Private AI for business: how to use AI and keep your data in-house
By Rafael · Updated · 2 min read
The short answer
Most organizations don't need to run AI on their own servers. A business plan with no-training terms covers most needs. For more control, use AI through your own cloud account or the API under business terms. Running open models on your own hardware keeps data fully in-house, but costs more effort and capability.
Four levels of control
- Business plan of a major AI assistantChatGPT Business, Claude for Work, Gemini in Google Workspace or Microsoft 365 Copilot. Contract terms, admin controls and no training on your data by default. The right starting point for most teams.
- The API under business termsBuild your own assistant or workflow on a provider's API. You decide what data is sent, and business terms don't use it for training by default. Ask providers about data retention options if you handle sensitive information.
- AI inside your own cloud accountMajor clouds offer leading models through your existing account, such as Claude on Amazon Bedrock or Google Cloud Vertex AI, with your cloud's security, regions and agreements. Amazon Bedrock, for example, says it doesn't store your prompts or use them to train models.
- Open models on your own hardwareTools like Ollama or LM Studio run open-weight models (such as Llama, Mistral, Gemma or Qwen) on your own computers or servers. Data never leaves your machines.
The trade-offs of running AI yourself
- Capability: models small enough to run on a laptop or office server are usually less capable than the leading hosted models, especially for complex reasoning and writing.
- Hardware: larger models need a powerful graphics card or a computer with lots of memory.
- Responsibility: you own security, updates, backups and access control. "On our own server" is only private if that server is well secured.
- Cost: little or no usage cost, but real setup and maintenance time.
A common, practical mix: a business AI plan for everyday work, plus a local or own-cloud setup only for the few tasks involving your most sensitive data.
"Chat with our documents" safely
Many organizations want AI that answers questions from their own files: handbooks, policies, past proposals. Whichever level you choose, apply the same rules you use for the files themselves: people should only get answers from documents they're allowed to see. Start with non-sensitive documents and expand once access rules are proven.
How to choose
- General work (writing, summaries, planning): a business plan.
- A custom assistant or automated workflow: the API or your cloud account, under business terms.
- Regulated data such as health information: talk to the provider about plans that support your obligations (for example, HIPAA-eligible setups with a business associate agreement) before sending any.
- Data that must never leave your building: open models on your own hardware.
Whatever you choose, pair it with a short AI policy and the right privacy settings.
Common questions
- What is private AI?
- AI used in a way that keeps your data under your control: business terms that rule out training on your data, AI running inside your own cloud account, or open models running on your own hardware.
- Can I run ChatGPT or Claude on my own computer?
- No. Those models only run on their providers' services. You can run open-weight models such as Llama, Mistral, Gemma or Qwen locally with tools like Ollama or LM Studio.
- Is a local AI model always safer?
- Not automatically. It keeps data off third-party services, but you become responsible for securing the computer, controlling access and keeping software updated.