Self-hosted AI models
Your data, your hardware, your control.
When sovereignty, latency, or compliance demand it, we deploy and fine-tune open-weight models on your infrastructure or private cloud. Zero data leaves your perimeter. We handle the model selection, hardware choice, deployment on Kubernetes, fine-tuning pipeline, and ongoing operations.
When this service makes sense
You probably need this if…
You handle data that legally cannot leave your perimeter — regulated health, financial, or government data.
Your customer contracts prohibit third-party processing and you've outgrown the carve-outs.
You're at high enough volume that managed-API spend is competitive with — or worse than — running your own GPUs.
You need latency the public internet can't deliver, like sub-100ms inference for an interactive product.
How we approach it
Our approach, step by step.
- 01
Pick the right open-weight model
We benchmark candidate models against your actual eval suite, not a generic leaderboard. The right model is the smallest one that passes your evals — anything bigger costs more to run for no measurable gain.
- 02
Provision the right hardware
Owned vs. rented, on-prem vs. cloud, single-tenant vs. shared, generation of GPU. We make the trade-offs explicit and choose based on volume, sensitivity, and your team's operational capacity.
- 03
Build the inference stack on Kubernetes
vLLM, TGI, or whichever serving stack fits — deployed on Kubernetes with autoscaling, batching, observability, and the same auth and rate-limiting infrastructure we'd build on top of a managed API.
- 04
Set up the fine-tuning loop
If your data demands it, a continuous fine-tuning pipeline with eval gates so you only ship a new model when it beats the current one on your tests.
What you get
Concrete deliverables.
- A self-hosted model deployment running in your infrastructure
- Kubernetes-based inference stack with autoscaling, batching, and full observability
- Eval suite specific to your workload, with gates that block bad model versions
- Optional fine-tuning pipeline for continuous improvement
- Operational runbook and on-call handover
Typical timeline
10-14 weeks for first deployment. Fine-tuning loop adds 4-6 weeks. Faster if you already have GPU infrastructure to deploy onto.
Common questions
What clients usually ask.
Will an open-weight model be good enough?
For most workflows — classification, summarisation, structured extraction, retrieval-augmented Q&A — yes. For frontier reasoning on novel problems, the gap is real. Our eval-first approach answers this for your specific workload before you commit to the spend.
What does this cost compared to using a managed API?
Crossover is typically somewhere between 50 and 200 million tokens per month depending on hardware, utilisation, and model. We provide a cost-shape comparison as part of discovery before you commit.
Who keeps the GPUs healthy?
On day one, we do. By the end of the engagement, your team does — with our written runbook, alerting setup, and a couple of months of shared on-call to pass the knowledge over.
Want to talk about self-hosted ai models?
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