Oberhahn vs. LiteLLM
LiteLLM standardizes model access and budgets the traffic it proxies. Oberhahn measures usage and individual impact across all AI activity, on or off the proxy.
LiteLLM is a widely used open-source proxy and SDK that unifies model access behind one API, with virtual keys, budgets, and request logging. Oberhahn is not a request-path proxy, it focuses on real-time visibility and per-person attribution across every provider, whether or not traffic runs through LiteLLM. Here is how they compare.
and climbing, including the usage that never touches the proxy
of AI usage runs agent-driven and unattended
and tool, not only what runs through the proxy
| Feature | Oberhahn | LiteLLM |
|---|---|---|
| Coverage & reach | ||
| Coverage beyond instrumented apps & routed traffic | Yes | |
| Real-time | ||
| Streaming updates | Yes | Partial |
| Security | ||
| Session-level tracing | Yes | |
| Audit logs | Yes | |
| Context intelligence | ||
| Tracks repeated context | Yes | |
| Attribution | ||
| Per-person attribution, every tool, no manual tagging | 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 | |
| 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.
Beyond the proxy, a model of the org
LiteLLM standardizes model access and budgets keys, users, and teams on its proxy. Oberhahn models the whole organization: the Floor, the Rhythm, and the Organizational Map render how every person and agent uses AI in real time, whether or not it runs through the proxy.
Then let individuals prove their impact
LiteLLM sees the keys, users, and teams on its proxy. Oberhahn attributes every person and agent across the org, on or off the proxy, 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
Open, without forcing a proxy
LiteLLM is open-source and self-hostable. Oberhahn keeps the openness, custom events, query access, your own views, full export, while giving you visibility without routing every request through one path.
- Org-wide visibility without per-app instrumentation
- Send custom events and query your data via API
- Build your own views; export everything, no lock-in
Choose Oberhahn if you
- Agents run unattended, including outside the proxy
- You want interactive vs. unattended usage classified out of the box
- Individual attribution matters for reviews and comp
- You need visibility without proxying all traffic
- Team and OKR rollups are important
Choose LiteLLM if you
- You want a unified, OpenAI-style API across many models
- Virtual keys and per-key budgets are the main goal
- Open-source and self-hosting are requirements
- You are standardizing model access on one proxy
- Does Oberhahn replace LiteLLM's proxy?
- No. LiteLLM handles model access, keys, and budgets at the request path. Oberhahn adds visibility and attribution across everything.
- Do I have to route through a proxy for Oberhahn?
- No. Oberhahn gives visibility without forcing all traffic through a single request path.
- Can they run together?
- Yes. Keep LiteLLM for access and budgets; use Oberhahn for org-wide visibility and attribution.
Compare it live
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