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Mon, 01 Jan 0001 00:00:00 +0000

Grafana Labs Ships Six Tools That Power Agentic Operations From Planning to Production

Mon, 27 Jul 2026 00:00:00 +0000

NEW YORK — July 27, 2026 — Grafana Labs, the company behind the open observability cloud, today announced the general availability of six AI capabilities during its inaugural AI Week, extending Grafana Assistant into an agentic operations layer that detects, investigates, and remediates production issues at the pace AI now creates them. The releases include Grafana Assistant Investigations, Grafana Assistant Workspace, Grafana Assistant Automations, the Grafana Cloud MCP server, gcx, and Grafana Agent Observability.

Observability has traditionally started after code reaches production: instrument it, dashboard it, alert on it, and hope you notice issues before your customers do. That timeline is moving earlier. Engineers are shipping more code, faster. Agents have dramatically increased the rate of change in many teams, and the practices that keep systems reliable must move earlier in the lifecycle to keep pace. According to Grafana Labs’ 2026 Observability Survey, 92% of practitioners say they’d get real value from AI catching anomalies, yet only 57% say they’re currently implementing observability for their own AI systems in any capacity. Grafana Labs’ answer to that gap is an assistant that brings high-quality insights from production telemetry right when you’re planning changes, not just after something breaks.

“We used to treat observability as something you bolt on just before code reaches production,” said Mat Ryer, Senior Director of AI, Grafana Labs. “That’s changing. Now, Grafana Assistant can review your plans before you’ve written a line of code, add the instrumentation for you once you have, watch features as they land in production, and stay with you as you scale while dealing with the inevitable incidents that follow. It’s the same assistant end-to-end, and it doesn’t just advise; it acts.”

Planning before a single line of code ships

Grafana Assistant Workspace, now generally available, gives that first conversation a home: chat history, live canvas, and investigation reports in one place instead of a sidebar. Bring in an architecture before a service exists, and Grafana Assistant flags where it won’t scale — before anyone opens an editor. Paired with Grafana Assistant Investigations, a running investigation becomes a shareable report, no rewriting required.

Turning a plan into instrumented, running code

Once a plan is in shape, Grafana Assistant helps engineers act on it without losing speed. Ask Grafana Assistant to instrument a new service, and it opens a pull request with the instrumentation in place, wires up the data source, configures Grafana, and watches until telemetry arrives, iterating with you if it doesn’t. Two GA releases power this stage: gcx and the Grafana Cloud MCP server.

gcx, Grafana’s agentic CLI for managing dashboards, alert rules, data sources, and other resources as code, brings self-managed Grafana and Grafana Cloud under one command line, with agent-friendly input and output for tools like Claude Code, GitHub Copilot, and Cursor, and full GitOps support for versioning dashboards and alerts in Git.

“Root cause analysis and dashboard creations are prompts in the Grafana Assistant side-panel that help us understand anomalies and correlations across signals that lead to speeding things up,” said Oren Lion, Director of Software Engineering, TeleTracking. “Now, with gcx, anyone can use Grafana Assistant in their coding agent, for example, to investigate flapping alerts, that’s democratizing…Grafana!”

The Grafana Cloud MCP server gives any MCP-compatible client, including Claude Desktop, Cursor, and more, direct access to a Grafana instance’s dashboards, alert rules, incidents, and data sources. No more pasting metrics into a chat window; the agent queries live telemetry from where the code is being written.

Grafana Assistant investigates while you stay in control

In production, the questions change. Instead of parsing raw metrics and logs, engineers can ask Grafana Assistant questions about their telemetry in plain language, learn what a signal is actually telling them, and plan their next move by correlating technical data with business outcomes.

Grafana Assistant Investigations and Grafana Assistant Automations are both now generally available, helping engineers get answers out of production faster than they can type their questions. When an incident happens, Grafana Assistant Investigations forms hypotheses about what went wrong and swarms over the problem to chase down the leads in the data, proving or disproving each one. Engineers can stay in the driver’s seat and steer the investigation themselves, or step back and wait the few minutes it takes for Grafana Assistant to finish and hand back a conclusion. Grafana Assistant Automations let a saved Grafana Assistant prompt run again automatically, on a schedule, or on demand, so recurring checks like a daily error rate summary are sent to your Slack channel without anyone re-typing the same question every morning.

“When I’m in Grafana, it’s never a leisurely thing. It’s always under pressure; trying to find answers to unexpected issues in a very reactive situation,” said Rob Kacic, Engineering Manager, MasterControl. “Grafana Assistant Investigations has been super helpful with that. I’ve had cases where it cut my time drastically; correlating backend errors and traces to specific front-end page IDs would have taken me hours, but with Grafana Assistant Investigations, it was a 15-minute task. Lowering that bar to entry has been the biggest win so far.”

Observing the AI agents that you build

Grafana Agent Observability, generally available today, extends Grafana Cloud’s OpenTelemetry-native monitoring to the AI systems teams are now shipping. It was born out of necessity: Grafana Labs needed to observe Grafana Assistant itself once usage started spiking past expectations. Instrumented agents emit telemetry that captures the usual signals (usage, latency, and errors) alongside two that matter specifically for AI systems: token usage/cost and the conversation itself. Conversation data gives teams forensic-level debugging down to the individual exchange. Built-in evaluators go a step further, letting teams introspect conversations to test a sample of conversations for the agent behavior directly, catching hallucinations, drift, or policy violations before a customer does.

“After two days in production [with Grafana Agent Observability], the difference was amazing,” said a spokesperson for Alter Domus. “We started collecting information about our MCP servers, prompts, and conversations, so now we can understand why MCP servers fail and why users are not achieving what they expect when interacting with those systems.”

“AI should allow you to move at 10x velocity, not produce incidents at 10x the rate,” Ryer said. “That’s why we’re releasing all these AI capabilities in a single week. Engineers shouldn’t have to wait for their tools to catch up to how quickly they’re already moving.”

Availability

Assistant Investigations, Assistant Workspace, Assistant Automations, the Grafana Cloud MCP server, gcx, and Agent Observability are all generally available today.
Try the tools with Grafana Cloud, including the free-forever tier.

Grafana Labs Named a Leader in 2026 Gartner® Magic Quadrant™ for Observability Platforms and Positioned Furthest in Completeness of Vision

Wed, 15 Jul 2026 00:00:00 +0000

NEW YORK — July 15, 2026 — Grafana Labs, the company behind the open observability cloud, today announced it has been named a Leader in the Gartner® Magic Quadrant™ for Observability Platforms for the third consecutive year, and positioned furthest on the Completeness of Vision axis for the second year running.

We believe this placement reflects where the market is headed: toward open, composable observability that helps teams in the AI era understand systems that are increasingly complex and agentic, and rapidly scaling thanks to AI-assisted engineering.

A Platform Built for the AI Era

According to Grafana Labs’ 2026 Observability Survey, operational complexity and overhead are now the

top observability challenge, even as adoption of managed observability continues to climb, with half of organizations now using managed observability in some form (up from 43% in 2025). Telemetry keeps growing, systems keep getting more distributed, and teams are tired of paying twice: once for data they do not need, and again in engineering time when something breaks, and nobody can see the full picture. What changed in the last year is the scale of that problem. AI has become production infrastructure with its own failure modes, cost curves, and accountability questions, while the people operating these systems still need to move faster than dashboards alone allow.

Observability has to serve both sides of the AI shift. Teams need to observe AI, from LLMs and agents to inference pipelines and the applications wrapped around them. And they need observability that uses AI responsibly to shorten investigations, not replace judgment. That demand is showing up across energy, manufacturing, media, and retail, and increasingly among AI-native companies running production AI workloads on Grafana Cloud.

That’s why over the past year, Grafana Labs expanded Grafana Cloud to help teams run complex systems, observe AI in production, and use AI to move faster on an open platform where observability spend scales with value, not volume.

AI inside the platform: Grafana Assistant puts a conversational AI layer inside Grafana Cloud that answers questions about your systems using the telemetry you already have, enriched by the Knowledge Graph, which connects signals to services, dependencies, and changes. Assistant Investigations goes further, automatically correlating alerts and surfacing root causes before an on-call engineer finishes reading the page.
Built for agents and humans: Grafana Cloud CLI (gcx) and a remotely hosted MCP server give AI agents native access to Grafana Cloud, the same capabilities engineers use in the UI, at machine speed. Skills extend that production context into coding tools like Claude Code and Cursor.
Observability for AI systems: Grafana Cloud AI Observability brings the same discipline to LLMs and agents that Grafana has always applied to infrastructure: monitoring performance, token usage, latency, drift, and agent decision traces alongside the rest of the stack. Grafana Labs also recently announced o11y-bench, an open-source benchmark for evaluating AI agents on real-world observability and SRE tasks. Notably, in the 2026 Gartner® Critical Capabilities for Observability Platforms, which is a companion of Magic Quadrant report, and provides evaluation across Use Cases, Grafana Cloud scored 4.4/5 for AI/LLM Observability.
Cost control at AI scale: Adaptive Telemetry, across logs, metrics, traces, and profiles, lets teams keep high-value signals while cutting noise and optimizing costs as AI workloads add new data types and volume.
Open by design: In the AI era, openness is a practical requirement: agents are only as good as the context they can reach. Grafana is built on OpenTelemetry, open APIs, and MCP interfaces, so your telemetry stays yours and is accessible to humans and agents alike.

“We believe being recognized as a Leader for the third year running — and furthest in Completeness of Vision for the second — reflects not just where we are today, but where the market is heading, and I am extremely proud of the organisation’s continued ability to innovate and adapt to meet our users’ needs,” said Anthony Woods, Co-Founder, Grafana Labs. “Observability is no longer just about metrics, logs, and traces. It is about intelligence, automation, and giving every team the ability to understand their systems at any scale. That includes the teams operating AI in production and the AI-native companies building the next generation of applications on top of it. Our job is to meet both with an open platform, actually useful AI workflows, and the visibility that their complex systems have been missing.”

What Customers Are Saying

Customers span various industries and sectors, including Anthropic, ASOS, Citigroup, Lovable, NVIDIA, Salesforce, and Microsoft, which rely on Grafana to monitor and understand complex systems at a global scale. While we are honored to be named a Leader by Gartner, the most important feedback comes from the people using Grafana every day.

As of June 25, 2026, Grafana Labs holds an overall rating of 4.5 out of 5 on Gartner Peer Insights™, based on 618 verified customer reviews, with 90% of reviewers saying they would recommend Grafana Labs products. Here’s what customers are saying:

“Our transition to Grafana Cloud has been a significant force multiplier for our DevOps team. By moving away from managing our own backend storage, we’ve reclaimed valuable engineering hours.” — IT Manager, Energy & Utilities Industry
“Grafana [Cloud] provides an amazing single pane of glass for our full observability stack.” — Solutions Engineer, IT Services Industry
“The AI agent and assistant are amazing. Adaptive Metrics let[s] us control metrics [on] the server side.” — IT Manager, Services Industry
“I really like how easy it is to use. I also think it’s a great product with rich features like the SRE AI Agent and on-call functions.” — Software Developer, $30B+ IT Services Company
“One of my best observability tools at this point. Grafana Cloud fits all my observability needs, from dashboards, to explore, to alerts.” — Engineer, Media Industry

Resources

Read the full Gartner Magic Quadrant report here
Try Grafana Cloud today, using the generous free tier

Gartner Disclaimer

Gartner, Magic Quadrant for Observability Platforms, Padraig Byrne, Pankaj Prasad, Martin Caren, D.B. Cummings, Tanmay Bisht, July 2026.

Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.

This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Grafana Labs.

GARTNER, Magic Quadrant and Peer Insights are trademarks of Gartner and/or its affiliates.

Gartner Peer Insights content consists of the opinions of individual end users based on their own experiences with the vendors listed on the platform, should not be construed as statements of fact, nor do they represent the views of Gartner or its affiliates. Gartner does not endorse any vendor, product or service depicted in this content nor makes any warranties, expressed or implied, with respect to this content, about its accuracy or completeness, including any warranties of merchantability or fitness for a particular purpose.

AI and Next-Gen Technology Companies Choose Grafana Cloud to Bring Intelligence to Their Own Infrastructure

Thu, 18 Jun 2026 13:00:00 +0000

NEW YORK — June 18, 2026 — Grafana Labs, the company behind the open observability cloud, today announced fast-scaling AI and technology companies — 7AI, TeamSystem, and Zama — are standardizing on Grafana Cloud as they scale AI-era infrastructure. Joining AI industry leaders like Anthropic and Lovable, these organizations are embracing a new model for observability that must keep pace with how fast models, data, and systems move in production.

Findings from Grafana Labs’ 4th Annual Observability Survey reinforce the shift: operational complexity and overhead are now the top observability challenge, while adoption of managed/SaaS observability continues to climb: half of organizations use managed solutions in some form (up from 43% in 2025). Exclusive SaaS use has also grown (10% in 2024 to 17% in 2026). That shift reflects growing pressure from AI workloads, which spike telemetry volume just as teams are trying to move away from fragmented, self-hosted stacks.

Existing customers like Harrison.ai highlight why AI-driven organizations are turning to Grafana Cloud. “Running AI in production means there’s no margin for blind spots,” said Graham Bucknell, Platforms Engineering Manager at Harrison.ai. “Grafana Cloud gives us end-to-end visibility across our systems, so our teams can understand what’s happening, act quickly, and scale safely. That transparency lets us move fast without compromising reliability or trust.”

TeamSystem Aims to Align Observability Cost With Value

As observability data volumes continue to surge, especially at the scale AI leaders like TeamSystem operate at, organizations are prioritizing more intelligent ways to manage cost without sacrificing visibility. While 90% of survey respondents expect their observability spending to stay the same or increase next year, they are becoming far more deliberate in how that budget is allocated, with 65% now citing cost as a key factor in tool selection.

This shift is driving demand for adaptive approaches that prioritize high-value signals over sheer data volume. TeamSystem is leveraging Grafana Cloud’s Adaptive Telemetry suite to reduce unnecessary data ingestion, control spend, and maintain deep visibility where it matters most. By aligning telemetry with actual usage and impact, they are improving both efficiency and performance without losing any insights.

Zama Puts Grafana Assistant to Work

For Zama, maintaining the performance edge of its FHE libraries and Zama Protocol requires granular, continuous benchmarking. The team has integrated Grafana Cloud as the primary source of truth for its automated performance testing suites. This setup allows Zama engineers to monitor regressions across 54 unique FHE operations and blockchain transaction types in real-time. By correlating telemetry with algorithmic updates and hardware-level trade-offs, Zama uses these insights to iterate on implementation efficiency. This data-driven approach to optimization is a core component of Zama’s roadmap to achieving 100 TPS on the Zama Protocol by the end of the year.

Zama also uses Grafana Assistant to accelerate investigation, reduce time to resolution, and make observability more accessible across engineering teams. By embedding actually useful AI directly into the observability experience, Grafana Assistant helps teams quickly understand complex systems, surface relevant insights, and onboard new engineers faster. For high-growth organizations operating at scale, this translates into faster debugging, improved productivity, and more confident decision-making.

7AI Migrates from OSS to Grafana Cloud

7AI, a fast-scaling AI company, is migrating from self-managed open source observability stacks to Grafana Cloud. At their scale, maintaining bespoke tooling competes directly with engineering capacity for core innovation. Rather than investing in infrastructure upkeep, these teams are standardizing on a managed platform that frees engineering time for product work while preserving deep visibility across production AI workloads.

“AI companies need observability that keeps pace with them,” said Anthony Woods, Co-Founder, Grafana Labs. “These organizations are running some of the most complex and fast-moving infrastructure in the world, and they’ve chosen Grafana Cloud because it gives them a single platform that unifies their telemetry, controls their costs, and puts AI to work in the places that matter, from root cause analysis to onboarding new engineers. This is what the next generation of observability looks like.”

PointsBet Goes All In on Grafana Cloud to Power AI-Driven Observability at Scale

Mon, 08 Jun 2026 23:00:00 +0000

SYDNEY – JUNE 9, 2026 — Grafana Labs, the company behind the open observability cloud, today announced that PointsBet, one of Australia’s fastest-growing digital wagering operators, has selected Grafana Cloud as its unified observability platform. PointsBet is using Grafana Cloud to consolidate telemetry across its proprietary betting platform, accelerate incident resolution with AI-powered insights, and give engineering teams the visibility they need to own and operate their services with confidence.

“Our platform is our product. Grafana Cloud gives us one place to see everything — and the AI tools to act on it fast.” – Daniel Lucas, CTO, PointsBet

Grafana Cloud was selected for its ability to deliver:

Unified Observability Across Every Data Source: PointsBet’s proprietary platform spans real-time odds calculation, player account management, front-end apps, and a custom-built betting engine, all generating high-volume telemetry from multiple sources. With Grafana Cloud, PointsBet can ingest and correlate metrics, logs, traces, and profiles in a single open platform, ending the fragmentation that slows incident response. Built on OpenTelemetry and open source foundations including Grafana Loki, Grafana Tempo, and Prometheus, there’s no vendor lock-in — just a unified view of the stack. This flexibility is what enables PointsBet’s shift toward a true service ownership model: engineering teams can now observe, understand, and act on what they build.
AI That’s Actually Useful: Grafana Assistant gives PointsBet engineers a context-aware AI co-pilot for investigation and troubleshooting, letting them query telemetry in natural language, navigate dashboards, and trace issues to root cause without deep expertise in PromQL, LogQL, or TraceQL. Now generally available in Grafana Cloud, Grafana Assistant can run multi-step incident investigations, generate and refine queries on the fly, and surface the right data at the right moment — keeping every action inside the tools teams already use. For a business where live betting windows close in seconds, reducing time-to-resolution isn’t a nice-to-have; it’s a competitive edge.
Application Observability That Empowers Developers: Grafana Cloud Application Observability provides PointsBet’s teams with end-to-end visibility into how their services perform — surfacing service dependency maps, latency hotspots, and the customer impact of every change. By connecting distributed traces, metrics, and logs in a unified view, Application Observability helps teams understand not just that something broke, but why and who is affected.

“Observability used to mean drowning in dashboards, alert noise and waiting for someone else to tell you what’s on fire,” said Saurabh Vyas, Head of SRE, PointsBet. “We chose Grafana Cloud because it brings technology and commercial teams together on the single view building autonomous value streams — and Grafana Assistant means our engineers spend less time asking ‘what’s wrong’ and more time fixing it. It enables the shift from reactive firefighting to teams that genuinely own their services end to end and that helps us build a platform our customers can reliably bet on.”

“Real-time platforms at scale are some of the hardest systems to operate — every component has to perform under pressure, and every signal matters when something goes wrong,” said Anthony Woods, co-founder, Grafana Labs. “PointsBet’s engineering team has built a sophisticated platform, and we’re proud to give their engineers the observability foundation they need to operate it. Open, AI-powered, and built to cut through complexity — that’s exactly what Grafana Cloud is for.”

Learn more:

Explore Grafana Cloud Application Observability
Get started with Grafana Assistant
Learn about AI and observability in Grafana Cloud

Grafana Labs Launches Grafana 13 at GrafanaCON 2026, Makes Open Observability Easier to Run at Scale

Tue, 21 Apr 2026 00:00:00 +0000

BARCELONA, SPAIN — April 21, 2026 — Grafana Labs, the company behind the open observability cloud, today unveiled Grafana 13 and a wave of open source updates at GrafanaCON 2026, anchored by a next-generation Grafana Loki architecture and simpler paths to OpenTelemetry on Linux and Kubernetes.

According to Grafana Labs’ 2026 Observability Survey, more than 77% of organizations now lean on open source/open standards for observability, yet more than 38% of teams still cite complexity as their top challenge. The updates announced at GrafanaCON are aiming to address that tension: keep the openness, lose the friction.

“We’re seeing a clear shift in how organizations think about observability. It’s no longer about choosing a single vendor; it’s about building on open foundations,” said Anthony Woods, Co-Founder, Grafana Labs. “Open source, open standards, and an open ecosystem give teams the control and flexibility they need in a world that’s only getting more complex. The future of observability will be defined by interoperability and community-driven innovation, not closed systems. What we’re announcing at GrafanaCON today helps make that open model not just possible, but practical at scale.”

Grafana 13: From Insight to Action, Faster

Grafana 13 focuses on helping teams move from raw telemetry to actionable insight more quickly. Key updates include:

Faster time-to-value through suggested dashboards, dashboard layout templates supporting standard methodologies like DORA and USE/RED method that reduce the blank-page problem, as well as guided learning paths for onboarding new and growing teams.
Dynamic dashboards, now generally available, allow dashboards to adapt based on variables, context, and user needs instead of multiplying static copies.
Programmability and governance at scale, 2-way Git workflows (supporting GitHub, GitLab, Bitbucket, and git) based on a redesigned dashboard schema and a versioned dashboard API, improved secrets handling, and dashboard restore, as well as advisory tooling for safer change management.
Expanded ecosystem support, with more than 170 data sources and 120 visualization panels, plus continued guidance on repeatable patterns and best practices.

Loki: Architecture for the Next Wave of Log Workloads

As observability data continues to evolve, Grafana Labs is also rethinking how logs are stored and queried at scale. The rise of structured logs and OpenTelemetry has fundamentally changed how teams use logs, shifting from simple search toward more analytical, high-cardinality queries, making it critical to rethink the system design to address performance bottlenecks and increasing infrastructure costs.

To address this, Grafana Labs introduced a major evolution of Grafana Loki, designed for modern log use cases and next-generation scale, specifically:

Kafka-backed ingestion for more efficient, durable pipelines at the ingestion layer.
A redesigned query engine and scheduler to better handle large-scale analytical workloads. A new query planner will distribute work across partitions and execute queries in parallel, optimizing for data locality and maximizing throughput, and allowing Loki to process significantly less data per query while returning results faster.

Together, these changes deliver up to 20x less data scanned and 10x faster performance on aggregated queries, making it possible to answer complex questions across massive log datasets with far greater efficiency.

To further accelerate Loki’s evolution, Grafana Labs also announced the acquisition of Logline, an early-stage company founded by tenured engineering leader and entrepreneur Jason Nochlin, focused on performant search of large-scale log data. Logline’s technology is designed to efficiently power “needle in the haystack” queries, such as searching for a specific user ID or error identifier across massive datasets, one of the most common and challenging use cases in log analysis. By bringing this capability into Loki, Grafana Labs aims to significantly improve precision search performance while maintaining Loki’s cost-efficient, index-light architecture.

OpenTelemetry: Easier to Install, Easier to Run

Grafana Labs continues to invest in an open observability model built on open standards and a broad ecosystem, anchored by Prometheus and OpenTelemetry.

According to Grafana Labs’ 2026 Observability Survey, a majority of organizations are using OpenTelemetry or are actively migrating toward it, signaling a clear industry shift toward vendor-neutral instrumentation. But while adoption is accelerating, many teams still face challenges around complexity, evolving semantic conventions, and operational overhead.

To reduce these barriers, Grafana Labs engineers are working with the broader community to help make OpenTelemetry easier to install and operate, including:

Integrated OpenTelemetry packages for Linux environments, enabling installation with a single command, and enhanced support for Kubernetes through the OpenTelemetry Operator.
A more unified experience through Grafana Alloy, the company’s distribution of the OpenTelemetry Collector. In a recent OpenTelemetry community survey, Grafana Alloy was the most cited vendor distribution of the OpenTelemetry Collector. With the new OpenTelemetry Engine mode, teams can now configure Alloy using standard OpenTelemetry Collector YAML, enabling fully OpenTelemetry-native pipelines seamlessly integrated with Grafana.

Grafana Labs continues to contribute upstream to improve the stability of instrumentation, semantic conventions, and distributions, helping make OpenTelemetry more consistent, interoperable, and production-ready across the ecosystem.

GrafanaCON 2026: Innovation Across the Open Observability Ecosystem

GrafanaCON 2026 brings together the global community behind Grafana’s 35 million users worldwide, with thousands of engineers, SREs, and developers gathering in Barcelona to share how they use observability in practice. The event goes beyond product announcements, showcasing real-world applications of open observability across industries.

This year’s agenda features user-led sessions highlighting observability in action, including:

“GrafanaCON is where the future of observability gets built in the open,” said Torkel Ödegaard, Co-Founder, Grafana Labs. “What you see in these announcements is a direct result of that collaboration. Everything we build is shaped by how people actually run these systems at scale. We continue to invest in open source because it’s the most effective way to solve hard technical problems together and push the entire ecosystem forward.”

Resources

Read more about Grafana 13 and other product announcements from GrafanaCON
Explore the GrafanaCON 2026 agenda and sessions
Read the blog announcing Pyroscope 2.0 and how it provides faster, more cost-effective continuous profiling at scale
Learn more about the new Grafana Marketplace, making it easier for partners to sell and distribute plugins developed for Grafana

Grafana Labs Targets the “AI Blind Spot” with New Observability Tools Announced at GrafanaCON 2026

Tue, 21 Apr 2026 00:00:00 +0000

Barcelona, Spain — April 21, 2026 — Grafana Labs, the company behind the open observability cloud, today announced a set of new AI-focused capabilities at GrafanaCON 2026: AI Observability in Grafana Cloud; a significant expansion of Grafana Assistant into more environments, as well as new agentic capabilities; the Grafana Cloud CLI (GCX), a new agentic interface for automated and agent-driven workflows; and o11y-bench, a new open source benchmark for evaluating AI agents running observability workflows.

AI is transitioning from experimentation to production, while observability, control, and operational trust are still catching up. Grafana Labs’ 2026 Observability Survey found near-universal interest in AI’s value, alongside real caution about autonomy: 15% of respondents expressed skepticism about AI taking autonomous actions without stronger safeguards. Now, observing LLMs and the systems they touch is becoming table stakes for teams that intend to run them safely and reliably at scale.

“AI systems are starting to look a lot like distributed systems did a decade ago: powerful, but difficult to reason about and even harder to operate,” said Jen Villa, Senior Director of Product, Grafana Labs. “We’re not approaching this as a separate category. The goal is to bring the same level of visibility and control to AI that teams already expect from the rest of their stack.”

Introducing AI Observability in Grafana Cloud: Monitor and Evaluate AI Systems in Real Time

Launching in Public Preview, AI Observability in Grafana Cloud is a complete solution designed to help teams monitor and evaluate LLM-powered applications and agents in real time.

As AI becomes embedded in customer-facing experiences, failures often don’t look like classic telemetry: unexpected outputs, inconsistent behavior, and silent degradation that erodes trust before traditional dashboards light up.

AI Observability in Grafana Cloud is built to close these visibility gaps by helping teams:

Observe AI agent behavior in real time, including inputs, outputs, and execution flows.
Continuously evaluate outputs, with alerts for issues such as low-quality responses, policy violations, or anomalous behavior.
Surface risk earlier, including potential data exposure or misuse (for example, leaked credentials or abnormal usage patterns).
Elevate agent sessions and conversations to first-class telemetry signals and correlate them in the same environment where applications are observed.

Teams can get started with AI Observability in Grafana Cloud today to understand what their AI is doing, how well it’s doing it, and where issues are emerging.

Grafana Assistant: Broader Reach, Deeper Workflow Support

Grafana Labs also announced a significant expansion of Grafana Assistant, its AI-powered agent for observability and operational workflows that helps monitor, troubleshoot, and manage systems through natural language conversations.

Assistant is no longer limited to Grafana Cloud; it is extending to additional environments and surfaces, including on-premises deployments of Grafana Enterprise, so teams with stricter data and control requirements can use the same AI-assisted workflows. Grafana open source users will also be able to use Grafana Assistant by connecting their accounts to a Grafana Cloud instance.

Additional new capabilities coming to Grafana Assistant include:

Assistant Workspace: Bring Grafana Assistant into full-screen, chat, and browse visualizations at the same time.
Assistant API: Call Grafana Assistant in workflows from anywhere and move the Assistant into your stack.
Automations: Schedule tasks and automation workflows, enabling routine operational actions to run without manual intervention.
Remote MCP server: Bring any agent and connect it to Grafana’s remote MCP server
Learn mode: Get personalized, hands-on lessons tailored to your role and infrastructure so you can elevate your skills.
And so much more: Grafana Assistant in Microsoft Teams, 50+ integrations, 15 native data source integrations, Python runtime in the Assistant, and EU-preferred inference for European customers.

The focus is shortening the distance from question to grounded investigation, especially when minutes matter. It is not just a generic chat interface.

To get started with Grafana Assistant, Grafana Enterprise and Grafana OSS users can create a Grafana Cloud account (including the actually useful free forever plan) and connect it to their Grafana installation via a one-click setup.

Grafana Cloud CLI (GCX): Observability Where Agents Already Work

Grafana Labs also introduced the Grafana Cloud CLI (GCX), a new interface designed for a shift already underway in how software is built and operated: engineers are increasingly working through AI-assisted development environments like Cursor, Claude Code, and GitHub Copilot, where the agent becomes the primary interface.

GCX is designed to bring Grafana Cloud into that workflow, so teams can:

Access the full Grafana Cloud surface area agentically, including provisioning, configuration, and telemetry querying
Invoke Grafana Assistant capabilities from the dev environment, without context-switching into separate tools
Close the agentic loop between code and production by using agents to query live observability insights, correlate alerts with recent repository changes, and propose fixes without leaving the development environment to create a continuous feedback cycle where observability data drives the next action

The intent is fewer handoffs between code, alerts, and dashboards, so investigation and remediation can stay closer to where changes ship. Download GCX and get started today.

o11y-bench: An Open Benchmark for Observability Agents

Grafana Labs also announced it is open sourcing o11y-bench, a benchmark for evaluating AI agents on observability workflows.

Built on Harbor and designed to run against a real Grafana stack, o11y-bench is intended to help teams measure how agents perform on the kinds of tasks that matter in practice: querying metrics, logs, and traces; investigating incidents; and making targeted dashboard changes.

In modern observability environments, where teams operate across open tools and multiple telemetry types, evaluating AI agents requires more than reviewing outputs. o11y-bench is designed to reflect that reality by measuring what agents actually do in the system, not just what they say.

Learn more at https://o11ybench.ai/

Building the Foundation for AI in Production

Together, these moves reflect a pattern: AI in production adds complexity that traditional tooling only partly covers. To coordinate work across AI observability, assistant experiences, and agent-driven workflows, Grafana Labs is forming a dedicated AI organization unifying these efforts under one team.

Mat Ryer has been appointed Senior Director of AI and will lead this work across the company.

“AI breaks in ways traditional observability wasn’t designed for,” said Mat Ryer, Senior Director of AI at Grafana Labs. “Latency and errors still matter, but they’re not enough. You also need visibility into correctness, consistency, and shifting agentic behaviours over time. AI is quickly becoming a key part of the way teams investigate and operate systems. We want to make all of that observable in a way that’s practical, reliable, and fits into how engineers already work today.”

Resources

Read more about all the other product announcements from GrafanaCON.
Explore the GrafanaCON 2026 agenda and sessions.

Grafana Labs Lleva la Observabilidad con IA a América Latina con el Evento Observability Sessions Santiago

Wed, 08 Apr 2026 12:00:00 +0000

SANTIAGO, CHILE — 8 de abril de 2026 — Grafana Labs, la empresa detrás del open observability cloud, trae su serie de eventos Observability Sessions a Santiago el 15 de abril, conectando a líderes de ingeniería, socios y clientes de toda América Latina para explorar la próxima frontera de la observabilidad impulsada por inteligencia artificial. El evento llega en un momento de fuerte impulso regional para Grafana Labs, que ha incrementado su equipo local en un 30% durante los últimos dos años para atender la creciente demanda de soluciones de observabilidad abiertas y escalables. Organizaciones de toda América Latina —incluyendo LATAM Airlines, Casas Bahia y Hona— están adoptando Grafana Cloud para gestionar mejor sistemas distribuidos complejos, controlar costos y aplicar IA para acelerar la resolución de problemas y la respuesta a incidentes.

“Consideramos hospedar la versión de código abierto de forma propia, pero al calcular el tiempo de ingeniería necesario para la configuración y el mantenimiento en comparación con Grafana Cloud, Grafana Cloud tenía mucho más sentido”, afirmó Matías Delgado, Staff Software Engineer en Hona. “Grafana Cloud nos permite entender nuestros servicios, crear SLOs reales y resolver problemas antes de que afecten a los clientes. Se ha vuelto esencial para nuestra forma de desarrollar.”

Durante el evento, los asistentes escucharán directamente de profesionales regionales y expertos de Grafana Labs a través de análisis técnicos en profundidad, sesiones lideradas por clientes y demostraciones prácticas. Los socios del evento incluyen: Nixe y LIDD.

“América Latina, especialmente en la región sur, es una región de rápido crecimiento para Grafana Labs, y estamos viendo cómo los equipos avanzan rápidamente desde la adopción de la observabilidad hasta la operacionalización de la IA dentro de sus flujos de trabajo”, dijo Fernando Paredes, Senior Enterprise Account Executive en Grafana Labs. “Observability Sessions Santiago es una oportunidad para reunir a la comunidad, compartir experiencias del mundo real y explorar lo que viene.”

Observabilidad Impulsada por IA: El Cambio hacia lo Agéntico

El lanzamiento de Grafana Assistant, ahora disponible de forma general en Grafana Cloud, marca un avance significativo en cómo los equipos de ingeniería interactúan con sus datos de observabilidad. En lugar de escribir consultas complejas o navegar entre herramientas, los usuarios pueden hacer preguntas en lenguaje natural y recibir respuestas contextuales y accionables. Assistant Investigations, ahora en vista previa pública, va un paso más allá: actúa como un agente autónomo y de múltiples pasos que coordina métricas, logs, trazas y perfiles para identificar causas raíz y recomendar acciones de remediación durante los incidentes.

Los datos de la Encuesta de Observabilidad 2026 de Grafana Labs reflejan el enorme potencial que estos avances representan para los equipos de ingeniería de América Latina. En toda la región, los profesionales identifican el análisis de causa raíz y correlación como el área de mayor valor potencial para la IA, con más del 61% de los encuestados de América del Sur describiéndolo como el caso de uso donde la IA podría generar mayor impacto.

Estas cifras señalan que las organizaciones en América Latina no solo están adoptando herramientas de observabilidad; están apostando por la observabilidad aumentada con IA como una prioridad estratégica.

Regístrese en Observability Sessions Santiago para conocer cómo los principales equipos de ingeniería de América Latina están aplicando la IA para mejorar la confiabilidad y acelerar la respuesta a incidentes.

Grafana Labs' 4th Annual Observability Survey Reveals a Field at a Crossroads: AI, Economics, Complexity, and the Enduring Power of Open Source

Wed, 18 Mar 2026 10:00:00 +0000

NEW YORK — March 18, 2026 — Grafana Labs, the company behind the open observability cloud, today released findings from its fourth annual Observability Survey, its largest ever, drawing responses from more than 1,300 practitioners and leaders across 76 countries. The survey paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all.

Among the key findings:

92% see value in AI helping surface anomalies and issues before they cause downtime
38% say complexity and overhead are their biggest observability concern
77% say open source or open standards are important to their observability strategy
77% say centralized observability has saved their organization time or money
Half of organizations now use observability tools to track business-related metrics

Together, the results point to a clear industry direction: organizations want observability solutions that are open, cost-efficient, and capable of delivering meaningful operational insights without adding complexity.

Practitioners Want AI That Earns Its Place, Not AI for AI’s Sake

The survey makes clear that observability practitioners are open to AI, but on their terms. Across a range of use cases, support is overwhelming: 92% see value in AI surfacing anomalies before they cause downtime as well as generating dashboards, alerts, and queries, while 91% endorse AI for forecasting and assisting with root cause analysis. Autonomous actions garner 77% support, but stand out as the highest area of skepticism: 15% don’t yet trust AI to act on their behalf and another 8% see no value in using AI for this.

The No. 1 barrier to AI adoption? Too much manual input of required context (26%). In other words, practitioners don’t want AI that creates new toil in place of old toil. And 95% say it’s important for AI to show its reasoning, the clearest possible signal that transparency is not optional. Notably, those who are most enthusiastic about AI are also the most insistent on explainability.

“The survey is clear: AI belongs in the observability workflow, autonomy is the next frontier, and explainability is the price of admission,” said Marc Chipouras, VP of Emerging Products at Grafana Labs. “Practitioners want trustworthy AI that reduces toil and helps them move faster.”

SaaS Adoption Surges as Organizations Invest for ROI, Not Just Growth

The economics of observability are shifting. Half of all respondents now use SaaS for observability in some capacity (up from 43% in 2025). The share using SaaS exclusively has grown steadily from 10% in 2024 to 17% in 2026, a clear signal of market maturation and growing confidence in managed services.

Spending is rising, but thoughtfully. Half of respondents expect to spend more on observability next year, not because vendor prices are going up (only a quarter of respondents cite this), but because of broader adoption (63%) and expectations of higher ROI (31%). Those who expect to spend less point to more efficient operations (37%) as the reason. Meanwhile, cost remains the single most important tool selection criterion for the third year running (65%), followed by ease of use (49%).

The message to vendors is clear: organizations are willing to invest, but they expect demonstrable value in return.

Complexity Remains the Industry’s Defining Challenge — and Centralization Is Helping

Complexity and overhead topped the list of observability concerns for 2026, cited by 38% of respondents, more than signal-to-noise challenges (34%) or cost (31%). Alert fatigue remains the biggest single obstacle to faster incident response, cited by 30% of respondents, nearly double the next most common response.

Yet there is genuine progress. More than three-quarters (77%) say they have saved time or money through centralized observability. Teams with mature, centralized practices are more satisfied with their internal operations (61%) compared to those with siloed setups (53%). The industry is also expanding its scope: nearly half (46%) of organizations have unified infrastructure and application observability in full production, and SLO adoption and business observability are both on the rise.

Self-managed teams are most likely to cite complexity as their top concern, while SaaS users are more likely to point to cost. The shift to SaaS, in part, is a direct response to the complexity burden, a trend expected to accelerate.

Open Source Remains the Bedrock while OpenTelemetry Is Coming Into Its Own

For the fourth consecutive year, open source and open standards are foundational to how practitioners think about observability. 77% say open source/open standards are important to their observability strategy, with 61% calling them “essential” or “very important.”

Almost two-thirds (65%) of organizations are investing in both Prometheus and OpenTelemetry. While Prometheus maintains a slight edge in overall investment (77% vs. 76%), OpenTelemetry is showing stronger growth signals: more respondents are building POCs or actively investigating (35% vs. 18%), and a higher share report increased investment over the past year (47% vs. 42%).

OpenTelemetry is no longer niche. It is now in broad use across metrics (57%), traces (50%), and logs (48%). Practitioners cite ease of adoption (41%) and the freedom to switch vendors (37%) as the top reasons they are turning to OTel, a direct expression of the industry’s desire for openness and portability, not lock-in.

About the Report

The Grafana Labs 4th Annual Observability Survey is based on 1,363 responses from engineers, SREs, and technology leaders across 76 countries, collected through online outreach and industry events between October 1, 2025 and January 6, 2026.

The full report and interactive dashboards are available at: https://grafana.com/observability-survey/