The data can't cross a boundary
Regulated records, contractual restrictions, or a security team that will not sign off on outbound inference. Weights come to the data instead of the reverse.
AI / Gemma
Sometimes the constraint isn't quality, it's jurisdiction — or a per-request cost that has to round to nothing at millions of calls. Gemma is Google's family of open-weight models, and open weights mean we can run inference inside your own account, network and audit boundary.
Regulated records, contractual restrictions, or a security team that will not sign off on outbound inference. Weights come to the data instead of the reverse.
Classification, tagging, routing and enrichment running millions of times a day. Once utilisation is high enough, owning the inference beats renting it.
Inference next to your application, with no shared-tenant queue and no third-party rate limit deciding your p99 on a busy afternoon.
A single well-defined job, done the same way every time. A smaller model tuned on your examples frequently outperforms a large general one here — and costs a fraction.
The model is the easy bit. Keeping GPUs busy but not wasted, surviving a node failure mid-request, and rolling out a new checkpoint without a maintenance window — that's ordinary platform engineering, and it's what we do anyway.
We'd rather you hear this before signing than discover it in month three.
No provider absorbs a bad night for you. That's fine if you already run production infrastructure — and a real cost if you don't.
Below a certain steady utilisation, a hosted API is simply cheaper. We'll model the crossover point with your actual traffic before recommending either.
For the hardest reasoning tasks, a large hosted model is usually still ahead. Many systems end up hybrid — small and local for volume, large and hosted for the hard cases.
Then let's bring the model to it. We'll size the infrastructure, model the cost and show you what quality you'd actually get.