How should teams reduce observability costs without losing useful telemetry?

Start at the pipeline, not the backend. Filter obvious noise, remove high-cardinality fields, and tail-sample traces so routine traffic is reduced after full context is available. That preserves error and latency signal while cutting ingest, storage, and query costs before they become locked into the observability bill.

Why This Matters for Security Teams

Observability spend becomes a security issue when telemetry growth outpaces the team’s ability to review, retain, and alert on it. Cost pressure often pushes organisations to delete data too early, while uncontrolled ingest creates blind spots in incident response and threat hunting. The goal is not maximum data collection. It is collecting the right evidence at the right stage, aligned to operational needs and control obligations described in NIST SP 800-53 Rev 5 Security and Privacy Controls.

Security teams usually get this wrong in two ways. One group keeps everything and pays for noisy telemetry that weakens signal quality. The other trims aggressively at the source and later discovers that key fields needed for forensics, service health analysis, or abuse detection were never preserved. The better approach is to define which events must remain searchable, which can be sampled, and which should be transformed into lower-cost aggregates. That requires input from engineering, SecOps, compliance, and platform owners, not just the observability tool administrator.

In practice, many security teams encounter telemetry gaps only after an incident has already forced them to prove what happened.

How It Works in Practice

The most effective cost reduction happens in layers. First, reduce waste before data reaches expensive backend storage. That means filtering low-value logs, dropping duplicate events, and normalising noisy fields such as per-request identifiers that create unnecessary cardinality. Second, preserve context at the edge by keeping enough metadata to support investigation even when full payloads are not retained. Third, apply sampling carefully so that routine traffic is reduced without erasing the events most likely to matter during failure or attack conditions.

For traces, tail sampling is often more useful than head sampling because the system can inspect the completed trace before deciding what to keep. For logs, policy-based routing can send security-relevant events to a longer-retention store while keeping operational noise in cheaper short-term storage. For metrics, use rollups and bounded labels to avoid uncontrolled series growth. Where possible, align these choices to retention tiers so security, reliability, and compliance needs are met without paying premium storage costs for every record.

• Keep error, auth, privilege, and configuration-change events at higher fidelity.
• Remove or hash fields that create high-cardinality noise but do not aid investigation.
• Route security-relevant telemetry to longer retention than routine application debug data.
• Validate that sampled traces still cover incident-response questions and abuse patterns.

Good practice also depends on detection design. If a control relies on exact sequence reconstruction, aggressive sampling will weaken it. If a signal is only used for trend analysis, a lower-resolution dataset may be enough. Current guidance suggests defining telemetry by use case first, then mapping each data class to the minimum retention and resolution needed to support that use case. These controls tend to break down when multi-tenant platforms generate unpredictable cardinality spikes because shared labels and customer-specific dimensions can inflate both ingest and query costs overnight.

Common Variations and Edge Cases

Tighter telemetry filtering often reduces cost and alert fatigue, but it also increases the risk of losing rare but important evidence, so organisations must balance savings against forensic depth. That tradeoff is especially sharp in regulated environments, where retention requirements and audit expectations limit how far sampling can go. There is no universal standard for this yet, so current guidance suggests treating security telemetry differently from product analytics and documenting the rationale for each retention tier.

Edge cases appear when teams use observability data for both engineering and security purposes. In those environments, a field that looks disposable to platform engineering may be critical for abuse detection or identity correlation. The same applies to identity-sensitive systems, where access logs, token events, and administrative actions may need longer retention than ordinary app telemetry. A useful reference point for control selection is NIST SP 800-53 Rev 5 Security and Privacy Controls, which helps teams justify retention, review, and auditability decisions.

Best practice is evolving for AI-assisted operations as well. If an AI agent or automation layer consumes observability data, teams should check that reduced telemetry still supports model evaluation, anomaly review, and post-incident reconstruction. For cloud-native estates, a second useful benchmark is CISA Secure by Design, because cost control should not be achieved by shifting preventable risk into missing evidence. The main failure mode is over-optimising one pipeline stage while ignoring the downstream investigative workload.

Related resources from NHI Mgmt Group

• How should security and platform teams reduce telemetry costs without losing operational visibility?
• How should security teams reduce telemetry overload without losing useful signals?
• How should security teams reduce endpoint telemetry sprawl without losing visibility?
• How should teams reduce Microsoft Sentinel costs without losing detection coverage?