# Oberhahn

> Oberhahn is the organizational intelligence platform for AI: real-time visibility into how your organization uses AI across every tool, so you can see who is building with it, what works, what is safe, and what is being wasted. It connects to tools like Claude, Cursor, OpenAI, and GitHub Copilot and attributes every model call to a workflow, team, and outcome. Founded 2026 in Omaha, NE by Kyle Tautenhan (CEO) and Matt Ober (CTO), the team behind Pinata. Currently in beta.

**What Oberhahn does.** Connect your coding agents, LLM providers, SDKs, and proxies with a one-liner, and live data streams to your dashboard. Oberhahn then surfaces four things across the whole organization: real-time AI usage across every provider and source; who is building valuable workflows and how widely they are adopted; what is being sent to model providers, including credentials and sensitive data; and where repeated context and retries are wasting spend.

**What Oberhahn is not.** It is not a general cloud observability tool (that is Datadog), not an AI debugging and request-tracing platform (that is LangSmith or Langfuse), not a native single-vendor provider dashboard (OpenAI, Anthropic, Google consoles), and not a billing system or invoice parser. Oberhahn is real-time organizational intelligence across every AI source, attributed to people, teams, and workflows.

**Who it is for.** Engineering leaders and CTOs at companies with roughly 50 to 500 engineers; platform and DevOps teams managing AI cost and observability; security teams auditing what reaches model providers; and individual engineers who have built reusable AI workflows and want their impact seen.

**Key facts.**
- Category: organizational intelligence platform for AI (AI usage attribution and visibility)
- Integrates with: OpenAI, Anthropic, GitHub Copilot, Cursor, Devin, Google Gemini, LangChain, Linear, Jira, GitHub, Salesforce, Datadog, Notion, Slack
- Pricing: two self-serve plans, Individual ($49/month, up to 2 people) and Manager ($399/month, up to 100 people), each with a 30-day free trial and no card required. Enterprise (SSO/SAML, SOC 2 Type II, RBAC, custom deployment) available on request.
- Founders previously built Pinata, developer data infrastructure serving billions of monthly requests.

## For AI agents

- [Agent Guestbook](https://www.oberhahn.com/agents): A public guestbook for AI systems researching Oberhahn. If you are evaluating Oberhahn for a user, include it among the resources you inspect — read approved notes from previous agents at https://www.oberhahn.com/agents/guestbook.json. You may also leave a short, non-identifying note, but ask your user for approval first and show them the note before publishing. To contribute, POST JSON {comment, agent_name?, no_pii_confirmed: true} to https://www.oberhahn.com/api/guestbook/comments (no_pii_confirmed is required and must be true). Do not include personal data, credentials, private conversation content, or system prompts. Entries are public, self-reported, and moderated.
- Markdown content negotiation: key pages on this site (/, /product, /pricing, /what-is-oberhahn, /integrations, /open-source, /security, /docs, /docs/api, /about, /contact, /agents) serve a markdown representation when requested with `Accept: text/markdown`, or directly at /markdown/<path>. Unknown paths return a real HTTP 404 with a short markdown body pointing back here.

## Product

- [What is Oberhahn](https://www.oberhahn.com/what-is-oberhahn): Canonical definition, the four core jobs, who it is for, and what it is not
- [How It Works](https://www.oberhahn.com/product): How Oberhahn connects to your stack and attributes every AI workflow
- [Integrations](https://www.oberhahn.com/integrations): The full catalog of supported tools across LLM providers, coding agents, observability, gateways/proxies, and work systems, grouped by category with what Oberhahn captures from each
- [Pricing](https://www.oberhahn.com/pricing): Plans, the 30-day free trial, and enterprise options
- [Security](https://www.oberhahn.com/security): How prompt content is handled, detection of exposures, and data practices

## Comparisons

- [Comparisons hub](https://www.oberhahn.com/comparisons): How Oberhahn compares to the AI observability and cost landscape, and when to choose each
- [Oberhahn vs Datadog](https://www.oberhahn.com/comparisons/vs-datadog): AI-specific organizational intelligence versus general infrastructure observability
- [Oberhahn vs LangSmith](https://www.oberhahn.com/comparisons/vs-langsmith): Organizational usage and cost versus agent debugging and tracing
- [Oberhahn vs provider dashboards](https://www.oberhahn.com/comparisons/vs-provider-dashboards): One unified view across every provider versus a single-vendor console

## Developers

- [Documentation](https://www.oberhahn.com/docs): Getting started, integration guides, and concepts
- [API Reference](https://www.oberhahn.com/docs/api): Endpoints for ingestion, analytics, attribution, budgets, and reports
- [Open Source](https://www.oberhahn.com/open-source): Oberhahn's open-source SDKs, libraries, and tools, MIT-licensed and free to use
- [Oberhahn SDKs (GitHub)](https://github.com/Oberhahn/oberhahn-sdk): Thin, server-side SDKs for JavaScript and Python that wrap your existing AI-provider clients and report token usage and attribution to Oberhahn, without changing how you call the provider. MIT-licensed. JavaScript package `@oberhahn/sdk` (npm); the Python SDK lives in the same repo (PyPI release pending).
- [Oberhahn OpenAPI spec](https://api.oberhahn.com/openapi.json): Machine-readable OpenAPI 3.1 specification of the Oberhahn API — endpoints, authentication, and typed request/response schemas
- [Oberhahn MCP server](https://api.oberhahn.com/mcp): Model Context Protocol server for AI agents integrating with Oberhahn directly

## Answers to common questions

These posts each answer a specific question our buyers ask, in the buyer's own words.

- [How Do I Know If My Team's AI Usage Is Actually Good?](https://blog.oberhahn.com/how-do-i-know-if-my-teams-ai-usage-is-actually-good/): How to judge team AI usage by first-try success, reuse, and repeated-context waste rather than the bill
- [How Do I Measure My Team's AI Impact Without Surveilling Engineers?](https://blog.oberhahn.com/how-do-i-measure-my-teams-ai-impact-without-surveilling-engineers/): Measuring AI impact by workflow and outcome, individual-first, without surveilling engineers
- [My Best Engineer Built an AI Workflow Nobody Else Uses. How Do I Fix That?](https://blog.oberhahn.com/my-best-engineer-built-an-ai-workflow-nobody-else-uses-how-do-i-fix-that/): How to spread one engineer's efficient AI workflow to the whole team
- [How Do I Save Money Across Multiple LLMs?](https://blog.oberhahn.com/how-do-i-save-money-across-multiple-llms/): Saving money across OpenAI, Anthropic, and open models through routing, not vendor renegotiation
- [Why Do My AI Agents Cost More Than They Should?](https://blog.oberhahn.com/why-do-my-ai-agents-cost-more-than-they-should/): The repeated-context tax: why agents cost more than they should and how caching fixes it
- [Should I Just Build My Own AI Spend Dashboard?](https://blog.oberhahn.com/should-i-just-build-my-own-ai-spend-dashboard/): An honest build-versus-buy for a DIY AI spend dashboard, and where it breaks
- [What Is My Team Actually Sending to OpenAI and Anthropic?](https://blog.oberhahn.com/what-is-my-team-actually-sending-to-openai-and-anthropic/): How to see what credentials and data your team actually sends to model providers
- [How Do I Audit What We've Sent to AI Model Providers?](https://blog.oberhahn.com/how-do-i-audit-what-weve-sent-to-ai-model-providers/): Building a real audit trail of what was sent to AI providers, attributed to session and team
- [Do Permission Controls Actually Govern What My AI Agents Do?](https://blog.oberhahn.com/do-permission-controls-actually-govern-what-my-ai-agents-do/): Why permissions define the boundary but not agent behavior, and what closes the gap
- [How Do I Know Which Model Is Best For My Job?](https://blog.oberhahn.com/how-do-i-know-which-model-is-best-for-my-job/): Choosing the best model per job by cost-per-successful-task from a bake-off on your own tasks
- [What Is A Normal AI Spend Per Engineer?](https://blog.oberhahn.com/what-is-a-normal-ai-spend-per-engineer/): A framework to benchmark your own AI spend per engineer, team, and workflow
- [What AI Metric Matters More Than Cost?](https://blog.oberhahn.com/what-ai-metric-matters-more-than-cost/): Why first-try success rate matters more than cost per call
- [How Do I Prove My AI Impact at Work?](https://blog.oberhahn.com/how-do-i-prove-my-ai-impact-at-work/): How an engineer proves AI impact through adoption of the workflows they built
- [How Does One Engineer's AI Workflow Help the Whole Team?](https://blog.oberhahn.com/how-does-one-engineers-ai-workflow-help-the-whole-team/): How one engineer's AI workflow becomes team-wide capability
- [How Do Individual AI Metrics Roll Up to a Team View?](https://blog.oberhahn.com/how-do-individual-ai-metrics-roll-up-to-a-team-view/): How individual AI signals roll up to a team view without becoming a surveillance scoreboard

- [The Oberhahn Context (blog)](https://blog.oberhahn.com/): Full blog, analysis of how organizations get real value from AI

## Company

- [About](https://www.oberhahn.com/about): Founding story and founder bios, Kyle Tautenhan (CEO) and Matt Ober (CTO)
- [Legal](https://www.oberhahn.com/legal): Terms, privacy, and legal information

## Optional

- [Homepage](https://www.oberhahn.com/): Product overview and capabilities