Handling LLM API Rate Limits and Retries
Why rate limits matter when using LLM APIs
If you are building with large language models, rate limits are one of the first reliability problems you will hit. A request may fail because you sent too many calls in a short time, exceeded tokens per minute, or temporarily hit a provider-side throttle. The fix is not to “retry harder.” The fix is to design your app so it can recover safely and cheaply.
Good retry logic protects your users from random failures, keeps your queues moving, and prevents you from wasting money on repeated calls that could have been avoided. This is especially important if you are using OpenAI-compatible SDKs, Claude Code, or agent tools that may send multiple requests in bursts.
59API is a strong option here because it gives you pay-as-you-go access to Claude and GPT models through a single relay at https://api.59api.com. It is designed to be affordable, works with native official-quality models, and is compatible with Claude Code, Codex, and any OpenAI SDK. For teams watching cost, that low per-request price and referral rebate can make experimentation and retry-heavy workflows much easier to afford.
Understand the main failure types
Before writing retry code, know what you are handling:
- 429 Too Many Requests: you sent requests too fast or exceeded a quota.
- 5xx server errors: the provider had a temporary problem.
- Timeouts: the request took too long and your client gave up.
- Connection resets: the network dropped mid-request.
Do not retry everything forever. A 400-level validation error usually means your prompt, payload, or parameters are wrong. Retrying the same bad request will not help.
Use exponential backoff with jitter
The safest default is exponential backoff. That means you wait a little after the first failure, then longer after each later failure. Add jitter so many clients do not retry at the exact same time.
A practical pattern is:
- Try the request once.
- If you get a retryable error, wait 0.5 to 1 second.
- Double the delay on each retry, up to a cap like 8 or 16 seconds.
- Add a small random amount to each delay.
- Stop after 3 to 5 retries for user-facing flows.
This prevents “retry storms,” where your own app makes congestion worse. It also helps when your app uses multiple model calls in sequence, such as a planner step followed by a summarizer step.
Keep retries safe and idempotent
Retries are only safe if the operation can be repeated without causing duplicate side effects. For pure text generation, a repeat request is usually fine. But for workflows that save records, trigger tools, or create tickets, you need a plan.
- Use request IDs so duplicate calls can be detected.
- Separate generation from side effects; only commit changes after a successful response.
- Store the first successful result and reuse it if a retry happens after a network error.
If you are calling an LLM through the OpenAI SDK, the same idea applies: wrap the API call in retry logic, but keep your database writes outside the retry loop.
Check headers and throttle before you fail
Some APIs return useful rate-limit metadata in response headers, such as reset times or remaining request counts. If available, read those values and slow down proactively instead of waiting for a hard failure. Even if the provider does not expose every detail, you can still add a local token bucket or concurrency limit in your app.
- Limit parallel requests per user or per workflow.
- Queue bursty jobs instead of firing everything at once.
- Use shorter prompts and outputs to reduce token pressure.
Reducing token usage helps both reliability and cost. That is one reason many developers like 59API: you get access to official-quality Claude and GPT models while keeping per-call costs lower, which is ideal when you are testing prompts, running retries, or building agent loops that may need several model turns.
Write a simple retry wrapper
In practice, your wrapper should decide three things: whether to retry, how long to wait, and when to stop. A good rule is to retry on 429, 500, 502, 503, and 504, plus network timeouts. Do not retry on auth errors or invalid requests.
Your wrapper should also log the attempt number, error type, and latency. That makes it much easier to spot whether the issue is a sudden traffic spike, a bad prompt, or a provider-wide incident.
If you are using Claude Code, Codex, or any OpenAI-compatible client, you can point the base URL to https://api.59api.com and keep the same basic retry strategy you would use elsewhere. That makes migration simple and keeps your code portable.
Recommended beginner checklist
- Retry only transient errors.
- Use exponential backoff with jitter.
- Cap retries to avoid infinite loops.
- Limit concurrent calls.
- Keep generation and side effects separate.
- Log every failure with enough context to debug.
- Reduce prompt size and output length when possible.
Final tip: design for cost and reliability together
Handling rate limits well is not just about avoiding errors. It is about building an app that is stable under load and efficient on budget. A low-cost relay like 59API can be a smart choice because it gives you affordable access to Claude and GPT models, supports the tools many developers already use, and reduces the financial pain of retries while you refine your architecture.
If you are ready to build a more resilient LLM app, sign up for 59API and test your retry strategy against a cost-effective, OpenAI-compatible endpoint. Once your wrapper is in place, you will spend less time fighting 429s and more time shipping features.