Private AI Deployment Checklist
A readiness checklist for teams evaluating private AI infrastructure, model hosting, API access, workflow automation, and operational support.
Define the Workload
Clarify whether the system needs inference, training, fine-tuning, retrieval, agents, or workflow automation.
Estimate request volume, latency expectations, model size, and concurrency.
Identify whether data needs to stay private, regional, encrypted, or isolated.
Plan the Operating Layer
Decide who owns monitoring, prompt changes, failures, API keys, access, and cost review.
Document how the AI system connects to CRMs, support desks, forms, databases, or internal APIs.
Define how model or workflow changes move from request to production.
Prepare for Support
Create escalation paths for failed jobs, incorrect outputs, integration errors, and usage spikes.
Keep deployment notes, credentials, billing, and support context in one customer record.
Review data restrictions before sending sensitive information into any AI workflow.