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A rate limit can protect an API’s total capacity and still leave customers competing unfairly for it. In an incident account, developer Sergey Shinder describes how one customer’s backfill consumed a shared service-wide token bucket while 112 other customers were rejected. The distinction matters: a global cap controls the total, but it does not decide how that capacity is shared.
What happened in the incident
Shinder reports that a customer started a historical API backfill. Within ten minutes, 112 other customers were being rejected. The edge limit was one token bucket shared across the service, sized at 2,000 requests per second. According to Shinder, the backfill customer’s steady rate was about 40 requests per second.
Those figures are the author’s account, not independently verified telemetry. He reports that aggregate availability for the hour was 91%, while availability for the 112 customers who were not doing anything unusual was closer to 30%. The aggregate figure made the incident look less severe than the experience of those customers.
The mechanism was the shared bucket: tokens became available as capacity renewed, and a high-volume caller could consume them before quieter callers reached the limit. The rate limit constrained total traffic, but because it was not keyed by customer, it did not reserve or distribute capacity among customers.
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Why a global rate limit is not a fairness policy
A global limit is useful for protecting a service from exceeding a capacity boundary. It answers, “How much traffic may pass through this shared resource?” It does not answer, “How much should each customer be able to use?” If all callers draw from the same bucket, a caller that continuously sends requests can dominate access even when its sustained rate is below the nominal aggregate limit.
As Shinder puts it, “A limit protects the service. It says nothing about who gets what, and where you have not said it, the answer is whoever pushes hardest.” Read Shinder’s account on DEV Community.
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How to combine customer isolation with service protection
Shinder says the team changed its policy to give each customer a bucket sized from that customer’s trailing 30-day peak multiplied by a factor, while keeping the global bucket as a service-protection backstop. This separates two jobs: per-customer limits reduce the chance that one caller consumes everyone else’s allowance, and the global limit still constrains total load.
That sizing rule was the author’s reported choice, not a universal default. Any per-customer scheme needs an explicit policy for how bucket capacity is calculated, how often it changes, and how customer identity is determined. A generous per-customer allowance can still add up to more traffic than the service can handle, which is why the global backstop remains useful.
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Choose the scope of each limit deliberately
“Rate limit” can refer to different scopes: a whole service, an Envoy process, a downstream connection, a route, a virtual host, or a bucket associated with a customer-specific descriptor. Those scopes produce different behavior and should be made explicit in both configuration and operational documentation.
Envoy’s local rate-limit documentation describes token buckets configured for routes or virtual hosts. When the checked bucket has no tokens, the filter returns HTTP 429 by default, although the response status can be configured. Depending on configuration, the bucket can be shared at the Envoy process level or allocated per downstream connection. Envoy descriptors can match request attributes such as caller cluster and path, creating distinct buckets for matching combinations and a default bucket for other requests. This is an implementation pattern for scoping limits; it does not establish that Shinder’s reported design was implemented or tested in Envoy. The documented page identifies Envoy 1.40.0-dev: Envoy local rate-limit filter documentation.
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Set priorities for interactive and batch work
Shinder also reports classifying requests so interactive calls outrank batch work from the same key. This can preserve responsiveness for user-facing operations when background work is consuming capacity, but priority is a policy choice, not an automatic feature of a token bucket. Teams need to define which requests count as interactive or batch, how much preference applies, and what happens when interactive demand itself reaches the limit.
Per-customer isolation and workload priority address different problems. Separate customer buckets limit competition between tenants; class-based rules govern competition between kinds of work, potentially within one tenant. Neither replaces the global capacity guard.
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Make throttling understandable to customers and operators
A 429 response should help identify which limit was reached, rather than leaving the caller to guess whether the constraint is per customer, per route, or service-wide. Envoy can optionally emit a Retry-After header for an enforced local 429. Its documented delay indicates when a token is next available in the rejecting bucket, subject to the configured behavior; it is not a promise that an entire account or service will be fully usable after that delay.
Envoy also exposes counters for checked requests, rate-limited decisions, and enforced rejections. Those signals help distinguish requests evaluated by a limiter from requests actually blocked. Shinder reports adding information about which limit was hit in responses and recording the throttled fraction per customer, giving both customers and operators a clearer view of the impact.
Measure the worst-affected customer, not only the aggregate
Service-wide availability can hide severe failures for a subset of customers. Shinder says the team began publishing the worst tenant’s success rate beside aggregate service availability. Together with per-customer throttling rates, this makes it harder for a healthy majority—or a skewed aggregate—to mask a tenant experiencing sustained rejection.
Useful observability should connect the policy to its outcomes: which scope rejected a request, which customer or workload class was affected, how often throttling occurred, and whether the customer’s successful requests are declining. Aggregate capacity measures still matter, but they cannot show whether scarce capacity is being distributed as intended.
Quick Recap
Questions to answer before enforcing a limit
- Scope: Is the bucket global, per process, per connection, per route, per customer, or keyed by a combination of request attributes?
- Allocation: What determines each customer’s allowance, burst size, and adjustment schedule?
- Identity: How will requests be reliably associated with the right customer?
- Priority: Which workloads take precedence, and what happens when prioritized traffic also exceeds capacity?
- Backstop: What global protection prevents the sum of customer allowances from overwhelming the service?
- Feedback: Can a caller tell which limit rejected the request, and does retry guidance describe the relevant bucket?
- Impact: Can operators see throttling and success rates per tenant as well as across the whole service?
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