Private AI vs. Enterprise AI: What's the Difference?

The Enterprise AI Pitch

If you have been evaluating AI tools for your firm, you have almost certainly heard the enterprise pitch. Microsoft Copilot promises AI integrated into the tools you already use, with enterprise-grade security. ChatGPT Enterprise offers a version of OpenAI's technology with enhanced privacy controls. Google's Duet AI embeds generative AI across Workspace.

The pitch is compelling: all the power of consumer AI, but with the security and compliance features your firm needs. Enterprise-grade encryption. Admin controls. Audit logs. No training on your data.

These are real features. But they obscure a fundamental architectural difference that matters enormously for professional services firms.

Where Your Data Actually Goes

When you use Microsoft Copilot, your queries and documents are processed by Microsoft's AI infrastructure. When you use ChatGPT Enterprise, your data flows through OpenAI's servers. When you use Google's AI tools, Google's infrastructure handles the processing.

"Enterprise" features add layers of access control, data retention policies, and audit logging on top of this architecture. They give you better visibility and control compared to consumer versions. But the underlying reality has not changed: your data is being processed on someone else's infrastructure.

For many businesses, this is perfectly acceptable. But for professional services firms — accounting practices handling client tax returns, healthcare organizations processing patient records, law firms managing litigation strategy — the question is not just "is my data encrypted?" It is "does my data leave my environment at all?"

What "Private AI" Actually Means

Private AI takes a fundamentally different architectural approach. Instead of sending your data to a vendor's AI platform, the AI model itself runs on infrastructure that your firm controls.

This is not a subtle distinction. It is the difference between sending a confidential document to someone else for analysis and having an analyst work on it inside your own office.

With a private AI deployment:

Your data never leaves your environment. Queries, documents, and responses are processed entirely within your firm's infrastructure. There is no API call to a third party. No data in transit to an external server.
You control the infrastructure. The servers, the storage, the network boundaries — they belong to your firm's environment. You define who has access, what gets logged, and how data is retained.
No terms of service govern your data. With enterprise AI products, your data usage is governed by the vendor's terms, which can change. With private AI, there are no external terms because there is no external vendor processing your data.
The model runs locally. Thanks to advances in open-source AI models from Meta (Llama), Mistral, and others, it is now possible to run models that rival proprietary systems entirely on your own infrastructure.

The Claims That Need Scrutiny

Enterprise AI vendors make several claims that deserve closer examination.

"We don't train on your data." This may be true today, for the specific plan you are on. But terms change. Companies get acquired. Business models evolve. The structural guarantee of private AI — that no external party ever sees your data — is fundamentally stronger than a contractual promise from a company whose primary business is building AI models.

"Your data is encrypted." Encryption is necessary but not sufficient. Your data is encrypted in transit and at rest, yes. But it is decrypted for processing on the vendor's infrastructure. The vendor's systems, the vendor's employees with sufficient access, and the vendor's security posture all become part of your firm's risk surface.

"We have SOC 2 certification." SOC 2 is a good baseline. But it certifies the vendor's controls, not your firm's ability to independently verify those controls. For firms in regulated industries where you may need to demonstrate to a regulator exactly where client data was processed and by whom, relying on a vendor's certification adds a layer of dependency.

Who Should Care About This Difference

Not every organization needs private AI. For many companies, enterprise AI products offer a perfectly reasonable balance of capability and security.

But for professional services firms, the calculus is different. Your firm's competitive advantage is built on client trust. Your clients give you their most sensitive information — financial records, health data, legal strategies, proprietary deal terms — because they trust you to protect it.

When you process that information through a third-party AI platform, you are extending that trust to a technology vendor whose interests may not always align with yours. Enterprise AI products minimize this risk. Private AI eliminates it.

This matters most for:

Firms in regulated industries where you may need to demonstrate to regulators exactly how client data is handled and processed
Firms handling high-value, high-sensitivity data where even the perception of third-party access could damage client relationships
Firms where confidentiality is a competitive differentiator and being able to tell clients "your data never leaves our environment" is a meaningful advantage

The Practical Reality

Private AI is not theoretical. Modern open-source models are capable enough for professional use cases. Cloud infrastructure is flexible enough to support dedicated deployments. And managed platforms like Metrovolo handle the complexity of deployment, maintenance, and model upgrades so that firms do not need to become AI companies themselves.

The choice between enterprise AI and private AI is ultimately about what your firm values most. If convenience and integration with existing tools are the priority, enterprise products are strong options. If data sovereignty, regulatory confidence, and the ability to guarantee clients that their data never touches a third-party AI system are the priority, private AI is the right architecture.

For firms that treat client confidentiality as foundational rather than aspirational, the answer is increasingly clear.