Question 1

Which rate-limiting algorithm should I use?

Accepted Answer

Token bucket is the most common default: it allows short bursts up to a cap while enforcing a steady average rate, which matches how real clients behave. Fixed-window counters are simplest but let users double their rate at the window boundary; sliding-window log or counter fixes that fairness gap at slightly higher cost. We pick based on how strict your fairness needs are and how much Redis memory you want to spend.

Question 2

Why do I need Redis instead of in-memory counters?

Accepted Answer

In-memory counters only work if every request hits the same server, which is never true behind a load balancer. With multiple API nodes, each would track its own count and a client could effectively multiply their limit by the number of servers. A shared store like Redis — using atomic increments or a Lua script — gives one consistent count across the whole cluster.

Question 3

What's the difference between rate limiting and throttling?

Accepted Answer

Rate limiting rejects requests over a hard threshold, typically with a 429 response. Throttling instead slows requests down — queuing or delaying them — to smooth traffic without outright rejection. Many APIs use both: throttle to absorb bursts, then rate-limit to enforce an absolute ceiling. We help you decide which behavior fits each endpoint.

Question 4

How should clients handle being rate limited?

Accepted Answer

Correctly built clients read the Retry-After header on a 429 response and wait that long before retrying, ideally with exponential backoff and jitter to avoid synchronized retry storms. We return standardized RateLimit-Limit, RateLimit-Remaining, and RateLimit-Reset headers so clients can self-pace before they ever hit the wall. Good documentation here prevents most support tickets.

Question 5

Can rate limits power my pricing tiers?

Accepted Answer

Yes — usage limits are one of the cleanest ways to differentiate plans. We tie quotas to API keys or subscriptions so a Free tier might allow 1,000 requests/day while Pro allows 100,000, with overage handling or upgrade prompts when limits are hit. Pair this with usage analytics and you have a monetizable, self-service product.

Question 6

Where should rate limiting live — gateway, app, or both?

Accepted Answer

An API gateway (Kong, NGINX, AWS API Gateway) is the ideal first line of defense because it rejects abusive traffic before it ever touches your application. For business-specific rules — per-user quotas tied to subscription state — application-level limiting has the context it needs. Most robust setups layer both: coarse protection at the edge, fine-grained policy in the app.

Question 7

Will rate limiting hurt legitimate users?

Accepted Answer

Not if it's designed and rolled out carefully. We start in monitor-only mode to observe real usage, set thresholds above genuine peaks, allowlist trusted partners, and separate limits by endpoint so heavy-but-valid operations don't trip consumer-facing calls. The goal is to stop abuse while real users never notice the limit exists.

Question 8

How do I stop distributed abuse from many IPs?

Accepted Answer

IP-based limits are easy to evade with rotating addresses, so for authenticated APIs we key limits on the API key or account instead. For public endpoints we combine IP limits with behavioral signals and, where needed, a WAF or bot-mitigation layer. Defense in depth matters: rate limiting is one control, not the whole security story.

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