How volumetric sampling makes the most of your trace budget in Grafana Cloud

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Tracing is one of the richest observability signals, but it's also noisy and susceptible to data bloat. In a busy system, the vast majority of traces describe the same healthy, fast, successful request over and over, so most organizations downsample their traces to cut costs. But that approach has consequences, since the sampling strategy you choose determines whether you get a faithful picture of your whole system, or just a smaller, blurrier copy of your busiest endpoints.We want to help you get the most from your traces without having to make too many compromises. With Adaptive Traces, you get smart sampling integrated directly in Grafana Cloud Traces. And with the new volumetric policy in Adaptive Traces, you can further optimize what you keep so you can save money and still get a diverse, representative set of traces.The volumetric policyThe volumetric policy dynamically chooses the best traces for your sampling budget. It does this intelligently and under the hood, ensuring fair representation for all of your services, and removing the toil of creating and maintaining bespoke sampling policies. All you have to do is specify a percentage target, and the rest is done for you.Below, we’ll talk about how we arrived at this policy, and why you need to make it part of your sampling strategy.What's wrong with doing probabilistic sampling?Probabilistic sampling is the easiest way to sample a random subset of traces because it uses the traceID to capture a random and uniform subset of traces. It's a very simple, widely adopted technique that can be run at any point in the …

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