API Rate Limiting and Throttling: What You Need to Know
API rate limiting restricts how many requests a client can make to an API within a defined time window. Rate limits protect API providers from abuse and overload and ensure fair usage across clients. Understanding rate limits helps you design integrations that work reliably within them.
How Rate Limits Work
Rate limits are typically expressed as requests per time window: 100 requests per minute, 1,000 requests per hour, 10,000 requests per day. Many APIs have multiple limit tiers — per-second limits for burst protection, per-minute limits, and daily limits. Exceeding a limit returns an HTTP 429 Too Many Requests response.
Rate Limit Headers
Well-designed APIs include rate limit information in response headers: X-RateLimit-Limit (your limit), X-RateLimit-Remaining (requests remaining in current window), X-RateLimit-Reset (Unix timestamp when the window resets). Monitor these to proactively slow down before hitting limits.
Handling Rate Limits in Integrations
- Exponential backoff: When a 429 is received, wait and retry — double the wait time on each subsequent limit hit
- Request queuing: Queue outbound API requests and send them at a controlled rate rather than in bursts
- Caching: Cache API responses and serve from cache for repeat requests — reduces API calls for frequently accessed data
- Batching: Many APIs support batch operations — fetch 100 records in one request rather than 100 individual requests
Implications for Architecture
Integrations that make high volumes of API calls must account for rate limits in their design. A synchronous integration that makes 50 API calls in a user request will hit limits under load. Asynchronous processing with queues and rate-aware workers is the correct pattern for bulk API operations.