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

Grafana Cloud’s new volumetric policy for Adaptive Traces automates dynamic trace sampling to preserve a more diverse and representative dataset within a specified storage budget. Unlike flat probabilistic sampling, which can allow high-volume services, endpoints, regions, or customers to dominate retained traces, volumetric sampling analyzes trace attributes, selects useful combinations with manageable cardinality, groups traces into buckets, and continually adjusts each bucket’s sampling rate as traffic changes. Grafana reports that, at the same sampling percentage, this approach produces roughly 25% higher information density than probabilistic sampling, retaining more unique information per byte stored. The policy complements Adaptive Traces features such as anomaly detection, diversity sampling, and explicit retention rules for latency, status, or audit-related traces; users can adopt it through onboarding or convert existing probabilistic policies with one click, while dropped traces remain retrievable for 24 hours.

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