Private AI deployment
Run AI within the boundaries your data needs.
Place inference and retrieval within agreed data boundaries and document every external provider involved.
Authorised documents and application data
Selected hosting and model endpoint
Approved services and redacted telemetry
A private endpoint is only one boundary
The fragile approach
A private endpoint as the whole plan
Logs, embedding services, backups and support access can create additional processing paths.
The intended approach
A documented processing boundary
Review all dependencies, identity paths, retention settings and recovery arrangements alongside model hosting.
An implementation example
Private is an architecture decision
Map the movement of prompts, files, generated output and diagnostic logs. Hosting choice alone does not define a private system when supporting services can still receive its data.
Know where each request goes
A business is considering where sensitive internal inference should run.
A failure to account for
Inference stays in the chosen region but diagnostic payloads are exported elsewhere.
Illustrative scenario, not a customer case study.
AI, data & automation
Control the complete processing path.
Processing boundaries
Choose where inference runs and which services can receive prompts, files and generated output.
Identity and connectivity
Configure private access paths, service identities and narrowly scoped credentials for the workload.
Model operations
Plan model updates, capacity, telemetry and rollback as part of the application’s operating model.
Inference routing
Select approved model endpoints by task, sensitivity and capacity, with explicit fallback behaviour.
Data handling
Define prompt and response retention, operational logging and access to captured traces.
Execution isolation
Separate generated code or untrusted processing from application credentials and production records.
From implementation to ownership
What your team receives
Agree the scope and the acceptance evidence before delivery starts.
Data-flow map
Prompts, files, embeddings, outputs, logs and every receiving service.
Included scope agreed before deliveryDeployment configuration
Hosting, identity, network and outbound controls in reviewable form.
Included scope agreed before deliveryOperating guide
Access review, model updates, monitoring and recovery responsibilities.
Included scope agreed before deliveryNo. The choice depends on processing terms, network controls, operating capacity and the required models. Compare managed and self-hosted options against the same data-flow requirements.
Discuss private ai deployment
Bring the workflow, the constraints and the questions your team needs to resolve.