Private AI means running generative AI entirely inside your own infrastructure: your models, your servers, your network, your data. Nothing leaves. Almost all of my private AI work runs on open-weight models such as Llama, Qwen, Gemma, Mistral, DeepSeek and gpt-oss, served with open source runtimes such as Ollama and vLLM. Open source is what makes private AI possible at all: you cannot run a model inside your own network unless you are allowed to download it. I help organizations design, deploy, and operate these systems, from the first hardware sizing conversation to a production deployment your team runs confidently on its own. This specialty was born of necessity: when I brought AI tools into my own projects, the problems that hit me immediately were security, performance, and privacy. Solving them for myself became a practice, and that practice became the work I now do for others.
Why private AI
- Data control: your source code, documents, and customer data never travel to a third-party API. There is no prompt log sitting on someone else’s servers.
- Compliance: if your industry or your clients demand that data stays in-house (healthcare, finance, legal, government, defense), private deployment is not a preference, it is the requirement.
- Predictable cost and latency: no metered API bills that scale with your team’s enthusiasm, no rate limits, no dependence on someone else’s uptime. Inference runs at the speed of your own hardware, every time.
- Real capability: modern open-weight models are genuinely good. For most internal workloads, a well-chosen local model on the right hardware covers the need, and the gap narrows every quarter.
- No vendor lock-in: an open-weight model you have downloaded cannot be deprecated, repriced or changed under you. You can pin the version, fine-tune it, and swap it for the next family when it is better, on your own schedule.
Built on open source AI
Two terms get mixed up, and the difference decides what you can ship. Open-weight means the parameters are published and you may run them. Open source, in the OSI sense, also means the training code and the data information are open. Most of what I deploy for private AI is open-weight under permissive terms, and I keep the vocabulary precise: see what open source AI actually means.
Licenses are the first check, not the last. Apache-2.0 and MIT weights come with no strings. Llama and Gemma come with their own terms: acceptable-use policies, scale thresholds, naming duties. I read the license file, not the model card, before a model goes anywhere near production, and I record the decision. My comparison of open model licenses is the checklist I use.
The runtimes are open source too: Ollama and llama.cpp for developer machines and small servers, vLLM for shared GPU serving, plus open chat interfaces, RAG frameworks and vector stores. For engineers, open coding assistants such as OpenCode work against local models. The full open source AI toolbox lists every tool with its license.
Model choice is per use case: family, size, quantization, and the hardware it needs, which is what AILane measures. I keep a map of the open model landscape current for that reason. Open is not always the answer; a managed model is sometimes right, and when it is, the Stepladder Framework keeps it behind the same contract as your own models. The trade-offs are spelled out in when to choose open source AI, and when not to, and all of it is collected in my Open Source AI Hub.
What I deploy
Local model runtimes
Self-hosted inference of open-weight models with open source runtimes like Ollama, llama.cpp and vLLM: model selection, quantization choices, GPU and memory sizing, and the operational setup to keep models updated and monitored. I published ailane, an open-source tool that inspects a machine’s hardware and reports which local AI models it can run, because sizing is the first question every deployment faces.
Model selection and license review
Choosing the model family and size for each use case, checking the license and its restrictions against how you will actually use it, and recording that decision so it survives audits and staff changes.
Private AI-assisted development
Coding assistants that work against local or self-hosted models, including open coding assistants such as OpenCode, so your engineers get AI help without code leaving the network. This includes the workflows, review discipline, and quality control that make AI assistance an asset in production, the subject of my book You Are the Quality Control.
Internal knowledge systems
Retrieval-augmented generation over your own documents, wikis, and codebases: your institutional knowledge made queryable, without indexing any of it into an external service.
Team enablement
Training and runbooks so the deployment survives contact with real use: who updates models, how quality is evaluated, what the escalation path is when output goes wrong. A private AI system your team cannot operate is a demo, not a deployment.
How engagements work
- Assessment: we map your use cases, data constraints, and existing hardware, work out which open-weight models and licenses fit, and decide what private AI should and should not do for you. Sometimes the honest answer is a hybrid, and I will tell you so. When it is, I build it on my Stepladder Framework: one contract in front of managed services and your own models, with traffic moved between them by configuration.
- Pilot: a scoped deployment on real workloads with clear evaluation criteria, so the decision to go further is made on evidence.
- Production: hardening, monitoring, model update strategy, and handover to your team.
- Training: hands-on sessions for the engineers who will live with the system.
Private AI deployment is one part of my wider consulting practice, which also covers AI-assisted development adoption, software architecture, and cloud modernization.
Guides and articles
I document this work continuously. Start here:
- The Stepladder Framework: the pattern I use to move a use case from managed AI services to models you run yourself, one rung at a time.
- Open Source AI Hub: what open source AI means, how model licenses work, the open model landscape and toolbox, and when to choose open.
- On-Premise AI vs Cloud AI: Which Should Your Company Choose?: the decision framework I use with clients.
- Security Risks of AI Coding Tools: the six risks I actually see in practice, and the discipline that answers each.
- How to Run LLMs Inside Your Company Network: the seven-step path from one GPU server to a service your team depends on.
- How to Build a Private RAG System Over Your Company’s Documents: making your internal documents queryable without indexing a word of them into an external service.
- Private AI Architecture Checklist: the audit list I walk before calling a deployment production-ready.
- Ollama for Teams: Local AI Deployment Guide: turning the tool your developers already know into a shared, governed service.
- How to Run AI Models Locally and in the Cloud with Ollama: the hands-on starting point for self-hosted models.
- AI & Code Experiments: setup guides for Claude Code, GitHub Copilot, and the OpenAI Codex CLI, plus ongoing experiments.
New guides land here as they are published.
Start a conversation
If your organization wants the productivity of generative AI without handing its data to a third party, tell me about your situation: contact me here.