Private Inference
On-premise or VPC-bound model deployments for sensitive workloads — open-weight models served on hardware you control, with the same gateway and governance as cloud routes.
Some data cannot leave a controlled boundary. Private inference makes 'do not send this to a third-party API' an explicit, observable routing decision instead of a workflow rebuild.
What it solves
Lets workflows handle regulated or sensitive data without losing the gateway, governance, and observability that exist for cloud routes.
How we build it
Open-weight models (Llama, Mistral, Qwen, DeepSeek, Gemma, or others fit to the task) served on vLLM, Ollama, TGI, or SageMaker / Vertex private endpoints. The gateway routes sensitive calls to the private endpoint; the same telemetry, eval, and approval gates apply. Hardware sizing tied to expected concurrency and token budget.
- Open-weight model selection per task class
- vLLM, Ollama, TGI, or private cloud endpoints
- Same gateway and telemetry as cloud routes
- Capacity tied to concurrency and token budget
What changes when it is in place
Regulated workloads stop being a wall the AI platform cannot cross. Sensitive data stays inside the boundary; non-sensitive calls still get the best frontier model.