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Reduce logging costs by measuring what you collect, cutting or sampling only low-value repetitive events, and setting retention by diagnostic and compliance needs. Keep structured records with request and trace identifiers, route each category deliberately, and test that engineers can still investigate a representative failure after each change.
1. Establish what is driving volume and cost
Start with a baseline before changing collection rules. Break volume and spend down by service, environment, severity, and log category; identify spikes, repeated events, and destinations that store duplicate copies. Use billing and ingestion data to distinguish a genuinely expensive stream from one that merely looks noisy.
Audit logs deserve particular care: Google Cloud notes that Data Access audit logs can be large and recommends estimating their cost. It gives development-project Data Access logs as an example of records an organization may decide it does not need, but that is not a blanket recommendation to disable security evidence. First determine what your security, audit, incident-response, and regulatory policies require. Google Cloud’s audit-log best practices also discuss retention and location.
2. Decide which events need full detail
Write an event policy before applying filters. The point is not to keep every line forever or to discard every successful event; it is to match detail and retention to the question the data must answer.
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- Keep: errors and security, audit, or compliance records required by policy, along with the context needed to explain consequential failures.
- Reduce or sample: known repetitive success, health-check, or high-volume low-criticality events when a metric, counter, or sampled trace can answer the operational question.
- Temporarily expand: allow elevated debug verbosity for a defined investigation window, with an owner, activation condition, and rollback plan.
For questions such as “How many requests timed out?” or “What was the latency distribution?”, a log-based metric may be more economical than retaining every matching event indefinitely. Google Cloud Logging can count matching entries or extract numerical values such as latency. Keep supporting event-level evidence where it is necessary for investigation. Cloud Logging’s overview describes metrics, analysis, alerts, routing, and storage.
3. Filter and sample selectively
Remove or downgrade events only when you know what diagnostic question they answer and have a less costly way to answer it, if needed. Filter predictable noise at the narrowest useful boundary—for example, a specific event type or environment—rather than suppressing a broad severity level that could also contain an important failure.
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Sampling can reduce data from high-volume paths, but choose it according to criticality and validate that it still exposes rare failures. AWS Prescriptive Guidance recommends higher trace sampling for critical paths and lower sampling for high-volume, less-critical routes in its Amazon EKS observability guidance. That is guidance about traces in an EKS context, not a universal log-sampling formula. Adapt it carefully if applying the same principle to logs, and do not assume any fixed sampling rate is right for every service. AWS Prescriptive Guidance for Amazon EKS observability also discusses retention and compression.
4. Keep the fields that make retained logs useful
Volume reduction is safer when the remaining records can be found, interpreted, and tied to the request or operation that produced them. Use structured logs and a consistent data model rather than relying on free-form messages alone. OpenTelemetry supports mapping existing formats to its log data model and emitting structured logs through APIs or appenders. The OpenTelemetry Logs specification describes the model and correlation fields.
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A practical baseline for an application event includes:
- timestamp, severity, service, and environment;
- a stable event name or type, plus relevant status or error information;
- request, trace, or span identifiers where available;
- the small set of contextual values needed to distinguish the affected operation, without exposing secrets or unnecessary personal data.
OpenTelemetry recommends including TraceId and SpanId in log records where possible. Its specification says, “This allows to directly correlate logs and traces that correspond to the same execution context.” Correlation matters because a log line without execution context may not show where it was called from or how it relates to other work. The OpenTelemetry observability primer explains how logs, spans, and traces complement one another.
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5. Set retention and routing by purpose
Separate records that need fast search during an incident from data that must be retained longer for audit, security, or other obligations. Route each category to the destination that fits its use, and check for duplicate routes: copies sent to multiple storage destinations can incur repeated storage and retention charges.
Google Cloud Logging can route entries to log buckets, BigQuery, Cloud Storage, and Pub/Sub. Its pricing documentation lists default retention of 30 days for the _Default and user-defined buckets, and fixed retention of 400 days for the _Required bucket. These are Google Cloud service-specific settings, not general defaults for other providers. Google also warns that routing copies to more than one bucket can result in multiple storage and retention charges. Confirm current pricing and configuration for your region and account before changing a production policy. Google Cloud Observability pricing documents the service’s changeable pricing and retention details.
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For any platform, compare destinations on ingestion and storage charges, retention controls, query speed, trace correlation, access controls, data location, and the diagnostic coverage left after filtering. No single backend or retention period is right for all teams; legal and contractual obligations can constrain the choices.
6. Verify the policy before calling it done
- Record the pre-change event volume and cost by source and destination.
- Apply one targeted filter, sampling rule, routing change, or retention change at a time.
- Compare the resulting volume and cost with the baseline, accounting for the destination and retention period.
- Run representative incident queries against the retained data. Confirm that an error can be found and reconstructed with its service, request, and trace context.
- Ask security, audit, or compliance owners to confirm that required evidence remains available and protected.
- Document the rule, rationale, owner, and rollback procedure so the policy can be revisited when traffic or requirements change.
If an incident becomes harder to explain, restore the relevant event detail or retention while you revise the policy. Cost controls should be reversible; a cheap pipeline that loses evidence needed to diagnose an outage or meet an obligation is not a successful reduction.
