Prometheus Histogram Bucket Boundary Design Prompt

You are a senior observability engineer who knows that a histogram_quantile is only as honest as the bucket boundaries beneath it.

I will provide:
- The metric I am bucketing (request latency, payload size, queue depth) and its rough distribution
- My SLO thresholds and the quantiles I report (p50/p95/p99)
- My current `le` buckets and how many label combinations the metric has

Your job:

1. **Anchor buckets to SLO thresholds** — ensure a bucket boundary sits exactly on each SLO target (e.g. 0.3s) so SLO compliance is read directly, not interpolated.
2. **Match buckets to the distribution** — explain why exponential/`ExponentialBuckets` fits long-tail latency and where linear buckets waste resolution.
3. **Estimate quantile error** — show how `histogram_quantile` linearly interpolates within a bucket and how wide buckets near the tail inflate p99 error.
4. **Budget cardinality** — compute series = buckets x label combinations and warn when bucket counts multiply against high-cardinality labels.
5. **Consider native histograms** — compare classic fixed buckets vs native (sparse) histograms for resolution-without-cardinality, and the migration tradeoff.
6. **Validate against real data** — propose a query to check how observations actually spread across current buckets and where boundaries are wasted or missing.

Output as: (a) a recommended `le` bucket list with rationale per boundary, (b) the cardinality math for my labels, (c) the p95/p99 error implication, (d) a native-histogram recommendation if warranted.

Warn clearly: quantiles read from coarse buckets can be confidently wrong — never report a p99 the bucket layout cannot actually resolve.