LLM Deployment: API, Private Cloud, or On-Prem? The Decision That Sets Your Ceiling

Where your LLM runs determines cost, privacy, latency, and what you can build later. The three deployment paths, who each one is for, and the hybrid most businesses should choose.

Teams agonise over which model to use and spend five minutes on where it runs. Backwards. Deployment architecture is the decision that compounds — it sets your privacy posture, your unit economics, and your ceiling.

Path 1: frontier APIs

Best models on earth, zero infrastructure, pay per use. The right default for 80% of business use cases. The trade: data transits a third party (enterprise terms mitigate this more than most lawyers realise), and unit costs bite at very high volume.

Path 2: private cloud

Open-weight models on cloud GPUs you control. Data stays in your tenancy; costs become infrastructure instead of usage. Worth it when volume is high and steady, or compliance demands residency. The trade: you now operate infrastructure, and open models trail frontier ones.

Path 3: on-premise

Models on your own hardware. Total control, air-gap possible, zero data egress. Right for regulated industries, government, and genuine secrecy. Wrong for almost everyone else — you’re buying depreciating GPUs and the staff to babysit them.

Choose the least sovereignty your constraints allow. Every step toward on-prem trades model quality and speed for control.

The hybrid most businesses should actually run

• Frontier API for reasoning-heavy, low-volume work — strategy, drafting, analysis
• Small self-hosted model for high-volume, narrow tasks — classification, extraction, routing
• Retrieval layer over your own data serving both, so knowledge stays home

This is how we architect client deployments and our own: sovereignty where it matters, frontier quality where it counts, and costs that look like a utility bill instead of a moonshot.