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.
source:codex cost:>5Then 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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