Cheaper than per-seat ChatGPT Enterprise at scale, once seat count crosses ~100 users.
Major LLMs supported — OpenAI, Anthropic, Gemini, Bedrock, plus self-hosted Llama, Mistral, Qwen.
Your prompts, files, and chat history stay in your tenant — no third-party LLM provider in the data path.
What you get from a private ChatGPT deployment
Six outcomes regulated and at-scale teams see when they replace ChatGPT Enterprise with a self-hosted chat UI.
Multi-Model Chat UI
OpenAI, Anthropic, Gemini, AWS Bedrock, plus self-hosted Llama / Mistral / Qwen. One chat interface, multiple model backends, switchable per-conversation.
Custom Assistants Per Team
Build dedicated assistants for sales, support, legal, engineering — each with custom system prompts, scoped tools, and per-team document access.
Optional RAG on Your Docs
Connect Slack, Drive, Confluence, iManage. Assistant answers with grounded citations to source paragraphs. Permission-aware retrieval respects each source's ACLs.
Self-Hosted in Your Tenant
LibreChat or Open WebUI runs in your VPC, on-prem, or air-gapped. Prompts, files, and chat history never leave the environment your security team already owns.
Bring Your Own LLM
Route to OpenAI, Anthropic, Gemini via your enterprise contract — or self-host Llama, Mistral, Qwen on vLLM or Ollama. Switch models without rebuilding the stack.
Flat Licensing Economics
One-time deployment plus an optional managed retainer. No per-seat surprises as headcount grows. Past 100 seats, 5–10× cheaper than ChatGPT Enterprise list pricing.
Why ChatGPT Enterprise and multi-tenant chat SaaS miss
ChatGPT Enterprise, Claude Enterprise, and other multi-tenant chat SaaS were built around a single assumption: it’s fine for your prompts, files, and chat history to sit in the vendor’s cloud. That works at small scale, but it leaves three problems on autopilot the moment usage gets serious: (1) per-seat pricing that punishes you for rolling the tool out to the people who’d benefit most; (2) data exposure that fails legal, compliance, or InfoSec review past pilot; and (3) lock-in to a single vendor’s model roadmap, with no way to A/B-test models or route sensitive prompts to self-hosted ones.
LibreChat and Open WebUI are the open-source answer. Same chat UX, same SSO/RBAC, same agents framework — except the stack runs in your tenant and you choose which model handles which prompt.
Inside private ChatGPT — the 8 capabilities we deploy
Eight capabilities LibreChat and Open WebUI deliver — together they cover every job you’d hire ChatGPT Enterprise for, plus the things vendor SaaS structurally can’t do because it’s multi-tenant.
1. Multi-model chat UI (LibreChat or Open WebUI)
A polished ChatGPT-style chat interface that supports OpenAI, Anthropic, Gemini, AWS Bedrock, and any self-hosted model. Switch models per conversation, save and share conversations across the team, native markdown and code blocks, file uploads with image and document understanding. Battle-tested at Shopify, Daimler, Boston University, ClickHouse, and Stripe.
2. Custom assistants and agents per team
Build dedicated assistants for sales, support, legal, and engineering — each with custom system prompts, scoped tools (code interpreter, web search, MCP servers), and per-team document access. The sales assistant doesn’t pull from internal-only engineering docs; the legal assistant has access to the DMS the engineering assistant doesn’t.
3. Optional RAG over your corporate documents
Connect Slack, Drive, Confluence, iManage, NetDocuments, SharePoint. The assistant answers with grounded citations linked back to source paragraphs. Permission-aware retrieval respects each source’s ACLs — users only see results from documents they have access to in the source app.
4. Bring-your-own-LLM gateway
LibreChat and Open WebUI are model-agnostic. Route to OpenAI, Anthropic, Gemini, AWS Bedrock via your enterprise contract — or self-host Llama, Mistral, Qwen on vLLM, SGLang, or Ollama. Swap models, run A/B tests, or set per-team routing rules without rebuilding the rest of the stack.
5. Code interpreter and web search
Built-in code interpreter for Python, JavaScript, and SQL execution in a sandboxed environment. Web search via Tavily, Brave, SearXNG, or a self-hosted crawler. Both gated by per-team permissions — not every assistant gets web access, and code execution can be scoped to specific assistants or specific users.
6. Native Slack and Teams integration
Use the assistant directly from Slack DMs, channel mentions, or Microsoft Teams threads. Adoption is dramatically higher than “yet another tab” deployments because the assistant lives where work already happens. Permission-aware retrieval still applies — channel-based answers only surface results the asker has access to in source apps.
7. Air-gapped and on-prem deployment
LibreChat and Open WebUI run in your VPC, on bare-metal in your data center, or fully air-gapped for classified environments. The full stack — chat UI, model gateway, vector store (if RAG is enabled), optional self-hosted model serving — deploys in one Kubernetes namespace or Docker Compose stack. No prompts, files, or chat history leave your perimeter.
8. Enterprise audit, SSO, and RBAC
SAML SSO and OIDC integrations for Okta, Azure AD, JumpCloud, and Google Workspace. Role-based admin controls. Full audit trails of who prompted what, which model responded, what context was retrieved, and which documents were cited — the audit pack your CISO and regulator expect, and the part vendor SaaS charges separately for at the enterprise tier.
Talk to a private ChatGPT expert
Bring us your seat count, model preferences, and data-sensitivity profile. We’ll come prepared with the right deployment shape — LibreChat or Open WebUI, BYO-LLM gateway, optional RAG — and a directional read on what you can stand up in your tenant next sprint.
Ask us about
- LibreChat or Open WebUI deployment and team onboarding
- Migrating from ChatGPT Team / Plus / Enterprise
- BYO-LLM routing across OpenAI, Anthropic, and self-hosted models
- Optional RAG over Slack, Drive, Confluence, iManage
- Air-gapped and on-prem deployment for regulated industries
- Custom assistants per team, with audit logs and RBAC
When you need private ChatGPT, not vendor SaaS
ChatGPT Enterprise and Claude Enterprise cover the median knowledge worker well — single-vendor chat, generic agents, vendor-hosted everything. That’s enough if your data-sensitivity profile and seat count fit the average customer.
But teams winning on private AI need things multi-tenant SaaS structurally can’t deliver:
- Chat history and prompts inside your tenant — not in a vendor’s multi-tenant cloud
- Multi-model routing — OpenAI, Anthropic, Gemini plus self-hosted Llama / Mistral / Qwen
- Custom agents per team — with custom system prompts, scoped tools, and per-team document access
- Audit logs your CISO and regulator can audit — not a vendor SOC report
- Flat licensing economics — not per-seat charges that compound as headcount grows
- Integrated with your stack — Slack, Drive, Confluence, iManage, SharePoint — not a parallel SaaS island
Private ChatGPT runs inside the environment your security team already owns, and gets better the more your people use it — without locking you into a single vendor’s model roadmap or pricing curve.
Frequently asked questions
Related solutions in the private-AI cluster
Additional resources
AI Transformation Workshop
Half-day strategy workshop to map your connector landscape and identify the right LibreChat deployment shape for your environment. Book a workshop →
AI Strategy Session
60-minute scoping call. We’ll talk through your current search stack, seat economics, and data-residency profile, then sketch the right LibreChat deployment. Book a session →
AI Consultant vs In-House Team
Honest tradeoffs on bringing the LibreChat deployment in-house versus engaging a partner for build + managed. Read the comparison →
Ready to deploy private ChatGPT?
A 45-minute strategy call. We’ll review your current chat stack, seat economics, and model preferences — then come back with a concrete deployment shape, model routing plan, and rollout sequence.