𝗥𝗮𝘁𝗲 𝗟𝗶𝗺𝗶𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗖𝗶𝗿𝗰𝘂𝗶𝘁 𝗕𝗿𝗲𝗮𝗸𝗲𝗿𝘀 𝗶𝗻 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀
Distributed AI systems are complex. They handle huge request volumes and heavy model inference. You rely on GPU clusters, databases, and third-party APIs. One bad component or a traffic spike can crash your entire system.
You need two tools to protect your system: rate limiting and circuit breakers.
Rate Limiting Rate limiting stops a single user or service from using too many resources. It ensures fair access for everyone.
Common methods:
- Token Bucket: Best for AI. It allows short bursts of activity while keeping a steady average.
- Leaky Bucket: Keeps a constant flow of requests.
- Fixed Window: Simple but can cause spikes at the start of a new window.
- Sliding Window: More accurate than fixed windows.
Pro tip for AI: Limit by token count, not just requests. One prompt with 4,000 tokens uses more resources than a prompt with 10 tokens.
Circuit Breakers A circuit breaker monitors calls to services like your GPU server or vector database. If a service fails too many times, the breaker opens. It stops all calls to that service immediately. This prevents a total system crash.
The circuit follows three states:
- Closed: Everything is working normally.
- Open: The service is failing. Calls fail fast or use a fallback.
- Half-Open: The system tests the service to see if it recovered.
Best practices:
- Track slow calls. If an LLM takes too long, treat it as a failure.
- Separate error types. Do not trip the breaker for user errors like 400 Bad Request. Only trip it for connection errors or timeouts.
Optional learning community: https://t.me/GyaanSetuAi