Grafana Labs has announced the general availability of Adaptive Profiles in Grafana Cloud, completing the Adaptive Telemetry suite to span all four telemetry signals: metrics, logs, traces, and profiles. With this release, every layer of an organization's observability stack can automatically identify and retain high-value data while filtering out what doesn't matter, without manual tuning or engineering toil.
Adaptive Profiles GA completes Adaptive Telemetry suite covering metrics, logs, traces, and profiles.
Organizations using multiple suite components see 30-50% reduction in total telemetry costs on average.
Adaptive Metrics has eliminated 28.5 billion active series, delivering 35% average cost reduction.
Adaptive Logs has eliminated 26 petabytes of log volume across Grafana Cloud customers.
Adaptive Traces reduces trace data volume by average of 82% using intelligent tail sampling.
57% of organizations are implementing LLM observability, with 65% citing cost as top selection criteria.
Telemetry volumes are growing faster than the insight they generate, and AI is accelerating the problem. As organizations deploy AI agents and LLM-powered applications, every model call, tool invocation, and agentic workflow generates new telemetry that needs to be observed, traced, and profiled. According to Grafana Labs' 2026 Observability Survey, 57% of organizations are already implementing LLM observability in some capacity, and 65% cite cost as the top criteria for selecting observability tools. The Adaptive Telemetry suite addresses this directly by continuously analyzing how telemetry is actually used and surfacing precise recommendations for what to keep, aggregate, or drop.
Continuous profiling gives engineers deep insight into how applications consume CPU, memory, and other resources in production, but broad deployment across infrastructure has historically been cost-prohibitive. Adaptive Profiles changes this by dynamically adjusting the detail and frequency of data collection based on workload behavior. During normal operations, it collects at a cost-effective baseline. When anomalies or performance issues arise, it automatically increases resolution to ensure engineers have the data they need to investigate. For engineering teams that have struggled to justify fleet-wide profiling, Adaptive Profiles makes the economics work, delivering richer performance data where it matters without requiring a fixed high-cost collection rate across every service.
Adaptive Metrics has helped customers eliminate 28.5 billion active series, delivering an average 35% reduction in metrics costs. One customer, Mux, cut metrics volume by 60% and extended retention from 14 days to 13 months. Adaptive Logs has eliminated 26 petabytes of log volume across Grafana Cloud, with TeleTracking seeing a 50% reduction. Adaptive Traces uses tail sampling to ensure traces with errors or high latency are always retained while filtering noise, reducing trace data volume by an average of 82%. Across the full suite, organizations using multiple components see 30-50% reductions in total telemetry costs on average.
"The fundamental problem with observability economics today is that cost scales with ingestion, not insight," said Steven Dungan, Staff Product Manager at Grafana Labs. "Adaptive Telemetry inverts that model. Every signal: metrics, logs, traces, and profiles, now has an intelligent layer that learns how data is used in practice, and then optimizes automatically. With Adaptive Profiles reaching GA, we've closed the loop on the full stack. Teams get more signal, less noise, and lower bills and they don't have to sacrifice one for another."
About Grafana Labs
Grafana Labs, the company behind the open observability cloud, is founded on the principles of open source, open standards, open ecosystems, and open culture. Grafana Cloud, our fully managed observability platform, is flexible and built for scale. With Grafana Cloud's actually useful AI, organizations can see, understand, and act on all their disparate data to move at the speed of their ambitions, while getting the visibility they need to run AI systems reliably and at scale. Today, more than 35 million users and 7,000+ customers – including Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce – trust Grafana Labs to ensure reliability of their applications and systems, resolve incidents quickly, and optimize their telemetry to reduce noise and cost. We are a 100% remote company with 1,600+ team members across 40+ countries, and we're backed by leading investors including Lightspeed Venture Partners, Sequoia Capital, GIC, Coatue, J.P. Morgan, CapitalG, and Lead Edge Capital.
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