How do I deploy private AI for my team or company?

Pick the models your team needs, size hardware so those models fit in memory with room for context, install inference software that exposes a standard API, and point your team’s apps, chat interface and coding agents at that endpoint. Start small with one or two machines and grow from real usage.

1. List the jobs: chat, coding agents, document search, extraction. This decides which models you need.
2. Size memory: model weights at your chosen quantization, plus context for each concurrent user.
3. Choose hardware: one machine for small models, a cluster for the largest ones. Measure speed on your own prompts.
4. Serve an API: OpenAI- or Anthropic-compatible endpoints let most tools connect without code changes.
5. Add the team layer: a chat interface such as Open WebUI, single sign-on, logging and access rules.
6. Operate it: monitoring, model updates, and a plan for hardware failure.

Updated Oct 26, 2026