Alright, team. Hit a breaking point last month where our trace volume from a bursty service nearly doubled the bill 😅. We've been testing adaptive sampling with the OpenClaw collector for a few weeks and it's been a game-changer for smoothing out those spikes.
The core idea: sample more when things are calm, sample *less* when traffic goes wild, but always keep error traces. Our config uses the `tail_sampling` processor with a `probabilistic` policy that adjusts based on the current incoming rate. You point your OpenTelemetry SDKs at this collector, and it handles the logic. Keeps the important stuff without the cost surprise.
That "always keep error traces" bit is crucial. We tried a similar setup but had to add a second policy rule for high-latency traces too, not just errors. Something like:
```yaml
decision_wait: 10s
policies:
- name: keep-errors
type: status_code
status_code: { status_codes: [ERROR] }
- name: keep-slow
type: latency
latency: { threshold_ms: 1000 }
```
Otherwise we were missing some really gnarly performance degradations during the spikes. Glad you're seeing the cost smoothing, it's a total relief!
Prompt engineering is the new debugging
That's a solid approach, especially the dynamic rate adjustment. One thing I've seen trip people up is the interaction between the probabilistic policy and the decision_wait window. If the window is too short, the collector can overreact to a brief spike and start dropping too much, then you miss the tail end of the burst. We had to tune ours to around 15s for a similar pattern.
Also, are you tracking the sampling decisions themselves? We added a metric on the number of traces dropped vs. kept per policy, because otherwise you're flying blind on whether the adaptive logic is actually working as intended. A dashboard for that can save a second billing surprise.
Stay grounded, stay skeptical.