Grafana is the open-source observability platform that connects to multiple data sources and renders unified dashboards for metrics, logs, and traces — serving as the "single pane of glass" that teams use to visualize AI infrastructure health, model performance, GPU utilization, and LLM cost analytics without storing data itself.

What Is Grafana?

• Definition: A multi-source visualization platform that queries data from Prometheus, InfluxDB, Elasticsearch, Loki, Jaeger, PostgreSQL, and dozens of other backends — rendering interactive dashboards with graphs, heatmaps, tables, and alerts.
• Architecture: Grafana is a pure visualization layer — it does not store metrics or logs. It queries existing data stores and renders results, making it composable with any monitoring stack.
• Created By: Torkel Odegaard (2014), originally forked from Kibana. Now maintained by Grafana Labs with a massive open-source community.
• Scale: Used by Netflix, Uber, PayPal, and virtually every major tech company — pre-built dashboards available for every popular AI framework and GPU monitoring stack.

Why Grafana Matters for AI Teams

• Training Run Monitoring: Visualize loss curves, gradient norms, learning rate schedules, and GPU utilization side-by-side in real time during model training.
• Inference Dashboard: Track TTFT (Time to First Token), tokens per second, queue depth, error rates, and cost per query with automatic alerting.
• GPU Fleet Management: Monitor temperature, memory usage, power draw, and SM utilization across hundreds of GPUs simultaneously — spot thermal throttling and underutilization instantly.
• Multi-Source Correlation: Overlay application metrics (Prometheus), logs (Loki), and traces (Tempo/Jaeger) on the same timeline — find root causes by correlating a latency spike with a log error and a specific trace.
• Cost Analytics: Track OpenAI API costs, RunPod GPU hours, and inference infrastructure costs — visualize cost per user, per model, per feature.

Core Concepts

Data Sources: Grafana's connectivity layer. Configure once, query anywhere:

• Prometheus (metrics time-series)
• Loki (logs — Prometheus-like, but for log streams)
• Tempo (distributed traces)
• InfluxDB (time-series)
• PostgreSQL / MySQL (structured data — query your experiment tracking DB)
• CloudWatch, Azure Monitor, Google Cloud Monitoring
• Elasticsearch / OpenSearch (log search and analytics)

Panels: Individual visualization units within a dashboard:

• Time Series: Line/bar charts for metrics over time.
• Stat: Single big number — current GPU temp, error rate, queue depth.
• Table: Tabular data — top 10 slowest queries, highest-cost models.
• Heatmap: Distribution over time — request latency distribution visualized as a heatmap.
• Logs Panel: Streaming log viewer filtered by labels.
• Traces Panel: Flame graph visualization of distributed traces.

Dashboards: Collections of panels arranged on a grid. Shareable as JSON — import community dashboards from grafana.com/grafana/dashboards.

Alerting: Grafana Alerting evaluates queries on a schedule and sends notifications via Slack, PagerDuty, email, and webhooks when thresholds are breached.

Pre-Built AI/ML Dashboards

Grafana Stack (LGTM)

Grafana Labs provides a full open-source observability stack:

• Loki — Log aggregation (like Prometheus but for logs).
• Grafana — Visualization layer.
• Tempo — Distributed tracing backend.
• Mimir — Long-term metrics storage (horizontally scalable Prometheus).

Together these four components cover all three observability pillars (metrics, logs, traces) in a single integrated stack.

Practical AI Inference Dashboard

A production LLM serving dashboard typically includes:

• TTFT p50/p95/p99 over time (line chart).
• Tokens per second by model (stacked bar).
• Active requests in queue (gauge).
• GPU memory utilization per device (multi-line).
• Error rate by error type (bar chart).
• Cost per 1K tokens trend (time series).
• Top 10 longest prompts by user (table).

Grafana is the universal lens through which AI teams observe their systems — its ability to unify metrics, logs, and traces from any data source into a single, interactive view makes it indispensable for monitoring the full stack from GPU hardware to LLM response quality in production.