Take control of observability costs with Cost Guardrails
Automatically cap ingest & retention, remove waste, and ensure predictable pricing — without sacrificing visibility.
Why Cost Guardrails Matters
If you've ever been startled by a sudden cloud bill after a traffic spike, you know the pain. Log and metric ingestion costs can spiral out of control overnight.
One incident or deployment can trigger massive data ingestion, leaving you with a bill that's 2-3x your normal spend — with no warning.
Keeping logs for compliance or 'just in case' means paying for storage long after the data's value has diminished. Most logs are never queried after 7 days.
Noisy log sources — health checks, debug logs, verbose application output — consume bandwidth and storage without providing value for debugging or RCA.
Without clear visibility into what's driving costs, you can't make informed decisions about what to keep, what to sample, or what to drop.
Engineering teams often exceed observability budgets, forcing difficult trade-offs between visibility and cost — especially during incidents when you need data most.
Intelligent cost optimization that preserves what matters
Set hard limits on ingestion volume per service or globally. When limits approach, Cost Guardrails automatically applies intelligent sampling to stay within budget.
Never exceed your budget — predictable costs even during traffic spikes.
Automatically identifies noisy log sources and applies pattern-based sampling. Preserves 100% of error logs, anomalies, and unique patterns while reducing overall volume.
Reduce ingest by 20-40% without losing critical debugging data.
Automatically adjusts retention periods based on data value. Critical data (errors, anomalies) kept longer; routine logs moved to cold storage or archived sooner.
Cut storage costs by 30-50% while maintaining compliance and RCA capability.
Real-time visibility into what's driving costs: top log sources, retention spend, sampling impact, and projected savings. Make data-driven decisions about your observability stack.
Understand your spend and optimize continuously with actionable insights.
Get notified when ingestion approaches limits or when unusual cost patterns are detected. Proactive alerts help you stay ahead of budget overruns.
Avoid surprises — get early warnings before costs spiral out of control.
Override guardrails for specific services or during incidents. Full control when you need it, automatic optimization the rest of the time.
Flexibility to handle edge cases without compromising day-to-day savings.
Real savings from real teams
See how much Cost Guardrails could save you
No. Cost Guardrails uses intelligent pattern-based sampling that preserves 100% of error logs, anomalies, and unique patterns. Only repetitive, low-value logs are sampled. You'll never lose data needed for root cause analysis.
Cost Guardrails supports compliance requirements. You can set service-level overrides to retain full data for specific services or data types. The system also supports tiered retention (hot/cold/archive) to reduce costs while maintaining compliance.
Very flexible. You can set caps at multiple levels: global, per-service, per-environment, or per data type. Caps can be adjusted in real-time, and you can override them temporarily during incidents or special events.
Yes. Cost Guardrails allows service-level overrides. You can exempt critical services from sampling, set custom retention policies, or temporarily disable guardrails during incidents. Full control when you need it.
Cost Guardrails uses ML-based pattern analysis to identify repetitive, low-value log sources. It analyzes log patterns, frequency, and correlation with incidents to determine what can be safely sampled without losing debugging value.
Cost Guardrails works out of the box with zero configuration. It automatically analyzes your data patterns and applies intelligent policies. You can customize settings, but it's designed to work immediately with sensible defaults.
See how Cost Guardrails can help you save 20-40% on observability costs without losing the data you need for debugging and root cause analysis.