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
As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...
Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...
AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...
Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...
A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...
IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...
A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...
According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...
2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...
Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...