# Observability
All Observability notes →What is Cardinality (in observability)
The number of unique time series (or unique log/trace label combinations) a metric produces — the single biggest driver of ingest cost and query latency in Prometheus-family backends (Mimir, Cortex, Thanos), and the reason unbounded labels are a production incident waiting to happen.
1 — Cost Drivers
Covers the ingest-volume, cardinality, and retention-window levers that actually drive observability platform cost.
2 — Telemetry Sampling
Covers sampling as a cost lever across all three signal types, and the fidelity it trades away.
3 — Downsampling
Covers reducing metric resolution over time as data ages, and the query-accuracy tradeoff that comes with it.
4 — Retention Policies
Covers setting differentiated retention per signal type and per tier, driven by actual debugging-lookback needs rather than defaults.
5 — Compression
Covers the compression techniques (chunk encoding, columnar compression) that let TSDBs and log stores shrink storage cost per sample.
6 — Tiered Storage
Covers hot/warm/cold storage tiering — recent data on fast disks, older data in object storage — and its query-latency tradeoff.
7 — FinOps for Observability
Covers attributing observability spend back to the teams generating the telemetry, and using that attribution to drive down cost at the source.