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 — self-hosted in your own cloud account, or fully managed on Grengin Cloud. Governance built in, no per-seat pricing. Here's how both paths stack up against three enterprise alternatives.
Open source Multi-model No per-seat pricing Runs in your cloud account 5-minute deployment
Jump to a head-to-head
From LibreChat to ChatGPT Enterprise: The Enterprise AI Landscape
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 — including which of the two Grengin paths fits you.
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
The same open-source, multi-model platform — cost analytics, usage caps, PII detection, and audit logs built in. Pick who runs the infrastructure.
Self-hosted
Deploy the VM in your own AWS account. You hold the keys — Perter is never in the data path.
Grengin Cloud
We run it for you on managed, isolated infrastructure. Sign up and invite your team in minutes — no infra to own.
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. Grengin is split into its two paths: self-hosted and Grengin Cloud.
| Dimension | Grengin Self-hosted | Grengin Cloud | ChatGPT Enterprise | Microsoft 365 Copilot | LibreChat |
|---|---|---|---|---|---|
| Software model | Open source | Open source | Proprietary | Proprietary | Open source (MIT) |
| Where it runs | Your AWS account (VM) | Grengin's managed infra, isolated per workspace | OpenAI infrastructure | Microsoft cloud only | Self-hosted (you assemble the stack) |
| Models | OpenAI, Anthropic, Google Gemini, Mistral, Groq, Cerebras | OpenAI, Anthropic, Google Gemini, Mistral, Groq, Cerebras | OpenAI only | OpenAI via Azure (+ selective Anthropic) | Broad — any OpenAI-compatible endpoint |
| Multi-model in one chat | Yes | Yes | No | No | Yes |
| Pricing model | Free software — pay only cloud compute + LLM providers | Flat fee per workspace + LLM providers — no seats | ~$60 / user / mo, 150-seat min (typical) | $30 / user / mo + qualifying M365 license | $0 software; you fund hosting, DevOps & gateway |
| Prerequisites | AWS account + any SAML 2.0 / OIDC IdP | None — sign up and invite your team | OpenAI commitment | M365 license + Entra ID + Office estate | DevOps team + 5-service stack |
| Time to first user | ~5 minutes | ~2 minutes | Days to weeks | Weeks (tenant rollout) | 30 minutes to days |
| Built-in governance (cost caps, PII, audit export) | In the VM | In the box | Partial | Via Purview (E3/E5) | Add a gateway |
| Vendor in the data path | No | Yes — we operate the app | Yes | Yes | No |
| Best for | Teams wanting full infra control | Teams wanting zero ops, fastest start | 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.
Deep Dive: Grengin vs ChatGPT Enterprise, Copilot & LibreChat
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 is free software with no per-seat fee — you pay only cloud compute and LLM providers.
- Multi-LLM by default. Use Claude, Gemini, Groq and more — not OpenAI alone.
- Your infra or our Cloud. Self-host in your AWS account — or launch fully managed on Grengin Cloud.
~$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.
- Self-host or Cloud. Marketplace VM in minutes — or skip infra with Grengin Cloud.
- 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)
Why Teams Choose Grengin, Across Every Comparison
Across all three comparisons, the same advantages show up. Grengin is the private, multi-model AI workspace teams can stand up themselves — self-hosted in your cloud, or managed on Grengin Cloud — without a procurement cycle, a Microsoft dependency, or a DevOps project.
1
Your cloud or ours
Self-host a single-tenant VM in your AWS account — your keys, your region, your SIEM — or launch on Grengin Cloud and we run the same software for you.
2
Multi-model, one interface
OpenAI, Anthropic, Google Gemini, Mistral, Groq, and Cerebras — switchable mid-conversation, with one audit log and one bill.
3
No per-seat pricing
Self-host is free software — you pay only cloud compute and LLM providers. Grengin Cloud is a flat fee per workspace, never per seat.
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. Same software on Cloud and self-host — your platform never becomes a black box you can't inspect or leave.
6
Live in minutes
Grengin Cloud is the faster start. Or deploy from the AWS Marketplace in about five minutes — connect SSO, invite your team, no sales cycle.
FAQ
Enterprise Conversational AI Platform: Frequently Asked Questions
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 access, enterprise AI governance, and no per-seat pricing — either self-hosted in your own cloud account, or fully managed on Grengin Cloud.
What is the difference between self-hosted Grengin and Grengin Cloud?
Same open-source software either way. Self-hosted means you deploy the Marketplace VM (or build from source) in your own AWS account: you hold the infrastructure, and Perter is never in the data path. Grengin Cloud is managed hosting — we operate the app on isolated infrastructure for you, so you can sign up and invite your team without owning infra. Cloud is the one path where traffic passes through systems we run; we say that plainly. You can export from Cloud into your own account later because the schema is identical.
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. Self-hosted Grengin deploys as a VM in your own AWS account, so Perter 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. Grengin Cloud is managed hosting — we operate the app — so it is the honest exception: traffic passes through infrastructure we run, with exportable audit trails and governance still built in.
Is there an enterprise AI platform without per-seat pricing?
Yes. Grengin has no per-seat pricing and no seat minimum — unlike ChatGPT Enterprise pricing which starts at ~150 seats before anyone sends a single prompt. Self-host: the software and Marketplace image are free; you pay AWS for compute at standard rates, and LLM usage to the provider at cost. Grengin Cloud: a flat fee per workspace plus your model providers — still never per seat. Either way your Grengin bill does not scale with headcount.
Can my team use multiple AI models in one place?
With Grengin's conversational AI platform, 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. That works the same on self-hosted and Grengin Cloud. 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 Cloud is the faster launch — sign up, connect keys and SSO, invite your team in minutes. Self-host from the AWS Marketplace takes about 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 — AI governance solutions are the real differentiator. LibreChat is a strong open source AI 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 AI enterprise governance behaviors in the product — whether you self-host the VM image or run on Grengin Cloud — so they work from day one, and because the code is open, your security team can still audit it.
Ready to start your enterprise AI adoption?
The simplest test is to deploy Grengin alongside whatever you're evaluating — self-hosted in your own AWS account, or fully managed on Grengin Cloud. No procurement cycle, no implementation partner.