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ScrapeLLM enforces per-account rate limits on synchronous requests and manages queue depth for async jobs. This guide covers how to scale efficiently without hitting limits.

Rate limits

All accounts are subject to per-second and per-minute request rate limits. Limits are stored per-user and enforced by the API gateway. When you exceed a limit, you receive HTTP 429:
Implement exponential backoff on 429 responses. See Error handling for a code example. For large batches, submit async jobs and poll for results. This avoids holding open HTTP connections and lets the ScrapeLLM job queue handle concurrency automatically.

Pattern 2: Concurrent workers (sync requests)

For smaller batches where you need immediate results, use concurrent sync requests - but respect rate limits.

Quick reference

Common questions

Why am I getting 429 errors?

You’ve exceeded your plan’s rate limit. Options:
  • Implement exponential backoff and retry
  • Switch to async job mode - jobs are queued server-side
  • Upgrade your plan for higher limits

What’s the best approach for processing 100+ prompts?

Use async jobs. Submit all jobs first, then poll for results. This decouples submission from processing and lets the queue handle concurrency automatically.

Can I increase my rate limit?

Yes - higher-tier plans include higher rate limits. Contact [email protected] for custom limits on enterprise volumes.