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Grafana Assistant in Grafana Cloud Introduced

Grafana Labs launched Grafana Assistant in Grafana Cloud in private preview.

Grafana Assistant in Grafana Cloud is a tightly integrated context-aware chat experience. It connects users to their observability data through a flexible interface that lets them ask anything, go places, make changes, and even run investigations in natural language.

Users new to the Grafana ecosystem can learn more about general concepts just by asking, and as they dig into specifics, the agent will drill into actual observability data available via Grafana to provide highly contextual answers to questions. More experienced users can run queries in natural language and even have data analyzed as part of a multi-step investigation.

Grafana Assistant appears as a sidebar within the Grafana interface, receiving context about the current page and providing relevant suggestions. Use cases for Grafana Assistant are limitless, but the team concentrated on a few core areas to start, ensuring it’s easy to interact with the agent through natural language to:

  • Ask questions about their observability data.
  • Navigate to specific views for metrics, logs, traces, or profiles.
  • Make bulk changes to dashboards.
  • Create new dashboards through natural language descriptions.
  • Perform multi-step investigations by following leads in their data.

"As the world's most ubiquitous visualization platform, Grafana is evolving to incorporate the latest technologies that are transforming our industry. With Grafana Assistant, we're making AI-powered observability a reality, not just as a concept but as a practical tool that helps users more quickly and easily diagnose issues, respond to incidents, build dashboards and alerts, and more – regardless of where their telemetry lives or how it's structured," said Tom Wilkie, CTO, Grafana Labs. “Grafana’s open source roots provide a unique advantage for our AI assistant; the wealth of content on the open web produced by our global community has enabled foundation models to be experts on Grafana, Prometheus, and Loki out-of-the-box. Our LLM-based agent was built to hit the ground running and provide meaningful assistance from day one.” 

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs 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 ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

Grafana Assistant in Grafana Cloud Introduced

Grafana Labs launched Grafana Assistant in Grafana Cloud in private preview.

Grafana Assistant in Grafana Cloud is a tightly integrated context-aware chat experience. It connects users to their observability data through a flexible interface that lets them ask anything, go places, make changes, and even run investigations in natural language.

Users new to the Grafana ecosystem can learn more about general concepts just by asking, and as they dig into specifics, the agent will drill into actual observability data available via Grafana to provide highly contextual answers to questions. More experienced users can run queries in natural language and even have data analyzed as part of a multi-step investigation.

Grafana Assistant appears as a sidebar within the Grafana interface, receiving context about the current page and providing relevant suggestions. Use cases for Grafana Assistant are limitless, but the team concentrated on a few core areas to start, ensuring it’s easy to interact with the agent through natural language to:

  • Ask questions about their observability data.
  • Navigate to specific views for metrics, logs, traces, or profiles.
  • Make bulk changes to dashboards.
  • Create new dashboards through natural language descriptions.
  • Perform multi-step investigations by following leads in their data.

"As the world's most ubiquitous visualization platform, Grafana is evolving to incorporate the latest technologies that are transforming our industry. With Grafana Assistant, we're making AI-powered observability a reality, not just as a concept but as a practical tool that helps users more quickly and easily diagnose issues, respond to incidents, build dashboards and alerts, and more – regardless of where their telemetry lives or how it's structured," said Tom Wilkie, CTO, Grafana Labs. “Grafana’s open source roots provide a unique advantage for our AI assistant; the wealth of content on the open web produced by our global community has enabled foundation models to be experts on Grafana, Prometheus, and Loki out-of-the-box. Our LLM-based agent was built to hit the ground running and provide meaningful assistance from day one.” 

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs 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 ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.