The production private AI stack

All three tools here have a genuine free tier, so a working configuration costs nothing while you evaluate it. The 03 COSTS table above breaks down each member. The meters that climb are physical and human: the GPUs you provision to hold concurrent load, the engineering time to keep production serving healthy, and Tabnine's per-seat cost as the engineering roster grows. The hardware and the people bite before any licence does. Budget the operations team before the tools.

No. vLLM and Open WebUI are the serving core: inference that survives concurrent load, and the interface (source-available, free to self-host) that turns it into a team service. The numbered steps double as the adoption order. Stand up vLLM first and prove throughput on your GPUs; front it with Open WebUI when the wider team needs access; add Tabnine last, through procurement, only when engineering wants coding assistance under the same no-cloud rule.

Ollama is the prototype you have likely proven the idea on: one machine, a handful of users, models running privately with almost no setup. Keep it for that. This stack is the production graduation: vLLM holds concurrent load across shared GPUs where Ollama would buckle, Open WebUI gives the whole organisation accounts and controls rather than one local endpoint, and Tabnine extends the perimeter into the editor. You move up when the pilot becomes a service.

vLLM and Open WebUI sit next to each other and are easily conflated. The dividing line is layer. vLLM owns serving: inference throughput, GPU efficiency and the standard API, blind to who is calling it. Open WebUI owns access: accounts, roles, admin control and document Q&A, with no inference engine of its own. vLLM wins any question about load and latency; Open WebUI wins any question about people and permissions. You run both because neither does the other's job.

Often, and this tier admits it. A production serving stack repays its GPU ownership and operations overhead only when many users genuinely hit it at once. Two signals say you have overshot: the real audience is a single team rather than an organisation, and no one owns running infrastructure overnight. A group reaching for this to feel serious rather than to serve concurrent load wants the competitive tier, the private and local stack.

This is the rare stack where confidential material can go in: all three run inside the perimeter, and vLLM's anonymous telemetry can be switched off too. Self-hosting moves the risk from a vendor's training terms to your own controls. Open WebUI sets the floor: a document is only as private as the roles and permissions you configure around it. Tabnine's air-gapped enterprise deployment keeps source code inside the boundary. Treat access control and admin roles as the real safeguard, not a vendor's promise.