How Grafana’s adaptive telemetry manages observability costs

AI infrastructure demand is tightening memory chip supplies and raising prices. Most generative AI projects fail to show measurable ROI despite billions in investment. Experts point to weak data infrastructure as the cause that prevents enterprise AI from reaching profitable scale. As AI infrastructure expands, companies face higher resource consumption. Power, cooling, and supply chains now shape deployment timelines and ROI. Most GPU capacity remains idle, which exposes costly overbuying and inefficiencies. This inefficiency mirrors the struggle in the hardware market, where the battle between ARM and x86 continues to shape server deployment.

Grafana Cloud provides Adaptive Telemetry for telemetry optimization to manage these costs. This tool targets the specific telemetry inefficiencies that arise when monitoring massive AI data centers.

I recommend the Cloud offering for these specific needs.

Grafana Labs maintains a $6 billion valuation as of 2024. The stack is LGTM. The company builds the LGTM stack to provide a complete observability solution. The stack includes Loki, which is a horizontally scalable, multi-tenant log aggregation system. Loki indexes metadata rather than full text. Mimir is a horizontally scalable, multi-tenant time-series database for long-term storage of Prometheus metrics. It replaced Cortex in 2022. Tempo is a high-scale distributed tracing database that requires only object storage to operate and does not index trace data. Pyroscope is a highly available, multi-tenant continuous profiling database.

Torkel Odegaard released Grafana in January 2014 as an outgrowth of work on Graphite at Orbitz. The company grew through substantial funding, including $24M in 2019, $50M in 2020, $220M in 2021, and $270M in 2024. Grafana Labs expanded its stack through several acquisitions. It bought Kausal in 2018, k6 in 2021, Pace in 2021, and Amixr in 2021. It also bought Asserts.ai and Pyroscope in 2023, TailCtrl in 2024, and LogLine in 2026. These additions support load testing, developer tooling, incident response, and log querying. The platform connects to many sources including Prometheus, InfluxDB, Elasticsearch, MySQL, PostgreSQL, and AWS CloudWatch.

You already know how to add a data source. The backend uses Go.

The application runs as a single binary and listens on port 3000 by default.

The default credentials are admin/admin.

Grafana Labs provides three management levels. Grafana Cloud handles managed deployments and scalability. While Grafana Enterprise provides control over self-managed environments, Grafana Cloud handles the details of scalability and availability so teams can focus on development and innovation instead of managing infrastructure. Grafana OnCall manages incident workflows and automates routine tasks like schedule generation and notification distribution.

Dashboards are defined as JSON documents. Users can use plugins to add visualization types like heatmaps, histograms, and pie charts. The plugin system includes data source plugins, panel plugins, and app plugins. Alerting rules send notifications to Slack, PagerDuty, email, and webhooks. Users use template variables to create dynamic dashboards. Data retrieval from databases and monitored services creates an overhead that could impact those resources. This overhead can affect the performance of the very systems you monitor.

Can organizations rely solely on open-source metrics to maintain high-fidelity observability during peak traffic?