Nova vs Grafana
Decision criteria.
Overview
Nova and Grafana sit at different layers of the operations stack. Grafana is the dashboarding and visualisation layer over your existing observability data; Nova is an agentic-SRE layer that reads telemetry and proposes actions. They are complements more often than substitutes; the choice question is usually "which problem do I have right now."
- Grafana. Open-source dashboards over Prometheus, Loki, Tempo, and dozens of other data sources, deep PromQL/LogQL fluency, alertmanager integration.
- Nova. Agentic-SRE workflow: agents that gather signals, propose an action, apply with verification, and learn from the outcome. Sits above whichever observability stack you already run.
- Operational fit. Reach for Grafana when the gap is "we cannot see what is happening"; reach for Nova when the gap is "we can see it but the on-call response is too slow."
- Per-team decision and integration shape. Nova reads from the same OTel and Prometheus surfaces Grafana visualises, so most teams keep Grafana and add Nova alongside.
The approach
Diagnose the actual gap before picking a tool. A dashboarding problem and a response-time problem look similar from the outside but want very different solutions.
- Gap classification. Is the bottleneck visibility (Grafana), or response (Nova), or both? The answer changes which trial you run.
- Signal-source inventory. List the metrics, logs, and traces you already collect; both tools work better when those sources are stable.
- Trial in a real on-call rotation. Vendor demos hide the parts that matter. Run for two weeks of real incidents.
- Document the choice and the integration plan. If you keep both, write down where each owns the workflow so on-call knows which surface to open first.
Why this compounds
The right tool for the right problem keeps paying back: faster diagnosis when visibility is the gap, faster response when escalation is the gap, and fewer overlapping vendors when you treat them as complements rather than rivals.
- Faster incident response. Matching tool to gap removes the seconds spent guessing where to look first.
- Operational consolidation. Stable signal sources serve both dashboards and agents; you instrument once.
- Reduced alert fatigue. Agentic triage filters noise before paging; dashboards stop being the first stop on every alert.
- Decision trail for the next renewal. The trial data becomes the renewal scorecard, not a cold start.