Vendor Comparison
We won't tell you ChatGPT Enterprise is bad. We'll tell you exactly when it's the right choice and when private AI is.
| Dimension | Vermont AI Systems Private | ChatGPT Enterprise | Copilot for M365 | DIY In-house |
|---|---|---|---|---|
| Data Isolation | Dedicated infrastructure Your data runs on your own VPC or on-prem — never shared multi-tenant. |
Shared multi-tenant Data processed on OpenAI's shared infrastructure. Enterprise terms apply but infrastructure is shared. |
Tenant-isolated within M365 Data stays within your Microsoft 365 tenant boundary, not across tenants. |
Depends on your team As isolated as your team builds it. Could be excellent — or not. |
| Public LLM Training Exposure | None Your data is never used to train any external model, period. |
Opt-out available Enterprise tier excludes training by default, but you're relying on OpenAI's policy. |
None Microsoft commits to not training on M365 tenant data. |
Configurable If you're calling external APIs (e.g., OpenAI), exposure depends on your config. |
| Year 1 Cost (typical) | $84,500–$120K+ $7.5K assessment + $35–75K build + $42K retainer. Scales with complexity. |
$36K–$144K $30/user/month × 100–400 seats. No setup cost, no custom model. |
$36K–$120K $30/user/month (or bundled in E3/E5). Often already licensed. |
$250K–$1M+ Eng salary, GPU infra, fine-tuning, MLOps pipeline, ongoing maintenance. |
| Time to First Production Use Case | ~90 days Assessment → build → deploy in a single focused engagement. |
2–4 weeks SaaS model. Fastest path to broad rollout for general productivity. |
4–8 weeks Native M365 integration accelerates rollout if tenant is already on E3/E5. |
6–12 months Hiring, infra provisioning, model selection, MLOps setup, testing — it adds up. |
| Custom Model Fine-Tuning on Your Data | Yes — exclusively yours Model is fine-tuned on your proprietary data. You own the weights. |
No Enterprise tier doesn't include custom fine-tuning on your data. |
Limited Copilot can use SharePoint/OneDrive content via retrieval, but no model fine-tuning. |
Yes — owned Full control, but requires ML engineering team to execute. |
| Regulatory Fit |
ABA Model Rule 1.6
GLBA / SOC 2
NAIC Model Law
ITAR (with controls)
HIPAA-ready
|
ABA 1.6 — bar association guidance varies
GLBA — BAA available
NAIC — insurer discretion
ITAR — not authorized
HIPAA — BAA available
|
ABA 1.6 — M365 tenant isolation helps but not definitive
GLBA — covered by M365 compliance center
NAIC — depends on implementation
ITAR — not certified
HIPAA — BAA available
|
ABA 1.6 — if built correctly
GLBA — configurable
NAIC — configurable
ITAR — possible with air-gap
HIPAA — configurable
|
| M365 / Google Workspace Integration | Yes, via API We connect to your existing tools. Not native — requires integration config during build. |
Native to M365 / OpenAI ecosystem Plugins, GPTs, and Microsoft's Copilot partnership make integration tight. |
Native to M365 Copilot lives inside Teams, Outlook, Word, Excel. Zero friction if you're M365-native. |
Build it yourself You wire up every connector. Could be done well — but adds months. |
| Internal IP Risk (Employee Data Pasting) | Minimal — no external egress Employees interact with a private model. Data never leaves the VPC. |
Moderate — policy-dependent Enterprise mode limits training, but data still transits OpenAI's infra. Employees may not know what's sensitive. |
Low within M365 boundary Data stays in-tenant. Risk is M365 oversharing, not external egress. |
Depends on architecture Fully internal if you use a local model. Risk exists if you proxy to an external API. |
| Vendor Lock-in | None — you own the weights Model weights, fine-tuning data, and deployment config transfer to you at project close. Month-to-month retainer. |
High Your workflows, integrations, and institutional muscle memory are tied to OpenAI's platform and pricing. |
High Deep M365 integration means switching costs compound over time. You're in Microsoft's ecosystem. |
None You own everything. But the lock-in becomes your team's continued employment and attention. |
| Audit Trail / Compliance Logging | Built-in, exportable Every query, response, and data access is logged. Exportable for eDiscovery or compliance audit. |
Available in Enterprise tier Conversation export and admin audit logs available — not always in the format regulators expect. |
Via Microsoft Purview Compliance logging through Purview. Requires proper configuration — not on by default. |
Build it Logging infrastructure is your responsibility. Gets complex fast under HIPAA or GLBA. |
| Support Model | Tim + team, direct access You're talking to the people who built your model. No tickets, no tier-1 queue. |
Tiered enterprise support Priority support with SLAs available at Enterprise. Routed through account management. |
M365 / Microsoft support Standard M365 enterprise support. Copilot-specific issues may require specialist escalation. |
Internal team only You own the support burden. That team has a lot of other things to do. |
| Best For | Regulated industries, IP-sensitive operations, audit pressure If a breach or compliance failure would be career-ending, this is the right choice. |
Broad productivity, low-sensitivity data, fast rollout Great for general-purpose productivity where data isn't regulated or competitively sensitive. |
M365-native shops, productivity assist, not a competitive differentiator Best when you're already deep in Microsoft and want AI inside the tools you use daily. |
Companies with permanent ML teams and $5M+ AI budgets If you have the people and budget to own this indefinitely, DIY gives maximum control. |
Vermont AI Systems
ChatGPT Enterprise
Copilot for M365
DIY In-house
Neither of these requires a sales call. Use them to sharpen your own thinking.