Choosing the Right Storage Backend Is Key to Effective Rate Limiting

Rate limiting commonly begins with a simple in-memory counter, which works well for single-process applications but loses all state on restart. Once multiple workers share traffic, they need a common storage layer so request counts remain consistent across instances. Redis is the recommended default for high-traffic shared limiters, while PostgreSQL suits services that already rely on it and don't need sub-millisecond checks. Developers must also define a fallback policy for when the storage backend times out, since different endpoints carry different risk levels. The open-source project RateLock aims to abstract these storage choices, letting applications maintain the same rate-limiting interface across local, Redis, and database-backed deployments.