Pricing Calculator

Observability Cost Calculator

Estimates your total monthly cost based on real usage across metrics, logs, and traces.

Metrics

Active time series

100Kseries
10K100M

Logs

Monthly ingestion

100 GBGB/month
10 GB100 TB

Traces

Monthly ingestion

50 GBGB/month
10 GB100 TB

Cost Breakdown

Total Cost of Ownership

Scale Plan base fee

$19/month platform fee

$19.00
Metrics

$0.00145/series · 100K active series

$145.00
Logs

$0.3/GB · 100 GB/month

$30.00
Traces

$0.3/GB · 50 GB/month

$15.00

Total Cost

$209.00/month
Enterprise

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Estimates are indicative. Actual costs may vary based on usage patterns.

Active series drives metrics cost

Prometheus-style metrics cost usually grows with active time series. Kubernetes labels, tenants, services, and dynamic infrastructure can multiply series count quickly.

Logs and traces scale with ingestion

Log and trace cost depends on monthly ingestion volume. Use the sliders to model what happens when teams keep more detailed telemetry instead of dropping it too early.

Use this before vendor comparisons

The calculator gives you a simple baseline before comparing Datadog alternatives, managed observability services, or Prometheus backend options.

Cost questions

Observability cost FAQ

How do I calculate observability costs?

Start with active metrics series, monthly log ingestion, monthly trace ingestion, and any base platform fee. Then compare the result against retention and operational requirements.

What drives observability cost?

The main drivers are metrics cardinality, log volume, trace volume, retention period, query demand, and the engineering time required to operate backend infrastructure.

Why can Datadog become expensive at scale?

Broad observability suites can become expensive when high-volume telemetry grows across several product areas. Teams with backend-heavy metrics, logs, and traces should model volume before migrating.

What is the cheapest way to keep Prometheus-compatible metrics?

The cheapest useful option is usually the one that preserves needed queries and retention while reducing backend operations and high-cardinality storage cost.