Many engineering leaders now spend almost as much watching their infrastructure as running it. The observability bill arrives, it has grown again, and nobody can say which data is worth paying for.
This guide shows how to reduce observability costs while keeping, or improving, your ability to find and fix problems.
Many engineering leaders now spend almost as much watching their infrastructure as running it.
Why are observability costs rising so fast?
- Container and microservice growth multiplies hosts, pods and services to monitor.
- High-cardinality metrics, such as labels containing user IDs or request IDs, create millions of time series.
- Verbose logging in production, often left at debug level.
- Tool sprawl. Separate tools for metrics, logs, APM, uptime and errors, each with its own bill and overlapping data.
- Default retention that keeps everything for months.
Where does observability spend usually go?
Observability cost drivers breakdown
Illustration in progress
| Cost driver | Typical cause | Primary fix |
|---|---|---|
| Logs | Debug logs, duplicate shipping, long retention | Filter, tier, shorten retention |
| Metrics | High-cardinality labels, unused metrics | Drop labels, aggregate, delete unused |
| Traces | 100% sampling of routine requests | Tail-based sampling |
| Hosts/agents | Per-host pricing on dense container nodes | Usage-based pricing, OTel |
| Tools | Overlapping platforms | Consolidate |
7 ways to reduce observability costs
1. Consolidate overlapping tools
Audit every observability tool and what it covers. Most organisations we assess have at least two tools collecting similar data. Consolidating onto one platform saves licence costs and reduces the time engineers spend switching tools.
Log tiering to reduce observability costs
Illustration in progress
2. Control metric cardinality
Find the metrics with the most active series. Remove labels with unbounded values (user ID, session ID, full URL paths). Aggregate where per-instance detail is not needed. Delete metrics no dashboard or alert uses.
3. Filter and tier logs
Drop or sample noisy, low-value logs at the source or in the OpenTelemetry Collector. Keep error and audit logs in searchable storage; route verbose logs to cheaper storage or shorter retention.
4. Use tail-based sampling for traces
Keep 100% of error and slow traces, and a small percentage of routine successful ones. You keep the diagnostic value at a fraction of the volume.
5. Set retention by value
Not all data needs the same retention. Keep high-resolution metrics for a short period and downsampled data for longer. Align log retention with compliance requirements, not defaults.
6. Choose a pricing model that fits your architecture
Per-host pricing can penalise dense Kubernetes clusters. Usage-based pricing rewards teams that manage cardinality and volume. Compare models against your real usage. See Grafana vs Datadog.
7. Make telemetry cost visible to teams
Show each team what its logs, metrics and traces cost, just as FinOps does for infrastructure. Visibility changes behaviour.
Will reducing telemetry hurt incident response?
Not if you cut the right data. Most savings come from data nobody queries: debug logs, unused metrics, routine traces and duplicate collection. Before deleting, check which data is used by dashboards, alerts and recent incident investigations. Often, a smaller, well-structured dataset makes investigation *faster*.
How Crozaint approaches observability costs
The Audit phase (weeks 1–2) of Crozaint's observability engagement maps every existing tool, the telemetry it collects and what it costs. We then consolidate onto a unified Grafana Cloud platform with OpenTelemetry instrumentation, giving one place for metrics, logs, traces and dashboards, and lower, unified observability spend.
Because Crozaint also runs FinOps engagements, we treat telemetry like any other cloud cost: allocated to owners, monitored for anomalies and optimised continuously.
Common mistakes to avoid
- Cutting data before checking which alerts and dashboards depend on it
- Keeping debug logging enabled in production
- Adding labels with unbounded values to metrics
- Paying for two tools that collect the same telemetry
- Treating observability spend as a fixed overhead
Conclusion
Pay for the telemetry you use. Consolidate tools, control cardinality, tier logs, sample traces and make costs visible to the teams that create them.
Observability bill out of control? Book a 30-minute discovery call to assess your stack with Crozaint.
