Oberhahn vs. Grafana
Grafana gives you a canvas, and Grafana Cloud prebuilt app-level AI dashboards. Oberhahn ships the org-level map, every person and agent, attributed in real time, already assembled.
Grafana is a flexible, open-source visualization layer, and Grafana Cloud now ships AI/Agent Observability with prebuilt LLM dashboards, conversations, and cost views. What stays DIY is the organization-level layer: per-person attribution and team rollups. Oberhahn is opinionated and purpose-built for exactly that, working out of the box. Here is how they compare.
and climbing, with no single org-wide view of who is driving it
of AI usage runs agent-driven and unattended
to an org-wide picture, no pipelines to assemble or maintain
| Feature | Oberhahn | Grafana |
|---|---|---|
| Coverage & reach | ||
| Coverage beyond instrumented apps & routed traffic | Yes | |
| Real-time | ||
| Streaming updates | Yes | |
| Security | ||
| Session-level tracing | Yes | |
| Audit logs | Yes | |
| Context intelligence | ||
| Tracks repeated context | Yes | |
| Attribution | ||
| Per-person attribution, every tool, no manual tagging | Yes | |
| Per-team & per-model attribution | Yes | |
| Agentic & autonomous work | ||
| Unattended vs. interactive classification | Yes | No |
| Real capacity incl. background agents | Yes | No |
| Runaway-agent loop detection | Yes | |
| Key-person / concentration risk | Yes | |
Straight talk for engineers: Oberhahn reports billed cash only, status means completion not quality, and interactive-vs-automated is a classification, not a judgment. No individual-hour surveillance, and no capacity baseline unless you set one.
Ship the org map, don't build it
Grafana Cloud's Agent Observability watches instrumented apps and agents. Oberhahn models the organization: the Floor, the Rhythm, and the Organizational Map render how every person and agent uses AI, across tools and providers, attributed to people and teams, on day one.
Then let individuals prove their impact
An app-level dashboard does not know who your people are. Oberhahn does, individuals surface what they shipped with AI, and managers spot champions and unowned workflows without anyone hand-building a query.
- Individuals surface the work they shipped with AI
- Managers find champions and unowned, high-value workflows
- Attribution rolls up to teams, projects, and reviews
Still open when you want to extend it
You choose Grafana for openness, Oberhahn keeps it. Send custom events, query your data via API, and build your own views on top, with full export and no lock-in. You get prebuilt AI intelligence without giving up control.
- Send custom events from your own agents and pipelines
- Query the underlying data via API
- Build your own views; export everything, no lock-in
Choose Oberhahn if you
- Agents run unattended and no one can see or size the work
- You want org-wide, per-person AI visibility without assembling it
- You want interactive vs. unattended usage classified out of the box
- Individual and team attribution should come standard
- You would rather not maintain pipelines
Choose Grafana if you
- You want full control and open-source flexibility
- You already run a metrics/logs stack to visualize
- Custom, bespoke dashboards are the goal
- You have the team to build and maintain it
- Can I send Oberhahn data into Grafana?
- Oberhahn exports data as JSON/CSV, and many teams keep Grafana for custom views while using Oberhahn for AI-specific attribution and classification.
- Why not just build this in Grafana?
- Grafana Cloud now ships app- and agent-level AI dashboards prebuilt. The organization-wide layer, per-person attribution, interactive vs. unattended classification, team rollups, is what you would still assemble and maintain yourself. Oberhahn ships that ready to use.
- Is there any lock-in?
- No. Your data exports as JSON/CSV at any time.
Compare it live
Connect your stack free and watch the same data run through Oberhahn and Grafana so you can decide on substance.
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