Your Proprietary Data Shouldn't Train Someone Else's Model.

Every fine-tuning job, every embedding call, every prompt with customer data in it passes through someone else's infrastructure, on someone else's terms. Frontier API vendors built genuinely useful products. They also built a business model where your most valuable data helps train a model you'll never own, running on hardware you'll never control, priced on a meter you don't set.

Open-weight models, Llama, Mistral, Qwen, and their fine-tuned descendants, have closed most of the capability gap that used to justify an API-only strategy. Fine-tuned on your data, they routinely match or beat general-purpose frontier models on the narrow, high-volume tasks that actually run your business. The question isn't whether an open model is 'as good' as a frontier model in the abstract. It's whether a model fine-tuned on your support tickets, your claims data, or your contracts outperforms a generic one on the ten tasks you run a million times a month. Usually, it does.

If the model gap has closed, why isn't everyone running private AI? Because the model was never the hard part. Standing up GPU infrastructure, securing it, operating it, and sizing it correctly is real engineering work, and most teams don't have a rented-to-owned playbook that avoids overbuilding on day one or underbuilding by month six. The numbers below are what it actually takes to get from a standing start to a right-sized, owned environment.

Rent first. Run production workloads on rented GPU capacity from day one, and let six months of real telemetry tell you what to build, not a sizing spreadsheet. Baseline against actual utilization, cost per workload, and growth trajectory. Then build to the evidence. On-prem or colocation sizing decisions get made against your own data, not a vendor's benchmark. The rented environment keeps running until the owned one is proven, so there's no cutover risk and no gap in production.

Control isn't a compliance checkbox. It's the difference between a vendor relationship and infrastructure you actually own. Four things have to be true for that to hold up under scrutiny, not just under a sales pitch. And a policy-driven AI gateway sits in front of all of it, routing each request to your private cluster or a public model by rule, so sensitive work never leaves your control.

Four stages, one team, from first conversation to steady-state operations. No handoff between the people who scoped it and the people who run it.

Private AI isn't the right call for every team, and we'd rather tell you that in the first conversation than the sixth month. Here's how we think about fit.