Service
Private LLM & ML model deployment
We select, deploy and validate open-weight language models and other machine-learning models on infrastructure you own or rent directly. Your data stays inside your environment, and you hold the accounts, keys and configuration.
What you get
- A working, documented model endpoint inside your environment
- Infrastructure sized to your expected usage, not guesswork
- Measured response times and throughput, with test conditions stated
- Running-cost estimates you can budget against
Typical scope
- Model shortlisting against your use case, language and licence requirements
- GPU / instance sizing and provisioning in your AWS, Azure, GCP or on-premise environment
- Serving stack configuration (for example vLLM, TGI, Triton or similar, chosen per workload)
- OpenAI-compatible or custom API, authentication and network restrictions
- Load testing and a written validation report
Technical detail
- → Quantisation choices (FP16, FP8, INT8/INT4) evaluated against quality and memory budget
- → Context length, batching and KV-cache settings tuned to your traffic profile
- → Infrastructure defined as code where your environment allows it
Scope and pricing are agreed per engagement. Implementation is a fixed, one-time fee; infrastructure is paid directly to your provider; ongoing support is optional and priced separately.
Ready to talk through your requirements?
Tell us the use case, where it should run and how many people will use it. We will take it from there.