What does an enterprise local AI deployment look like?
A typical setup has four layers: machines that hold the models (one server or a cluster), inference software that serves an OpenAI- or Anthropic-compatible API, a gateway that adds sign-in, quotas and logging, and the apps people use, such as a chat interface, coding agents and internal tools. Everything runs inside the company network.
Compute: Macs with large unified memory, or NVIDIA GPU servers. A cluster lets you run the largest open models and add capacity over time.
Inference: software that loads the models, splits them across machines and serves an API. Standard API formats mean every tool can connect the same way.
Gateway: single sign-on, per-team keys, rate limits, usage tracking and audit logs. Open-source gateways such as LiteLLM can sit in front of local and cloud models together.
Apps: a chat interface such as Open WebUI for everyone, coding agents for engineers, and internal apps that call the API directly.
Most companies start with a pilot for one team, measure real usage for a few weeks, then size the full deployment from those numbers.
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