Break the link between data volume and your observability bill with Adaptive Telemetry

Grafana Cloud’s Adaptive Telemetry aims to reduce observability costs by retaining data according to its operational value rather than simply limiting collection or retention as telemetry volumes grow, particularly in AI-driven systems. New Adaptive Traces features include volumetric sampling to distribute samples across services and request types, diversity sampling to preserve representative attribute combinations, and 24-hour trace-ID retrieval for dropped traces, which together can reduce trace volume substantially while retaining useful diagnostic coverage. Adaptive Logs can filter unwanted logs directly at collectors through Fleet Management to reduce egress and storage costs, while archive and replay capabilities store lower-value logs in customer object storage for later retrieval. Adaptive Metrics now offers Auto-Apply, which automatically implements aggregation recommendations under coverage-first or cost-first settings, reducing manual review work. Grafana Labs cites Rapid7 as an example of a customer using these capabilities to lower metrics costs, expand logs and traces coverage, and improve query performance while reinvesting savings in broader observability.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.