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Cutting Datadog cost: log exclusion and metric cardinality

7 min read · June 17, 2026 · TurboFinOps

Observability is bought to understand cost — and then becomes a cost of its own. Datadog bills scale with indexed log volume and custom-metric cardinality, both of which grow silently as teams add instrumentation. Two levers recover most of the waste.

Lever one: indexed logs

Indexing every log line is expensive and usually unnecessary. Health checks, debug chatter and verbose access logs rarely get searched, yet they are indexed at full price.

Add exclusion filters for high-volume, low-value sources and keep them in the cheaper retention tier instead of indexing. You still have the data; you just stop paying premium rates to search logs nobody queries.

Lever two: custom-metric cardinality

Custom metrics are billed by unique tag combinations. A single metric tagged with a high-cardinality dimension — user ID, request ID, container ID — can explode into thousands of billable series.

Audit which metrics drive cardinality, drop tags nothing queries, and aggregate where you can. Most teams find metrics emitted by old dashboards that no monitor or graph references.

Make it a habit

Treat observability spend like cloud spend: review it monthly, attribute it, and correlate it with the cloud and AI cost it is meant to explain.

TurboFinOps ingests Datadog usage daily and surfaces both levers as recommendations — estimated savings for log-exclusion filters and for trimming metric cardinality.

Frequently asked questions

Will exclusion filters lose data I need?
No — exclusion filters stop indexing (the expensive part) while you keep the logs in a cheaper tier. Index what you actually search; archive the rest.
How do I find the metrics driving cost?
Look for custom metrics with high-cardinality tags and cross-reference against dashboards and monitors. Anything emitted but unreferenced is a candidate to drop or aggregate.

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