Oberhahn vs. Helicone
Helicone logs and caches LLM traffic through its gateway or async SDKs. Oberhahn maps all AI usage to people, teams, and agents, with no request-path changes or per-app instrumentation.
Helicone captures your LLM requests, via its gateway/proxy or async logging, to log usage, cache responses, and track cost, close to Oberhahn on efficiency. Where they diverge is scope: Oberhahn attributes usage to individuals, teams, and agents across the whole organization. Here is how they compare.
and climbing, caching trims the bill; mapping it to people and teams is the remaining work
of AI usage runs agent-driven and unattended
attribution to people and teams, not just API keys
| Feature | Oberhahn | Helicone |
|---|---|---|
| Coverage & reach | ||
| Coverage beyond instrumented apps & routed traffic | 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 | ||
| Autonomous & background agent visibility | Yes | |
| Unattended vs. interactive classification | Yes | No |
| Real capacity incl. background agents | Yes | |
| 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.
Past logging and caching, a model of the org
Helicone is close on efficiency, it proxies requests to log usage and cache responses. Oberhahn answers the question a proxy log cannot: who and what across the organization is using AI. The Floor, the Rhythm, and the Organizational Map map it to people, teams, and agents in real time.
Then let individuals prove their impact
A log grouped by API key does not know your org chart. 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
And build on an open layer, without a proxy
Helicone is open source, with async logging alongside its gateway. Oberhahn's openness sits at the org layer, custom events, query access, your own views, full export, with no per-app instrumentation at all.
- 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 and no one can see or size the work
- You want interactive vs. unattended usage classified out of the box
- Individual attribution matters for reviews and comp
- You need visibility without instrumenting every app
- Team and OKR rollups are important
Choose Helicone if you
- You want a drop-in proxy for logging and caching
- Caching to cut cost is the main goal
- App/API-key-level views are enough
- You are comfortable routing traffic through a proxy
- Does Oberhahn require a proxy?
- No. Oberhahn sits outside the request path entirely. (Helicone also offers async logging; that still means instrumenting each app.)
- Do both handle caching?
- Helicone offers response caching at the proxy. Oberhahn surfaces repeated-context and cache-efficiency insight so you can act on waste.
- Can I use both?
- Yes. Keep Helicone's proxy/caching and use Oberhahn for org-wide visibility and attribution.
Compare it live
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