Outbound API Rate Limiting, Managed

What "Outbound Rate Limiting" Means

API gateways protect your endpoints from external callers — that's inbound rate limiting. This is the opposite problem: coordinating your own services' calls to external dependencies. When your workers call OpenAI, Stripe, or any third-party API, those providers enforce limits on you. You need to stay within them consistently across all your workers.

Dead-Simple Integration

Three steps: sign up → create a resource with your limits → wrap your API call. No infrastructure changes, no new services to run.

import ratequeue.aio as rq

# Before: fires immediately, races with your other workers
response = await stripe_client.charges.create(...)

# After: waits for capacity, coordinates across all workers
async with rq.acquire("stripe", api_key=RATEQUEUE_API_KEY):
    response = await stripe_client.charges.create(...)

Your existing API call stays unchanged — you just wrap it. The context manager handles the coordination transparently.

What You're Not Building

A properly-built distributed rate limiter is a non-trivial engineering project. Here's what RateQueue replaces:

• →Redis Lua scripts for atomic limit checks
• →Token bucket or sliding window algorithm implementation
• →Distributed lock management for concurrent counter access
• →Overflow queue for requests that exceed the current limit
• →Priority ordering for queued requests
• →Monitoring and dashboards for queue depth and error rates
• →Redis infrastructure: replication, persistence, failover
• →Retry logic and exponential backoff strategies

Works With Any API

RateQueue isn't tied to any specific provider. If an API has rate limits, you can coordinate access to it: