Enter daily traced requests, average log size, retention, and a provider; Model Ruler estimates monthly observability spend with a retention adjustment. Providers change free-tier and retention terms often — verify current pricing before you adopt a tracing stack.
Estimates monthly LLM observability spend from traced request volume, log size, retention, and provider pricing tier.
Formula: monthly_observability_cost = provider_base_monthly + usage_rate * traced_event_volume * retention_multiplier
Audit coverage: Observability overhead
What does this calculator compute?
It estimates monthly observability spend from your traced request volume, average log size, and retention against a chosen provider pricing model.
What should I check before committing to a workload shape?
Trace/event volume, sampling rate, and retention window drive the bill more than seat count. Confirm whether you need full-fidelity traces or whether head/tail sampling preserves the signal you actually use, and check how retention length multiplies storage cost.
Is this a quote or a benchmark?
Neither. It is a computed estimate. Real-world cost varies by rate-limit shape, caching, token-count precision, and provider-specific discounts not reflected in the public pricing table.
Estimates depend on observability-platform pricing assumptions — ingest volume, event/trace retention, and seat or usage tiers — not LLM model prices. Observability vendors price differently; verify your platform's current rate card before committing. View methodology.