Where does the data live?
Source, persistence, retention, and perimeter.
Private AI & Local Inference
Not all data can leave. Not all models need to live outside.
I design AI architectures where models, data, and applications are placed in the right environment — public cloud, private cloud, on-premise, or local — based on risk, budget, performance, and governance.
AI accelerates. Systems thinking governs.
Where the model runs is a data governance choice.
Private AI doesn't mean demonizing public LLMs. It means deliberately deciding where to place data, models, and logs based on risk and context.
When it's needed
- Confidential data
- Internal documents
- Contracts
- Health or sensitive data
- Source code
- Operational procedures
- Public administration data
- Intellectual property
- Regulatory constraints
Architecture options
- Public cloud LLMs
- Controlled external APIs
- Self-hosted open-source models
- Local GPU inference
- Dedicated servers
- Containerized clusters
- Private RAG
- Air-gapped or semi-isolated environments
The right questions
Architecture comes before tools. Technical placement must answer verifiable questions.
Where does the model run?
Cloud, private, on-premise, local GPU, or hybrid.
What gets logged?
Prompts, outputs, embeddings, metadata, and accesses.
Who can access it?
Roles, policies, audit, and isolation.
What does inference cost?
GPU, API, latency, throughput, and maintenance.
Which outputs must be verified?
Technical gates before automations or operational decisions.
Output
Concrete artifacts to make an AI system controllable.
- Private AI architecture
- Proof of concept
- Internal RAG
- Conversational document system
- Local LLM infrastructure
- Containerized deployment
- Access policies
- Technical documentation
Evaluate a private AI solution
Data, models, and applications must sit in the right place. Let's start from risk and context.