Run open-weight models in your own cloud
Open-weight language models running in your own cloud account and region, sized to your real traffic, with no prompts sent to a third-party API.
Overview
Some teams cannot send prompts to a public AI API. Patient notes, loan files and source code are the usual reasons. We deploy open-weight models such as Llama, Mistral, Qwen and gpt-oss on vLLM, TGI or Ollama inside your own cloud account, in the region you choose.
We size GPUs against your actual request volume, not a guess. If your volume is low, we will tell you that a region-pinned managed endpoint is probably cheaper than idle GPUs, and help you set that up instead.
Signs you need this
- Legal or security has blocked public AI APIs and staff use them anyway
- You have a working prototype on a hosted API but cannot ship it to production
- Your GPU instances sit idle most of the day and nobody knows the right size
- Every model upgrade is a manual rebuild that one engineer understands
What you get
- Model and hardware shortlist
- Two or three candidate models tested on your own prompts, with the GPU type, memory and quantisation each one needs.
- Serving stack in your account
- vLLM or TGI on Kubernetes or plain instances, behind an OpenAI-compatible endpoint your applications can call.
- Network isolation
- Private subnets, private endpoints and egress rules so prompts and outputs never leave your network.
- Autoscaling and scheduling
- Scale-to-zero or scheduled capacity for quiet hours, with queueing so bursts do not drop requests.
- Usage and latency dashboards
- Tokens, queue depth, time to first token and GPU utilisation per model, in Grafana or your existing tool.
- Build versus buy note
- A short written comparison of self-hosting against a region-pinned managed endpoint at your current and expected volume.
How the work runs
- 1
Workload review
1 to 2 weeks
We collect sample prompts, expected volume, latency targets and data rules, then shortlist models and hardware.
- 2
Benchmark
1 to 2 weeks
We run the shortlisted models on your prompts and measure quality, throughput and memory on real GPUs.
- 3
Build
2 to 4 weeks
We deploy the serving stack with infrastructure as code, private networking, authentication and monitoring.
- 4
Load test and handover
1 to 2 weeks
We test at expected peak, tune batching and autoscaling, and walk your team through runbooks.
Timings depend on the size of your estate. The assessment fixes them before any build starts.
What you can check afterwards
- Applications call a model endpoint that resolves only inside your private network
- Flow logs show no prompt traffic leaving your chosen region
- A load test report records throughput and latency at your expected peak
- Your team can swap or upgrade a model by changing one configuration file
Tools we work with
- vLLM
- Text Generation Inference
- Ollama
- Llama
- Mistral
- Qwen
- gpt-oss
- Kubernetes
- NVIDIA GPU Operator
- Terraform
- Prometheus
- Grafana
From our work
North American client, delivered by the OpsBreak team
Moving a language model workload from paid APIs to self-hosting
About 60% lower model running cost than API pricing
Questions buyers ask
- Does any of our data leave the country?
- No. The model weights are downloaded once, then inference runs in your account and region. We block outbound traffic from the serving nodes and you can verify it in flow logs.
- Will an open-weight model be as good as the big hosted ones?
- For many focused tasks such as summarising, extraction and internal search, a well-chosen open model is good enough. For hard reasoning it may not be, and the benchmark phase shows you the gap on your own prompts before you commit.
- Is self-hosting always cheaper?
- No. GPUs cost money whether they are busy or not. At low volume a managed endpoint pinned to your region is often cheaper, and we will say so.
- What access do you need?
- A role in a dedicated cloud account or project with rights to create GPU instances, networking and Kubernetes resources. We do not need production data during the build.
Talk to us about private LLM hosting.
Tell us what you run and where. We reply within one business day with questions or a quote.