I’m building a high traffic API and need trace sampling advice

For a high-traffic API, the main goal is to keep traces useful for debugging and performance analysis without overwhelming your backend, storage, or costs.

Practical sampling strategy

1) Use head-based probabilistic sampling as the default

• Sample a fixed percentage of requests at the start of the trace.
• Good for keeping overhead predictable.
• Typical starting points:
  • 0.1%–1% for very high traffic services
  • 1%–10% for lower-volume critical services

2) Always keep traces for important events

Override the default sampling decision for:

• Errors / exceptions
• Slow requests above a threshold
• Requests from VIP customers or internal users
• Specific endpoints you are investigating
• Synthetic checks / health probes if useful

This gives you more diagnostic value than pure random sampling.

3) Prefer tail-based sampling if you can afford the complexity

Tail sampling decides after the request completes, so you can keep:

• traces with errors
• traces with high latency
• traces with rare edge cases

Tradeoff:

• More infrastructure and memory buffering
• Higher operational complexity
• Better signal quality

A common pattern is:

• Head sample a small percentage of all traffic
• Tail retain all errors and slow traces

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Suggested starting policy

If your API is very busy, a good baseline is:

• 1% head sampling for all requests
• 100% capture for:
  • 5xx responses
  • requests slower than, say, 500ms or 1s
  • sampled routes of interest
• Optionally, 0.1% for ultra-high-volume endpoints like health or polling

Then adjust based on:

• trace volume
• backend ingestion cost
• how often you need traces for debugging

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Important implementation tips

Propagate the sampling decision

Make sure downstream services honor the upstream trace decision so you don’t create partial or inconsistent traces.

Don’t sample blindly by endpoint only

If you only sample “important” endpoints, you may miss systemic issues elsewhere. Combine:

• baseline random sampling
• targeted overrides

Watch cardinality and storage costs

Sampling won’t help if spans have huge attributes, high-cardinality tags, or verbose logs embedded in traces.

Measure the impact

Track:

• traces ingested per second
• percent sampled
• backend cost
• error trace coverage
• p95/p99 latency trace coverage

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Rule of thumb by traffic level

• < 100 RPS: 10%–100% may be acceptable
• 100–1,000 RPS: 1%–10% usually good
• 1,000+ RPS: start around 0.1%–1% and rely on targeted sampling

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Recommended default for most high-traffic APIs

If you want a simple answer:

Start with 1% probabilistic sampling, plus 100% sampling for errors and slow requests, and move to tail-based sampling if you need better debug coverage.

If you want, I can also help you design a sampling policy for your exact traffic level, latency SLOs, and observability backend.