Grafana Labs is an open source software platform built to support monitoring, visualization, and metric analytics.
This company operates with a remote-first culture, allowing team members to work from anywhere.
Grafana Labs builds an open and composable monitoring and observability stack, centered around Grafana, the renowned open-source technology for dashboards and visualization. With over 20 million users globally, Grafana's recognizable dashboards monitor everything from beehives to NASA launches. Grafana Labs empowers more than 3,000 companies, including industry leaders like Bloomberg, Citigroup, Dell Technologies, and Salesforce, to manage their observability strategies. The LGTM Stack, featuring scalable metrics with Grafana Mimir, logs with Grafana Loki, and traces with Grafana Tempo, can be run fully managed with Grafana Cloud or self-managed with Grafana Enterprise offerings. This ensures comprehensive observability with extensive enterprise data source plugins, dashboard management, alerting, reporting, and security.
Grafana Labs operates as a remote-first, global company, bringing together talent from around the world. The culture is built on transparency, autonomy, and trust, fostering an innovation-driven environment. Decisions are made with open communication, and team members are empowered to take ownership of their work. Grafana Labs embraces diversity, with employees in over 40 countries, and actively seeks out diverse perspectives to make stronger decisions. They prioritize enduring value over short-term gains, operating with integrity and a commitment to shared success. The company also actively fights against bureaucracy, stagnation, and busywork.
Engineers at Grafana Labs tackle complex challenges in observability, working on open-source projects like Grafana k6 for performance testing. They build and maintain tools written in Go, focusing on developer experience, correctness, and scalability. The team operates with a high degree of independence, collaborating closely on design, implementation, and code reviews, with a release cadence of approximately every six weeks. They focus on delivering production-quality code and ensuring system reliability. Engineers also leverage modern AI coding assistants for faster prototyping, test generation, and refactoring, while maintaining strong code review and quality standards. A key focus is on solving observability challenges like alert fatigue and root cause analysis, utilizing their comprehensive suite of tools to provide contextual insights and proactive troubleshooting.