Own the intelligence

Private LLMs on open-weight models

Your own language model, on your own data, inside your own AWS account. No prompts leave the building.

What we do

For brands with data-residency, privacy or cost reasons to keep AI in-house, we deploy and fine-tune open-weight models (Llama, Mistral, Qwen and others) on AWS, using Bedrock, SageMaker or self-managed GPU instances, connected to your warehouse and knowledge base with retrieval, access control and audit logging.

What you get

  • Model and hosting selection with a cost model
  • Secure deployment in your AWS account (VPC-only, encrypted, logged)
  • Fine-tuning and retrieval pipeline on your data
  • Evaluation, red-teaming and handover documentation

Research and answers on this topic

Related work

Common questions

When does a private LLM make sense over an API model?
When customer or financial data cannot leave your environment, when you process enough volume that per-token pricing becomes expensive, or when you need a model tuned to your catalogue and voice. Many clients run both: a private model for sensitive work and an API model for the rest.
Which open-weight models do you use?
Llama, Mistral and Qwen families are our defaults, chosen per task after benchmarking on your own evaluation set. We re-benchmark as new releases arrive.
What does the AWS footprint look like?
Typically Bedrock for managed inference, or SageMaker and EC2 GPU instances when you need fine-tuning or full control. Everything runs inside your VPC with KMS encryption and CloudTrail logging.
Who maintains the model after handover?
Your team, with our documentation and training, or us under a care plan. Either way you own the weights, the data and the infrastructure.

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