Intelligent Observability Data Sampling to Reduce Monitoring Costs by 60-80%
Companies spend $10K-100K+/month on observability (Datadog, New Relic). Most telemetry data is redundant, healthy requests don't need individual traces. Intelligent sampling that keeps interesting data and discards noise would cut costs dramatically.
Problem Statement
Engineering teams send all traces, logs, and metrics to observability platforms. 90% of traces are healthy requests that add no debugging value. Teams pay per GB for storage of redundant data. Reducing volume means losing visibility during incidents.
The Idea
An intelligent sampling layer that sits between applications and observability backends, keeping error traces and slow requests while discarding redundant healthy telemetry to reduce monitoring costs by 60-80%.
Why Now
Observability costs are the fast-growing line item in cloud budgets. Datadog bills per host, per GB, and per trace. At scale, teams pay for storing millions of identical healthy request traces. Cost pressure is forcing teams to choose between visibility and budget.
Target User
Platform engineers, SREs, and FinOps teams managing observability budgets at scale
Target Market
Companies spending $10K+/month on observability tools (tens of thousands)
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