How do I handle rate limits when scaling AI agents?
Strategies for Rate Limits
Rate limits are restrictions on how many requests you can make to an API in a given time. When scaling, you'll hit these limits quickly. Implement exponential backoff: when you get a 429 (Too Many Requests) response, wait a bit and retry, increasing the wait time each attempt. Add jitter (randomness) to avoid synchronized retries.
Use a queue to smooth out traffic. Instead of sending requests as they come, put them in a queue and have workers process them at a controlled rate. This prevents bursts that trigger rate limits. If you have multiple API keys, round-robin between them to multiply your effective limit.
- Implement exponential backoff with jitter for retries.
- Use a message queue (e.g., Redis, RabbitMQ) to buffer requests.
- Distribute requests across multiple API keys or accounts.
- Cache responses to reduce the number of API calls.
- Monitor rate limit headers (e.g., X-RateLimit-Remaining) to adjust dynamically.
Long-Term Solutions
If you consistently hit rate limits, consider upgrading your plan or negotiating a higher limit with your provider. Some providers offer enterprise tiers with higher limits. Alternatively, use multiple providers and route requests based on availability or cost.
Design your agent to degrade gracefully when rate limited. For example, fall back to a smaller model or a cached response. This ensures a better user experience even under heavy load.
Common mistakes
- Retrying immediately without backoff, which makes rate limiting worse.
- Using a single API key for all requests, hitting the limit faster.
- Ignoring rate limit headers and not adjusting request rates dynamically.
