With Grafana Labs' Adaptive Telemetry suite, customers including Mux, SailPoint, TeleTracking, and Auditboard have cut telemetry spend by 30–50% on average, redirecting savings into deeper observability coverage
NEW YORK--(BUSINESS WIRE)--Grafana Labs, the company behind the open observability cloud, today 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 Adaptive Profiles now GA, 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.
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. AI gives teams more to watch, which means the cost of watching everything is rising sharply. Most organizations are already collecting far more data than they ever query, alert on, or act upon — AI-generated telemetry is making that gap wider, faster. Grafana Labs built the Adaptive Telemetry suite to address this directly: not by asking teams to manually cull their data, but by continuously analyzing how telemetry is actually used and surfacing precise recommendations for what to keep, aggregate, or drop.
"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."
Adaptive Profiles: Continuous Profiling at Scale, Without Runaway Costs
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 Profiles ensures that we can leverage Cloud Profiles without worrying about cost overruns,” said Michael Beltz, VP, Cloud Operations at Upland Software. “The ability to see where code is slowing down, memory is being allocated, and where improvements are needed [with Cloud Profiles] is necessary for us to reduce infrastructure resources and improve the user experience."
The Complete Suite: Savings Across Every Signal
Adaptive Profiles joins three generally available capabilities that have already delivered significant, measurable results across the Grafana Cloud customer base.
Adaptive Metrics is now the most widely deployed component of the suite and has helped customers eliminate 28.5 billion active series, delivering an average 35% reduction in metrics costs. One customer, Mux, was able to cut its metrics volume by 60% and extended retention from 14 days to 13 months.
"Adaptive Metrics is an amazing feature. It not only saves us hundreds of thousands of dollars a year, but it's also a forcing function for us to look closely at our metrics to find additional opportunities for time series reduction and cardinality improvements," said Kyle Weaver, Staff Software Engineer at Mux.
Adaptive Logs applies the same usage-based analysis to log data, identifying high-volume, low-value log patterns and generating recommendations for what can be safely dropped. Across Grafana Cloud, Adaptive Logs has eliminated 26 petabytes of log volume — data that teams were paying to store but never using. TeleTracking, an early adopter, has seen a 50% reduction in log volumes.
“Adaptive Logs helps reduce noise, making it easier to spot valuable logs and ultimately saves us costs,” said Andrew Qu, Software Engineer II at TeleTracking.
Adaptive Traces, which reached general availability in October 2025, uses tail sampling to ensure that traces with errors, high latency, or other signals of interest are always retained, while noise from healthy, repetitive spans is filtered out. Across Grafana Cloud, Adaptive Traces has reduced trace data volume by an average of 82%.
"Before Adaptive Traces, we had two bad options: send everything and blow our budget, or send so little we couldn't get meaningful insight," said Geoff Schultz, Manager, Infrastructure Engineering at Auditboard. "Now tracing is actually usable, we can dial sampling up or down as needed, keep costs in check, and still give teams the visibility they need."
Across the full Adaptive Telemetry suite, organizations using multiple components see 30–50% reductions in total telemetry costs on average.
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. Learn more at grafana.com and follow us on LinkedIn and X.
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