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Use a token bucket when you want to allow controlled bursts while limiting sustained traffic. Choose a sliding-window log when an exact rolling quota matters more than storing request timestamps, or a sliding-window counter when you want smoother window boundaries with less state. The right choice also depends on where quota state lives and what happens when that state is unavailable.
Token Bucket vs Sliding Window Explained
These algorithms decide whether to admit a request under a rate limit, but they enforce different meanings of “within the limit.” A token bucket meters traffic over time and can permit a burst. A sliding window counts requests against a rolling interval; its exactness and storage cost depend on whether it uses a timestamp log or a counter approximation.
| Algorithm | Burst behavior | Precision and state | Typical fit |
|---|---|---|---|
| Token bucket | Allows bursts up to bucket capacity as tokens accumulate | Redis describes its implementation as exact and using one hash key | Bursty traffic where short spikes are acceptable but sustained use should be controlled |
| Sliding-window log | Does not allow requests to exceed the rolling-window quota | Exact rolling count; stores O(n) request entries | High-value quotas or audit-sensitive limits where precision justifies timestamp storage |
| Sliding-window counter | Smooths sharp fixed-window boundaries | Near-exact estimate; Redis’s example uses two string keys | General-purpose limits needing smoother rolling behavior without storing every event |
| Fixed-window counter | Can permit a boundary burst of up to 2× the nominal limit | Approximate; Redis’s example uses one key | Simple quotas where the boundary artifact is acceptable |
The implementation details in this comparison come from Redis’s rate-limiter algorithm guide; the table describes those documented choices, not a universal performance benchmark.
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How the algorithms make a decision
Token bucket: allow controlled bursts
A token bucket has a maximum capacity B and a refill rate r tokens per unit of time. As time passes, tokens are added up to the capacity. A request is allowed if enough tokens remain, and its cost is deducted; otherwise it is rejected or delayed. In the simplest model, each request costs one token.
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The refill rate controls sustained traffic, while capacity sets the largest burst the bucket can absorb after tokens have accumulated. This makes token bucket useful when occasional spikes are legitimate, but it does not mean the system can sustain that burst rate indefinitely. Weighted request costs are possible in the basic model, although a specific gateway or limiter may not expose that capability.
Amazon API Gateway’s HTTP API documentation describes token-bucket throttling with a steady-state rate and a burst limit. In its model, a token corresponds to a request, and rate and burst submissions are both considered.
Sliding-window log: count the actual rolling interval
A log retains timestamps for requests in the current interval. When a request arrives, the limiter removes entries older than the interval, counts the remaining timestamps, and allows the request only if adding it would stay within quota. Because it tracks individual events, it can enforce an exact rolling count. Its cost is state: Redis characterizes the log as O(n) entries, where n is the number of retained requests.
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Sliding-window counter: estimate without retaining every request
A counter keeps totals for the current and immediately preceding fixed intervals. It estimates the rolling-window count by weighting the previous interval according to how much of that interval overlaps the current rolling window. This smooths the abrupt reset of a fixed-window counter without storing each request time. It is an approximation, not the exact count produced by a timestamp log.
Fixed window: simple, with a boundary edge case
A fixed-window counter counts requests in a fixed interval and resets at the boundary. That simplicity can allow a client to use nearly the full quota just before a reset and nearly the full quota again just after it—up to twice the nominal limit across the boundary. Use it only when that behavior is acceptable.
When should you use token bucket vs sliding window?
- Choose token bucket when short bursts should be permitted and a replenishing rate should govern longer-term use. Tune rate and capacity separately: one determines ongoing allowance, the other burst size.
- Choose sliding-window log when crossing a rolling quota is costly enough to justify keeping timestamps and doing exact rolling accounting.
- Choose sliding-window counter when the fixed-window reset is too abrupt but storing every request is too expensive. Describe its result as an estimate or near-exact count, not an exact log.
- Choose fixed window when implementation simplicity matters and its boundary burst is harmless for the protected resource.
There is no universal benchmark in the cited documentation proving one algorithm is faster or better in every system. Select based on the quota semantics, state cost, burst policy, and deployment—not an assumed performance ranking.
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What should the limit apply to?
Define the identity used to group requests before choosing an algorithm. A limit might be per user, IP address, API key, tenant, or model. Then decide whether the quota applies within one process, across all service instances, or across regions. The same algorithm can produce different effective limits depending on that scope.
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A process-local counter is insufficient for a service behind a load balancer if a client can be routed among several instances: each instance may see only part of that client’s traffic. Redis’s rate-limiter documentation describes using shared state and atomic Lua scripts so the read, decision, and update happen together. Atomicity matters when concurrent requests might otherwise spend the same token or overwrite a counter update.
Operational choices that affect enforcement
Decide how the limiter fails
If a shared store is unavailable, an application can fail open and allow traffic, or fail closed and deny it. The safer policy depends on what the limit protects: availability, cost, abuse prevention, or a critical user action. Set a timeout for the limiter check so a slow dependency cannot leave request handling waiting indefinitely. Redis’s Go example explicitly leaves this policy to the application.
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Tell clients what to do after a denial
Rate-limited clients need a clear rejection response and retry behavior. AWS documents that API Gateway can return HTTP 429 when rate or burst targets are exceeded and advises clients to resubmit failed requests in a rate-limited way. Avoid treating a configured cloud throttle as a guaranteed hard ceiling: AWS says its throttles are best-effort targets rather than guaranteed request ceilings.
Check the scope of distributed counters
An edge provider may not maintain one counter for its entire network. Cloudflare’s documentation on request-rate calculation says counters are maintained by data center rather than globally across the entire network, with an exception for multiple data centers associated with a geographical location. A limit enforced this way may not behave like a single centralized counter when traffic moves across locations.
Consider work-based limits when request costs vary
If requests consume very different amounts of compute, counting each as one request may not represent resource use well. Cloudflare documents cost-based rate limiting for Enterprise customers using Advanced Rate Limiting: the origin returns a numeric score in a response header, and a rule applies a score budget per client over a period. Its documented score input range is 1 to 1,000,000; this is a product-specific feature and depends on that plan and origin integration.
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Implementation examples and caveats
Redis provides examples of fixed-window, sliding-window, and token-bucket rate limiting, including a shared-store approach for distributed quotas. Its guide describes its own Redis check as sub-millisecond; that vendor statement should not be treated as a benchmark for other deployments.
For API Gateway, rate and burst controls are available at account and route levels for HTTP APIs, but actual configuration and quotas depend on API type, account, and region. Treat provider settings as operational targets and verify the applicable service configuration rather than assuming a universal limit.
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