How to handle high-cardinality metrics without exploding costs: A practical playbook

High-cardinality metrics arise when combinations of labels create large numbers of distinct time series, increasing costs in systems priced by active series and potentially prompting teams to remove dimensions that are valuable during incident response. The recommended approach is to first audit series counts and identify the attributes driving growth, including checking OpenTelemetry’s default cardinality limits and overflow behavior; retain dimensions that answer operational, customer-impact, or cost-allocation questions; and remove or rewrite unneeded attributes centrally through OpenTelemetry Collector processors or SDK views. The discussion argues that dimensions such as tenant, model version, route, region, and deployment version can be essential for diagnosing outages and tracking AI usage, while unique request identifiers may be better suited to traces or events. It presents Axiom as an alternative metrics store that charges by ingested data volume rather than series count, supports high-cardinality tags through adaptive metric placement, and can be adopted alongside an existing stack via the OpenTelemetry Collector, though its MetricsDB uses second-level timestamp precision and merges resource, scope, and metric tags into one namespace.

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