Not every AI workload belongs in the cloud. If you're in finance, healthcare, or the public sector — or you just don't love the idea of your source code and customer data leaving the building — running open-weight models on infrastructure you control is often the better call. Here's how we think about it.
The models are good enough now
The open-weight field moved fast. Models like Gemma, Qwen, and Nemotron run comfortably on a single workstation or a modest GPU box and are more than capable for grounded, task-specific work: classification, extraction, retrieval-augmented answering, tool-calling inside an agent. You don't need a frontier model to wrap a mainframe API or triage a support queue — you need the right model, quantized and tuned for the job.
What you actually get
- Data stays in-house. Sensitive code, documents, and PII never leave your network. With an air-gapped deployment, they never touch the public internet.
- Predictable cost. Fixed infrastructure instead of metered per-token billing. Your AI bill doesn't balloon with usage or scale.
- Control. You choose the model, the version, the update cadence. No surprise deprecations, no changing terms.
The pattern that works: local-first, cloud fallback
The mistake is treating it as all-or-nothing. The setup we deploy routes each request to a local model when the box is available, and falls back to a frontier cloud model automatically for the rare task that genuinely needs it. Most traffic is free and private; the hard cases still get answered.
Concretely, that's an inference runtime (Ollama or vLLM) serving the local model, a thin router that health-checks it and picks a backend, and the same MCP connectors underneath so agents don't care which model answered.
The unglamorous part: evals
The reason teams get burned by local models isn't capability — it's that they never measured. Before anything ships, we stand up evals against your actual tasks (not a leaderboard), benchmark a few candidate models, and pick on evidence. Then observability and rollback so it stays honest in production.
Done right, on-prem AI isn't a downgrade. It's more private, more predictable, and — for the workloads that fit — genuinely cheaper.
Curious whether your workload fits? Email us.