ChatGPT Enterprise
One model family from one vendor, on OpenAI infrastructure, sold per seat with a minimum commitment. Polished and powerful for large, OpenAI-committed organizations — but priced for Fortune 500 procurement.
Platform comparisons · 2026
The enterprise AI landscape,
The enterprise AI landscape,
compared honestly.
Grengin is the open-source, multi-model option you deploy inside your own cloud account — governance built in, no per-seat pricing, no vendor in your data path. Here's how the four approaches stack up.
Open source Multi-model No per-seat pricing Runs in your cloud account 5-minute deployment
Jump to a head-to-head
Four shapes of enterprise AI
Before picking a tool, it helps to understand what you're actually buying. Each of these solves a different problem, and the right answer depends on your stack, your budget, and how much control you need.
Microsoft 365 Copilot
AI woven into Word, Excel, PowerPoint, Outlook, and Teams, grounded in your Microsoft Graph. Hard to beat for in-Office work — if your whole stack and identity already run on Microsoft.
LibreChat
A capable, MIT-licensed, multi-model project you assemble and host yourself. Excellent extensibility and the freedom of running everything on your own infrastructure — if you have the DevOps capacity to operate it.
Grengin Where Grengin fits
Open-source, multi-model AI that deploys as a VM in your own AWS or Azure account in about five minutes — with cost analytics, usage caps, PII detection, and audit logs already in the image. No per-seat math, no vendor in the data path.
At a glance: Grengin vs the field
The dimensions that decide most enterprise AI evaluations — software model, where it runs, model choice, pricing, speed, governance, and who can see your data.
| Dimension | Grengin | ChatGPT Enterprise | Microsoft 365 Copilot | LibreChat |
|---|---|---|---|---|
| Software model | Open source | Proprietary | Proprietary | Open source (MIT) |
| Where it runs | Your AWS / Azure account, or self-hosted | OpenAI infrastructure | Microsoft cloud only | Self-hosted (you assemble the stack) |
| Models | OpenAI, Anthropic, Google, Groq, Cerebras, Hugging Face | OpenAI only | OpenAI via Azure (+ selective Anthropic) | Broad — any OpenAI-compatible endpoint |
| Multi-model in one chat | Yes | No | No | Yes |
| Pricing model | Compute-metered ($0.001 / vCPU-hr) — no per-seat fee | ~$60 / user / mo, 150-seat min (typical) | $30 / user / mo + qualifying M365 license | $0 software; you fund hosting, DevOps & gateway |
| Prerequisites | None — any SAML 2.0 / OIDC IdP | OpenAI commitment | M365 license + Entra ID + Office estate | DevOps team + 5-service stack |
| Time to first user | ~5 minutes | Days to weeks | Weeks (tenant rollout) | 30 minutes to days |
| Built-in governance (cost caps, PII, audit export) | In the VM | Partial | Via Purview (E3/E5) | Add a gateway |
| Vendor in the data path | No | Yes | Yes | No |
| Best for | Mixed-stack SMB & mid-market wanting control | Large, OpenAI-committed enterprises | Microsoft-native teams; in-Office AI | Teams with DevOps capacity who want to build |
LLM usage is paid to OpenAI, Anthropic, Google and others at provider rates on every platform — via BYOK or pass-through at cost. Figures reflect typical 2026 deployments; see each comparison for the full methodology.
Read the head-to-head comparisons
Each page is an honest, section-by-section breakdown — capabilities, models, security, privacy, integrations, governance, pricing, and deployment — plus a step-by-step migration guide.
ChatGPT Enterprise
Grengin vs ChatGPT Enterprise
Best for: large, OpenAI-committed enterprises that want a polished single-vendor assistant.
OpenAI's enterprise tier bundles the GPT-5.x family, unlimited usage, SSO, SCIM, and strong certifications into a per-seat subscription — typically around $60 per user per month with a 150-seat minimum. One model family, one administrative envelope, hosted on OpenAI's infrastructure.
- No 150-seat tax. Grengin meters by vCPU-hour, not by employee — cost scales with usage.
- Multi-LLM by default. Use Claude, Gemini, Groq and more — not OpenAI alone.
- Nobody in your data path. Runs single-tenant in your own AWS or Azure account.
~$60/user/mo · 150-seat minimum (typical)
Microsoft 365 Copilot
Grengin vs Microsoft 365 Copilot
Best for: Microsoft-native teams whose AI use cases live inside Office apps.
Copilot embeds AI into Word, Excel, PowerPoint, Outlook, and Teams, grounded in your tenant Microsoft Graph and governed through Purview. Genuinely transformative for in-Office work — but it requires a qualifying M365 license, Entra ID, and the full Microsoft data estate, at $30 per user per month on top of what you already pay.
- No Microsoft tax. No E3/E5, no OneDrive/SharePoint/Teams, no Entra ID requirement.
- Works with any stack. Google Workspace, Slack, Notion, Linear — and any SAML/OIDC IdP.
- Multi-model, not OpenAI-via-Azure-shaped. Pick the right model per task.
$30/user/mo + qualifying M365 base license
LibreChat
Grengin vs LibreChat
Best for: teams with DevOps capacity who want to assemble and own the stack.
LibreChat is one of the most successful open-source AI projects — MIT-licensed, multi-model, with mature agent and MCP support. The catch is operations: a production deployment means LibreChat plus MongoDB, Meilisearch, PGVector, a RAG API, a reverse proxy, and SSL — and teams routinely add a separate gateway for cost tracking, PII detection, and audit.
- Five-minute VM, not a five-day project. One image instead of five services.
- Governance in the box. Cost analytics, hard caps, PII detection and audit export pre-installed.
- Same open-source freedom. Audit it, fork it, self-host from source if you outgrow it.
$0 software · ~$13,600–$45,400/yr realistic TCO (50 users)
The common thread
Across all three comparisons, the same advantages show up. Grengin is built to be the private, multi-model AI workspace teams can stand up themselves — without a procurement cycle, a Microsoft dependency, or a DevOps project.
1
Runs in your own cloud
A single-tenant VM in your AWS or Azure account — your KMS keys, your region, your SIEM. Grengin Inc. is never in the data path.
2
Multi-model, one interface
OpenAI, Anthropic, Google, Groq, Cerebras, and open-source models — switchable mid-conversation, with one audit log and one bill.
3
No per-seat pricing
The VM image is metered at $0.001 per vCPU-hour. You pay compute at standard rates and LLM usage at provider cost — that's it.
4
Governance built in
Real-time per-user, per-department, per-project cost analytics, hard or soft usage caps, PII detection at the prompt layer, and exportable audit logs.
5
Open source
Audit the code, fork it, or run it on your own metal. Your platform never becomes a black box you can't inspect or leave.
6
Live in five minutes
Deploy from the AWS or Azure Marketplace, connect SSO, invite your team — no sales cycle, no implementation partner, no tenant cleanup.
FAQ
Choosing an enterprise AI platform
What is the best enterprise AI platform?
There's no single best platform — it depends on your stack. ChatGPT Enterprise suits large, OpenAI-committed organizations; Microsoft 365 Copilot suits Microsoft-native teams whose AI lives inside Office apps; LibreChat suits teams with DevOps capacity who want to build their own stack. Grengin is built for mixed-stack SMBs and mid-market teams that want multi-model AI, no per-seat pricing, and governance running inside their own cloud account.
What is the most private and secure AI for business?
Privacy comes down to where the software runs. With SaaS products like ChatGPT Enterprise and Microsoft 365 Copilot, the vendor's hosting, key management, and incident response sit in your data path — so their certifications are the central question. Grengin is open-source software you deploy as a VM in your own AWS or Azure account, so Grengin Inc. is not in the data path: the app runs inside your existing security boundary, and the only thing that leaves your VPC is the LLM API call to the provider you choose.
Is there an enterprise AI platform without per-seat pricing?
Yes. Grengin has no per-seat pricing and no seat minimum. The Marketplace VM is metered at $0.001 per vCPU-hour — around $3 per month for a 4-vCPU instance — you pay AWS or Azure for compute at standard rates, and LLM usage is paid to the provider at cost. Your bill scales with usage, not headcount, which is why the gap with per-seat products widens as a team grows.
Can my team use multiple AI models in one place?
With Grengin, yes — a user can start a conversation on Claude for a long document, switch to GPT to refine, ask Gemini Flash to translate, and use Groq for fast drafts, all in one thread. ChatGPT Enterprise is OpenAI-only and Microsoft 365 Copilot is OpenAI-via-Azure, so genuine multi-model access on those platforms usually means adding a second tool — which becomes another shadow-AI surface to govern.
How fast can an enterprise AI platform be deployed?
Grengin deploys from the AWS or Azure Marketplace in roughly five minutes: boot the VM, connect SSO, invite your team. ChatGPT Enterprise typically runs days to weeks for the sales cycle and provisioning, Microsoft 365 Copilot requires tenant readiness and a rollout, and a production-ready LibreChat stack ranges from 30 minutes to several days — plus ongoing maintenance for each new CVE.
Is open-source AI enterprise-ready?
It can be — governance is the real differentiator. LibreChat is a strong open-source project, but production teams commonly bolt on a separate AI gateway to get cost tracking, PII detection, and compliance-grade audit export. Grengin is also open source, but ships those governance behaviors inside the VM image, so they work the moment it boots — and because the code is open, your security team can still audit it.
Ready to compare for yourself?
The simplest test is to deploy Grengin alongside whatever you're evaluating. Five-minute Marketplace deployment in your own AWS or Azure account — no procurement cycle, no implementation partner.