Grafana Labs, the company behind the open agentic observability cloud, today announced new releases at ObservabilityCON 2026 that help teams manage their telemetry in the AI era, finding the right log line in seconds and keeping only the telemetry worth paying for as AI workloads scale. Major query performance enhancements to Grafana Cloud Logs, built on technology from the company’s Logline acquisition, are now generally available, and Adaptive Logs Archive is in public preview.

AI is massively increasing telemetry volumes. Coding agents are turning everyone into a developer, so more software is being built, faster, and all of it needs to be watched. The agents add to the load too, since every agent call and tool call emits its own telemetry. Left alone, observability costs grow with every new workload, which makes cost and query optimization non-negotiable for running AI at scale. In the 2026 State of Observability Report, also released this week by Grafana Labs and ETR, a Futurum company, 78% of the 300 IT and engineering leaders surveyed increased observability spend last year and 73% expect to again, citing new products added to the stack (61%) and AI-accelerated development creating more to watch (60%).

“The economics of observability only work if you keep the data worth keeping and can dial that up or down as your system’s state changes, and that’s more true than ever with the explosion in telemetry driven by AI,” said Jen Villa, Senior Director of Product, Grafana Labs. “But even the data worth keeping is still massive, so you still need to be able to find what you’re looking for in it, fast. Adaptive Telemetry handles the first, and the query performance enhancements we’ve made in Grafana Cloud Logs handle the second.”

Find the one log line that matters, in seconds

Built on technology from Logline, which Grafana Labs acquired in April 2026, Cloud Logs now offers customers faster full-text search, at no additional cost.

This added functionality identifies logs with low frequency like request IDs and customer IDs over object storage, so an engineer paged at 1 a.m. can find the exact IDs in seconds, straight from the alert. Agents send far more queries than people do, so faster queries let agents and engineers move faster together.

“When I ran our first query on newly submitted logs after enabling Logline, the improvement was clear. We went from processing nearly a terabyte of logs in three minutes to around 100 gigabytes in under three seconds,” said Timo Kirzdörfer, OpenTelemetry & Grafana Cloud Admin, DATEV eG. “That represents an 80–90 percent reduction in the volume of logs searched for newly submitted logs, and we saw the results immediately.”

Keep only the data worth paying for

Grafana Labs’ Adaptive Telemetry suite optimizes cost across metrics, logs, traces, and profiles by recommending what to aggregate or sample, so teams keep the telemetry that matters. The result is better signal at a lower cost: customers using multiple components, including Mux, TeleTracking, and Auditboard, have cut telemetry spend by 30 to 50% on average. New updates cover logs, metrics, and traces:

  • Adaptive Logs Archive (public preview): Adaptive Logs Archive lets teams route logs to an object storage bucket they own in an open format. They get the cost savings of not putting those logs in Grafana Cloud, but still retaining them in low-cost storage in case they’re ever needed for an investigation, audit, or compliance request.

  • Adaptive Metrics Auto-Apply (GA): Platform teams no longer have to review cardinality-reduction recommendations one by one. Auto-Apply continuously applies recommendations without a human in the loop, while existing dashboards and alerts keep working unchanged. With Cost Mode, a built-in safeguard only applies recommendations that won’t increase your total active series, so optimization never turns into a cost spike.

  • Query traces dropped by Adaptive Traces (GA): Sample aggressively without worrying about losing the one trace you need. Developers can retrieve any trace by ID from the last 24 hours, even if Adaptive Traces dropped it, so cost savings never come at the expense of an investigation.

“Before, finding waste meant someone manually digging through metric cardinality and log volume, and the job never ended because new services kept adding more. Adaptive Metrics and Adaptive Logs now do that analysis for us,” said Carl Eastman, Principal Software Engineer, Rapid7. “They show which metrics and log patterns are never queried and recommend what to aggregate or drop, and we apply those recommendations instead of hunting for them. Our engineers spend their time on the few rules that need human judgment, like dropping debug logs we know are noise, rather than the whole long tail.”

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About Grafana Labs

Grafana Labs is the company behind Grafana Cloud, the fully managed observability platform trusted by more than 10,000 organizations to ensure reliability, resolve incidents faster, and optimize telemetry at scale. Built on open source and open standards and designed for interoperability across any stack, Grafana Cloud brings AI to observability and observability to AI, giving teams (and their agents) unified visibility so they can see, understand, and act on all their disparate data, wherever it lives, and move at the speed of their ambitions. Customers, including Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce, rely on Grafana Labs. We are a 100% remote company with team members across 40+ countries, 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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