model. If no substitute meets the task’s requirements, return a controlled error or degraded result rather than silently sending the request to an unsuitable model.
Classify the failure before deciding what to do
An HTTP status is a useful clue, not a complete diagnosis. Check the provider’s error body and request logs as well as the status, since a rate limit, exhausted quota, malformed input, and service outage call for different responses.
| Signal | Likely interpretation | Recommended response |
|---|---|---|
| 400 or invalid argument | Malformed input, invalid parameters, or an unsupported request combination. | Do not retry the unchanged request. Correct the request or select a compatible model. |
| 401 | Authentication failure. | Check credentials and authentication configuration. A different route helps only if its credentials are valid. |
| 403 | Permission or policy denial. | Check access, organization policy, and enabled API permissions. Do not route around a restriction. |
| 429 RESOURCE_EXHAUSTED | Rate limit, quota exhaustion, or shared-capacity overload, depending on the service and error details. | Follow provider retry guidance and use bounded backoff. Consider another authorized deployment only if it has usable quota and the request remains within policy. |
| 408, timeout, 503 UNAVAILABLE, or other 5xx | A timeout or server-side problem may be temporary. Google Cloud describes 503 as temporary unavailability and 504 as a deadline-exceeded condition. | Retry briefly within the end-to-end deadline. If a suitable independent route exists, fail over after the retry budget is used. |
Google’s Gemini API troubleshooting guide recommends retrying certain transient errors, not client errors such as 400, 402, or 403. Google Cloud’s Agent Platform API error guidance distinguishes authentication, permission, quota or overload, unavailability, and deadline errors. Exact meanings and retry instructions can vary by API, so use the documentation for the service you call.
Retry transient errors without creating a retry storm
Retries can absorb brief overloads and interruptions, but every retry adds latency and traffic. Set both a maximum attempt count and an overall deadline for the operation. Use exponential backoff with jitter so that many clients do not retry together in synchronized bursts. Stop when the caller’s deadline is reached, even if another retry would otherwise be allowed.
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Google’s Gemini guide gives example waits of 1, 2, 4, and 8 seconds. It describes the Python SDK as retrying transient failures up to four times, with an approximately one-second initial delay and a 60-second maximum. These are documented examples and SDK behavior, not universal settings for every provider or application.
Google Cloud’s Agent Platform guidance says to retry no more than two times for the errors it describes, with at least a one-second delay and subsequent exponential increases. Treat that as provider-specific advice. Avoid stacking independent retries in an SDK, application, and gateway: the combined attempts can exceed your intended budget. Choose which layer owns retries and make the total attempts and deadline explicit.
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Choose between another deployment and another model
These are separate failover layers. Another deployment can preserve the same model while moving traffic to different capacity; a different model or provider can offer an alternate route when equivalent capacity is unavailable. Neither route is automatically safe or equivalent.
Same-model deployment redundancy
Put multiple deployments behind a stable model alias or group, preferably across independent failure domains such as regions or providers. A gateway can try another deployment in that group after one fails. This is most useful when you want to preserve model behavior and the alternative deployment has capacity, authorization, and compatible configuration.
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Cross-model or cross-provider fallback
Configure a deliberate order of fallback groups for a distinct model or provider. Before enabling it, verify the substitute supports the request’s required modalities, structured output, tool use, and context length. Also assess latency, privacy and residency commitments, contractual data handling, safety policy, price, quota, and rate-limit headroom. A successful response only means the alternate served the request; it does not prove that its output is interchangeable with the primary model. If behavior may materially differ, communicate that to users or downstream systems.
Fail fast or degrade gracefully
Some work should not be sent to a different model. Depending on the application, a cached response, partial result, queued job, or human review may be safer than a substitute with unsuitable capabilities or policy characteristics. Treat this as an application decision, not an automatic guarantee of a routing gateway.
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Build routing with explicit limits and recovery behavior
A routing layer can manage deployment selection, retries, and cross-group fallback in one place, but its behavior is implementation-specific. LiteLLM’s router documentation describes same-group retries, cross-group fallbacks, cooldowns, and error-specific retry policies. It also documents weighted failover for its simple-shuffle strategy, exclusion of failed deployments within a request chain, and a default maximum fallback count of five. Those are LiteLLM-specific features and defaults; verify the version and configuration actually deployed before relying on them.
LiteLLM’s request-flow documentation distinguishes retries within a group from fallback to another group. Keep the distinction visible in your own configuration and telemetry: retrying the same model on another deployment is different from changing the model or provider.
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- Define the eligible routes. Record which deployments and fallback groups may handle each request, including the authorization, region, privacy, and policy constraints that apply.
- Set one retry owner. Decide whether the application, SDK, or gateway controls retries. Configure a maximum attempt count, backoff with jitter, and an end-to-end deadline.
- Specify failover conditions. Retry only transient categories. Route to another deployment or model only for the errors and request types where that route is an acceptable substitute.
- Prevent repeated traffic to unhealthy routes. Use a circuit breaker or cooldown so a failing deployment is not selected on every request. Configure recovery or reintroduction behavior and validate it in the gateway you use.
- Define the exhausted-budget outcome. Choose a controlled error or an application-level degraded result. For multi-step agent workflows, persist step state and avoid replaying side effects just because a model call was retried.
- Test failures deliberately. Exercise timeouts, rate limits, server errors, invalid requests, and fallback failures. Verify that attempts stop at the deadline and that the application records the actual route and final outcome.
Monitor whether failover is helping
Record the error category, selected deployment and model, retry count, latency, and whether fallback succeeded. These details help distinguish a provider incident from a bad request, exhausted quota, or a fallback that is too slow or unsuitable. Alert on persistent rate limiting and repeated fallback rather than treating endless retries as recovery.
When both the primary and fallback fail, return the defined controlled outcome, record the incident, and preserve enough request or workflow context for safe resumption. Do not automatically replay an operation that may already have triggered an external side effect.
Quick Recap
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