Oberhahn vs. Arize Phoenix
Phoenix helps engineers trace and evaluate model quality. Oberhahn measures organization-wide usage, spend, and who is driving it.
Arize Phoenix is a popular open-source tool for tracing LLM and agent runs, running evals, and debugging model performance in development and production. Oberhahn works at the organizational layer instead, real-time spend and per-individual attribution across every provider. This comparison applies to open-source Phoenix, not the commercial Arize AX platform (which adds audit logs, user views, custom dashboards, and alerting). Here is how they compare.
and climbing, evals improve quality but never say who is spending
of AI usage runs agent-driven and unattended
using AI, not just the runs you evaluate
| Feature | Oberhahn | Arize Phoenix |
|---|---|---|
| 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 | No |
| Open & extensible | ||
| Build your own AI-attribution views | 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.
From evaluating runs to modeling the org
Phoenix helps engineers trace and evaluate model quality. Oberhahn measures something different: how the whole organization uses AI. The Floor, the Rhythm, and the Organizational Map render a live model of which people, teams, and agents drive usage, not the quality of individual runs.
Then let individuals prove their impact
An experiment view is about outputs, not people. Oberhahn attributes usage to individuals and teams, so people surface what they shipped and managers find 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
Phoenix is open-source for eval and tracing. Oberhahn is managed but open where it counts: custom events, query access to your data, your own attribution 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 org-wide visibility, not per-app traces and evals
- You want interactive vs. unattended usage classified out of the box
- Individual and team attribution roll up to reviews and OKRs
- You prefer a managed product over self-hosting
Choose Arize Phoenix if you
- You are debugging and evaluating model quality
- Open-source and self-hosting are requirements
- Trace-level experimentation is the main use case
- Your team instruments the application directly
- Do Oberhahn and Phoenix overlap?
- Lightly. Phoenix is for tracing and evaluating model quality; Oberhahn is for org-wide usage, spend, and attribution. Many teams use both.
- Is Oberhahn open source?
- Oberhahn is a managed product. If open-source self-hosting for eval and tracing is a hard requirement, Phoenix fits that need.
- Does Oberhahn evaluate model quality?
- No. For evals and experimentation, Phoenix is purpose-built. Oberhahn focuses on usage, spend, and attribution.
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