When an AI API times out, returns a server error, or rate-limits your application, classify the failure before deciding what to do next. Retry transient failures briefly within a fixed deadline; correct invalid requests and access problems; and route to another deployment or model only when it is authorized and suitable for the task. If no safe route is available, return a controlled degraded result instead of retrying indefinitely.
First identify what failed
An error response is not automatically an outage. Its category helps distinguish a temporary capacity or service problem from a request your application must fix. Google’s error guidance separates authentication, permission, quota or overload, unavailability, and deadline errors; its Gemini troubleshooting guide also distinguishes retryable transient failures from client errors.
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| Signal | What it can mean | First response |
|---|---|---|
| 400 or invalid argument | Malformed input or unsupported request parameters. | Fix the request or its compatibility; do not retry the unchanged request. Google Gemini troubleshooting guide and Google Cloud error guidance. |
| 401 or 403 | Authentication failure or insufficient permission. | Check credentials, access, organization policy, and API permissions. A different route helps only if it is authorized and policy-compliant. Google Cloud error guidance. |
| 429 RESOURCE_EXHAUSTED | Quota, rate limiting, or shared-capacity overload. | Follow provider retry instructions and use bounded backoff. If a suitable alternate deployment has headroom and is permitted, consider routing there; persistent errors need investigation, not endless retries. Google Gemini troubleshooting guide and Google Cloud error guidance. |
| 408, timeout, 5xx, or 503 UNAVAILABLE | A request timeout or server-side problem; 503 is identified as temporary unavailability in Google Cloud’s guidance. | Retry briefly within the end-to-end deadline, then use a suitable independent route if one exists. For a 504 deadline exceeded response, also check whether the caller’s deadline is too short. Google Cloud error guidance. |
These codes are useful examples, not a universal contract for every AI provider. Read the provider’s current API documentation and inspect its response details; the same numeric code can have provider-specific context.
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Retries are for failures that may clear on their own, such as a transient server error, timeout, or rate limit with retry guidance. They are not a repair strategy for malformed input, bad credentials, missing permissions, or exhausted quota that will not recover within the request’s useful lifetime.
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Bound attempts by both count and deadline
Set a maximum attempt count and an overall request deadline. Exponential backoff spaces retries progressively; jitter adds randomness so many clients do not retry at the same instant and create another burst. Google’s Gemini guide gives 1, 2, 4, and 8 seconds as example waits. It describes its Python SDK as retrying transient errors up to four times, with roughly a one-second initial delay and a 60-second maximum. Those are documented examples for that SDK and guidance, not universal settings.
Provider recommendations can differ. Google Cloud’s Agent Platform guidance says to retry no more than two times for the described errors, with at least a one-second delay and subsequent exponential increases. Apply the instructions for the API and operation you actually use rather than combining retry counts from unrelated examples.
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Avoid stacked or uncoordinated retries
If an SDK, gateway, and application each retry independently, one user request can produce many upstream attempts and exceed its deadline. Choose a clear retry owner or coordinate the layers so their combined attempt count and timing are bounded. Stop when the caller’s deadline is reached, even if another retry would otherwise be allowed.
Choose the right recovery route
Try another deployment of the same model
When the model itself is still suitable but one endpoint or region is unhealthy or constrained, a second deployment can provide redundancy. Keep a stable model alias or group and configure more than one deployment behind it; separate failure domains, such as regions or providers, are more useful than replicas that share the same bottleneck. LiteLLM documents routing retries among deployments in a group in its router documentation and distinguishes same-group retries from cross-group fallback in its request lifecycle documentation.
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Fall back to a different model or provider
A fallback model is a different route, not automatically an equivalent substitute. Configure an ordered fallback group only if the alternate can satisfy the application’s requirements. A successful HTTP response can still be a failure for your product if the model cannot use a required tool, follow a required output schema, process the input modality, fit the context, or meet the latency, privacy, residency, safety, or budget constraints.
For each candidate route, assess:
- Capability and compatibility: modalities, structured output, tool use, and output expectations.
- Limits and timing: context and input limits, rate-limit headroom, and the remaining end-to-end latency budget.
- Data and policy: privacy, geographic residency, contractual handling, and safety requirements.
- Cost and operations: price, quota, observability, retry control, and the complexity of maintaining another route.
If the alternate may behave materially differently, communicate that to users or downstream systems rather than silently presenting it as identical.
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Degrade gracefully instead of switching
Some requests should not be sent to a less capable or less appropriate model. Depending on the task, a cached answer, partial result, queued job, or human review may be safer. This is an application-level decision: a routing gateway cannot determine whether a substitute is acceptable for your users.
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Keep retry and fallback behavior explicit. LiteLLM’s router documentation describes weighted failover for its simple-shuffle strategy, retries that exclude failed deployments within a request chain, cooldowns for unhealthy deployments, and a default maximum fallback count of five. These are LiteLLM-specific features and defaults; verify the version and configuration you deploy rather than treating them as general gateway behavior.
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- Define the eligible routes. Group deployments that can serve the same task, and list distinct fallback groups only where their behavior and data handling are acceptable.
- Classify errors. Retry only the transient categories allowed by the provider’s guidance. Treat invalid input and authorization failures as errors to correct, not reasons to walk through every model.
- Set one combined budget. Configure maximum attempts, fallback count, backoff, and an end-to-end deadline so retries cannot grow without bound across layers.
- Exclude failed routes in the current chain. Otherwise a retry or fallback can cycle back to the same unavailable deployment instead of making progress.
- Use cooldowns and deliberate recovery. Stop routing repeatedly to a failing deployment for a period, then reintroduce it according to an explicit recovery policy. Cooldown behavior and reintroduction controls depend on the gateway.
- Record outcomes and test failure paths. Track error category, chosen deployment or model, retry count, latency, and fallback outcome. Test timeouts, quota errors, unavailable deployments, and failure of the fallback itself.
Protect multi-step workflows and data guarantees
Model calls can sit inside workflows that also write records, send messages, or trigger other side effects. A retry of the model call must not accidentally replay those actions. Persist workflow step state, distinguish a repeated inference request from a repeated business operation, and make side effects idempotent or otherwise safe to resume.
Failover also changes where data may be processed. Before enabling another provider, region, or model, confirm its privacy, residency, and contractual handling meet the same requirements as the primary route. Routing availability is not permission to send data to a new destination.
What to return if every route fails
When the primary and fallback routes are unavailable or unsuitable, stop the chain and return a controlled error or degraded experience. Preserve enough request and workflow context for safe resumption, record the incident, and avoid implying that a result was generated when it was not. The right user-facing response depends on whether the task can be queued, partially completed, retried later, or handed to a person.
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