Why High-Cardinality Metrics Break Everything

High-cardinality metrics promise granular per-request, per-user insights but quietly break production systems in four ways: costs become unpredictable and scale with runtime behavior rather than configuration; queries slow down during incidents when speed matters most; engineers lose trust as sparse, short-lived series create flickering dashboards and inconsistent results; and teams over-instrument without intent, creating multiplicative cardinality explosion. The core issue isn't that high-cardinality is wrong, but that most observability systems don't surface their own limits around storage, indexing, query performance, and data ambiguity. Success requires treating high-cardinality metrics like APIs with explicit ownership, guardrails, pre-deployment cardinality estimation, and systems designed for interactive exploration under pressure rather than brute-force scans.