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Retry logic is one of those things nobody thinks about until it's costing them money. A naive retry loop doesn't just waste engineering time chasing a bug — it can quietly double or triple your token spend on the exact requests that were already failing.
Why This Matters
When a request to an inference endpoint times out, hits a rate limit, or returns an error, the instinct is to retry immediately. Done carelessly, this creates a few expensive failure patterns:
- Retrying a request that actually succeeded server-side, but the response was lost client-side — you now pay for the same generation twice
- Retrying immediately and repeatedly against a rate limit, generating a burst of failed (but sometimes partially billed) calls
- No backoff between retries, hammering an already-struggling endpoint and making the underlying problem worse
- No cap on retry attempts, letting a bad request retry indefinitely
A well-designed inference router actually helps here: when a model hits a rate limit or capacity constraint, requests can fall back automatically to another model in your pool, with no dropped calls — which is a very different experience than a client blindly retrying against the same failing model.
A Simple Framework
- Add exponential backoff between retry attempts instead of retrying instantly
- Cap the maximum number of retries for any single request
- Distinguish retryable errors (timeouts, rate limits) from non-retryable ones (bad request, invalid input) — don't retry the latter
- Configure fallback models in your router so failures reroute instead of just retrying the same model
- Log and monitor retry rates — a rising retry rate is an early warning sign, not just background noise
Example
Before: A script hits a rate limit and immediately retries in a tight loop with no backoff, generating a burst of failed requests in seconds and, in some cases, duplicate billed generations when a "failed" request had actually completed server-side.
After: The same script uses exponential backoff, a maximum of three retries, and a router configured with fallback models — so a rate limit on the primary model quietly reroutes to a backup model instead of hammering the same endpoint.
> Tip: Log the reason for every retry, not just the fact that one happened. Patterns in retry reasons (mostly rate limits vs. mostly timeouts vs. mostly malformed requests) point to very different fixes.
Common Mistakes
- Retrying instantly with no backoff, worsening rate-limit pressure
- Retrying non-retryable errors (like a malformed request) that will never succeed no matter how many times you try
- No retry cap, allowing a bad request to loop indefinitely
- Not configuring fallback models, leaving your app fully dependent on one model's availability
DigitalOcean explains automatic fallback behavior in its Inference features guide — worth reading before you write your own retry logic from scratch.
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Good retry logic isn't glamorous, but it's directly tied to your bottom line. A well-configured router with fallback models handles most of this for you — the rest is just disciplined error handling on your end.