Observing Themselves: How Grafana Labs Builds a Better Observability Platform by Being Its Own Most Demanding User

•6 min read
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In the complex world of software and infrastructure monitoring, having robust observability tools is paramount. For Grafana Labs, the company behind the ubiquitous Grafana dashboards and a suite of powerful open-source observability projects like Loki (logs), Mimir (metrics), and Tempo (traces), this isn't just a business—it's a daily operational reality. Grafana Labs "eats its own dogfood" at an impressive scale, running its massive, multi-tenant Grafana Cloud platform on the very software it develops and offers to the world. This profound internal usage is a cornerstone of their product development philosophy, driving innovation, resilience, and a deep empathy for their users' challenges.

Grafana Cloud: Powered by the "LGTM Stack" Itself

One of the most compelling testaments to Grafana Labs' commitment to its own technology is how it operates Grafana Cloud. Tom Wilkie, Grafana Labs CTO, has openly stated this: "We use our own Grafana Cloud products — [powered by] Mimir, Loki, Tempo, Grafana — to monitor our Grafana Cloud SaaS, and that's what drives the reliability." This isn't a siloed test environment; it's the real-time, production monitoring of a critical commercial service using the tools they build.

This practice offers several distinct advantages:

  • Real-World Stress Testing: Operating Grafana Cloud means dealing with immense scale, diverse workloads, and the constant need for high availability. By using Mimir for scalable metrics, Loki for log aggregation, and Tempo for distributed tracing to monitor this very service, Grafana Labs subjects its own tools to the most demanding conditions imaginable.
  • Identifying "Growing Pains" Firsthand: When performance issues or scalability challenges arise within their own infrastructure, Grafana Labs' engineers are the first to experience them. A Grafana Labs blog post on Tempo 2.7 candidly mentions, "As Grafana Cloud Traces — the fully managed distributed tracing system powered by Grafana Tempo — has grown, we have felt some internal growing pains related to operating enormous Tempo clusters, and are working to reduce resource consumption." This direct feedback loop from internal SREs and developers to the product teams is invaluable for driving meaningful improvements.
  • Deep Operational Empathy: By being operators of their own software at scale, Grafana Labs gains profound empathy for the challenges their users face. This understanding directly influences product roadmaps, feature prioritization, and the development of more intuitive and effective tools.

Meta-Monitoring: A Sign of Mature Internal Use

The concept of "meta-monitoring"—monitoring your monitoring systems—is a practice well understood at Grafana Labs. The Loki documentation, for example, advises users to monitor their Loki cluster using a separate Grafana, Loki, and Prometheus setup (ideally via Grafana Cloud). This recommendation itself stems from the operational wisdom gained by running these systems internally. It acknowledges that for true resilience, the system observing your primary observability stack needs its own independent line of sight. This type of nuanced, practical advice is born from real-world operational experience.

The "Big Tent" Philosophy, Tested Internally

Grafana Labs champions a "big tent" philosophy, aiming for its tools to be open, composable, and capable of integrating with a vast ecosystem of data sources and other observability components. This approach is also, by necessity, part of their internal reality. As they operate Grafana Cloud and integrate various open-source components and their own managed services, their internal teams are constantly testing and validating these integrations.

This ensures that when Grafana Labs promotes the interoperability of its stack, it's based on practical, internal validation. Their open-source roots and commitment to transparency mean that many of the challenges and solutions they discover internally often benefit the wider community through improved documentation, bug fixes, and new features in the open-source projects.

From Internal Feedback to Product Evolution

The feedback loop from internal users (SREs, developers, support engineers, and potentially other teams using Grafana for business intelligence) directly impacts product development:

  • Usability Enhancements: Daily use of Grafana dashboards, LogQL (for Loki), PromQL (for Mimir/Prometheus), and TraceQL (for Tempo) by their own teams helps identify usability friction points and drives improvements in query builders, visualization options, and overall workflow.
  • Scalability and Performance: The need to manage Grafana Cloud efficiently pushes the boundaries of Mimir's scalability for metrics, Loki's efficiency for log ingestion and querying, and Tempo's ability to handle vast numbers of traces. The performance improvements in Tempo 2.7, for instance, were explicitly linked to addressing internal resource consumption challenges.
  • New Feature Development: It's highly probable that many new features and even entire products (like Grafana OnCall or Grafana Incident for incident management) are born from identifying needs within their own operational teams first. Building tools to solve their own problems, then generalizing them for the broader community and customers, is a common and effective pattern in open-source-centric companies. The development of "observability as code" features in Grafana 12 and the new Grafana Assistant AI (as mentioned at GrafanaCON 2025 by Business Wire) likely stems from a desire to streamline their own complex observability configurations and make data interrogation more accessible.

Benefits for the Entire Ecosystem

Grafana Labs' commitment to "dogfooding" its entire observability stack offers significant benefits that extend beyond its own operational resilience:

  • More Robust Open Source Software: Features and fixes tested under the demanding conditions of Grafana Cloud often make their way back into the open-source versions of Grafana, Loki, Mimir, and Tempo, benefiting the entire community.
  • Better-Informed Product Strategy: Having a deep, firsthand understanding of user challenges allows Grafana Labs to build a more relevant and impactful product roadmap.
  • Increased Trust and Credibility: When a company successfully runs its own critical services on the products it offers, it sends a powerful message of confidence and reliability to its customers and users.

Conclusion: Charting the Course by Navigating It Themselves

Grafana Labs stands as a compelling example of a company that truly lives and breathes its own technology. By operating Grafana Cloud on the very LGTM (Loki, Grafana, Tempo, Mimir) stack it champions, Grafana Labs doesn't just build observability tools; it becomes its own most critical and informed user. This deep, internal reliance fuels a continuous cycle of refinement, innovation, and operational excellence that benefits their open-source community, their enterprise customers, and the broader observability landscape. They are not just charting the future of observability; they are navigating it firsthand, every single day.