Oberhahn vs. Langfuse
Langfuse instruments an application's LLM calls for developers. Oberhahn measures AI usage, spend, and individual impact across the whole organization.
Langfuse is an open-source LLM engineering platform: trace requests, manage prompts, and run evaluations for your application. Oberhahn sits above the app layer, giving leaders and individuals real-time spend and attribution across all AI usage. Here is how they compare.
and climbing, usage now spans more than the app you instrumented
of AI usage runs agent-driven and unattended
using AI, not just the one your engineers trace
| Feature | Oberhahn | Langfuse |
|---|---|---|
| Coverage & reach | ||
| Coverage beyond instrumented apps & routed traffic | Yes | |
| Security | ||
| Session-level tracing | Yes | 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 | ||
| Autonomous & background agent visibility | Yes | |
| 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.
Above the app layer, a live model of the org
Langfuse instruments an application's LLM calls for the team that owns it. Oberhahn sits above that: the Floor, the Rhythm, and the Organizational Map render how every person, team, and agent uses AI in real time, across providers and tools, not inside one traced codebase.
Then let individuals prove their impact
Traces are owned by an app; impact is owned by people. Oberhahn attributes usage to individuals and teams, so people surface what they shipped and managers spot champions and unowned workflows.
- Individuals surface the work they shipped with AI
- Managers find champions and unowned, high-value workflows
- Attribution rolls up to teams, projects, and reviews
Managed, and still open
You may choose Langfuse for self-hosting. Oberhahn is managed but keeps the openness that matters: custom events from any agent, query access to your data, your own views, and full export with no lock-in.
- 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 across teams and no one can size the work
- You need organization-wide visibility, not per-app traces
- You want interactive vs. unattended usage classified out of the box
- Individual impact should be visible to the person
- You want it managed, not self-hosted
Choose Langfuse if you
- You want open-source and self-hosting control
- Application-level tracing and evals are the goal
- Your team owns and instruments the app directly
- Prompt management lives with engineering
- Is Oberhahn open source?
- Oberhahn is a managed product focused on AI usage, spend, and attribution. If open-source self-hosting is a hard requirement for app tracing, Langfuse fits that need.
- Can they coexist?
- Yes. Keep Langfuse for application traces and evals; use Oberhahn for org-wide spend and attribution.
- Any lock-in with Oberhahn?
- No. All data exports as JSON/CSV.
Compare it live
Connect your stack free and watch the same data run through Oberhahn and Langfuse so you can decide on substance.
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