I'm building a service that needs predictable latency under traffic spikes

For predictable latency under traffic spikes, the key idea is control concurrency and shed load before saturation. A good design is usually a mix of:

1) Put hard limits at every layer

• Rate limit incoming requests per client/API key.
• Bound concurrency for expensive handlers, DB queries, and downstream calls.
• Use timeouts everywhere so slow requests don’t pile up.
• Set queue limits if you buffer work; unbounded queues destroy latency.

2) Fail fast instead of degrading unpredictably

• Reject requests with 429/503 when capacity is exceeded.
• Prefer graceful overload over allowing tail latency to explode.
• Return partial or cached results when possible.

3) Protect downstream dependencies

• Use bulkheads: isolate pools for different request classes.
• Add circuit breakers for flaky dependencies.
• Apply backpressure if consumers can’t keep up.

4) Reduce work on the hot path

• Cache aggressively for read-heavy traffic.
• Precompute expensive results.
• Avoid synchronous fan-out to many services.
• Keep request processing CPU- and allocation-efficient.

5) Scale in a controlled way

• Autoscaling helps, but it usually reacts too slowly for spikes alone.
• Keep some headroom.
• Use warm pools / pre-provisioned capacity for expected bursts.

6) Measure and test tail latency

• Track p50, p95, p99, p99.9 latency.
• Load test with burst patterns, not just steady-state traffic.
• Verify behavior when dependencies slow down or fail.

A practical pattern

A robust architecture often looks like:

• Edge rate limiting
• Small bounded request queue
• Worker pool with fixed concurrency
• Timeouts + circuit breakers
• Caching
• Overload responses when saturated

If you want, I can help you design this for a specific stack (e.g. Kubernetes, Go, Node.js, Java, Redis, Postgres) or sketch a reference architecture.