python-client-rate-limiting

To handle API rate limiting in a Python SDK, implement adaptive rate limiting using token and leaky-bucket algorithms to control request flow. This optimizes throughput and prevents rate-limiting errors during high request volumes.

Implement retry logic with server hints by honoring `Retry-After` headers to delay retries after rate limiting responses. This ensures your API client respects server-provided instructions and avoids overwhelming the service.

Proactive traffic shaping analyzes `X-RateLimit-*` headers to reduce request rates before hitting limits. It uses AIMD algorithms to adjust request paces dynamically based on server responses, preventing throttling.

Yes, you can use OpenTelemetry to monitor API rate limiting incidents. It tracks rate-limiting incidents and shaping costs via telemetry signals, providing visibility into how traffic control impacts request throughput.

Aiolimiter supports adaptive control for asynchronous Python SDKs by managing token and leaky-bucket algorithms. Integrated with AIMD algorithms, it adjusts request rates dynamically based on server responses to avoid throttling.