Private ChatGPT for Business, Deployed on Your Infrastructure
A self-hosted, ChatGPT-style workspace on LibreChat or Open WebUI, wired into your Slack, Drive, Confluence and corporate docs. It retires the ChatGPT Team and Plus subscriptions your people already expense, keeps every prompt inside your tenant, and takes per-seat pricing off the table.
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, and engineering — each with custom system prompts, scoped tools, and per-team document access.
Optional RAG on Your Docs
Connect Slack, Drive, Confluence, iManage. The 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 the other multi-tenant chat tools all rest on one assumption: that you are fine with your prompts, files and chat history sitting in the vendor's cloud. It holds up in a pilot. The moment real usage arrives, three problems start compounding on their own:
LibreChat and Open WebUI are the open-source answer.
Same chat experience, same SSO and RBAC, same agents framework, except the stack runs in your tenant and you decide which model handles which prompt. Your prompts, uploaded files, fine-tunes and chat history stay under controls your security team already owns, with no third-party LLM provider in the data path unless you deliberately route a conversation to one.
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.
Multi-model chat UI (LibreChat or Open WebUI)
A polished ChatGPT-style interface supporting 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.
Custom assistants and agents per team
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 reaches the DMS the engineering assistant can't.
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.
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.
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.
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 answers only surface results the asker has access to in source apps.
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, optional self-hosted model serving — deploys in one Kubernetes namespace or Docker Compose stack. No prompts, files, or chat history leave your perimeter.
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.
Talk to a Private ChatGPT Expert
Bring your seat count, model preferences and data-sensitivity profile. We come back with a concrete deployment shape (LibreChat or Open WebUI, a BYO-LLM gateway, optional RAG) and a straight read on what you could stand up in your tenant this quarter.
Book a Strategy Session →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. But teams winning on private AI need things multi-tenant SaaS structurally can't deliver:
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
For the core job — multi-model chat UI with team management, SSO, optional RAG, and a custom-agents framework — yes. LibreChat ships a polished ChatGPT-style UX, supports OpenAI / Anthropic / Gemini / Bedrock plus self-hosted models, and has an active enterprise edition. What you give up: a vendor handling upgrades and a slick onboarding experience. What you gain: data sovereignty, flat economics past ~100 seats, multi-model routing, and full control over which models your team uses.
A 100-person company on ChatGPT Enterprise at ~$60/seat/month is ~$72K/year. At 500 seats, ChatGPT Enterprise is $360K–$600K/year and climbing. A standard LibreChat or Open WebUI deployment plus a year of managed service runs roughly the same as the 100-seat year-one tier — and you own the stack from year two on. The gap widens dramatically past 100 seats.
All of them. LibreChat and Open WebUI are model-agnostic. Route to OpenAI, Anthropic, Gemini, AWS Bedrock, Cohere, or Mistral via your enterprise contract — or self-host Llama, Mistral, Qwen via vLLM / Ollama. Different teams can use different models; sensitive prompts can route to self-hosted models while general queries hit a vendor API. Per-team or per-conversation model routing is configurable.
Chat history is stored in your tenant's database (Postgres or MongoDB). Prompts routed to vendor APIs go through your enterprise contract with that vendor (OpenAI Enterprise, Anthropic Enterprise, etc.), which has its own no-training, no-retention guarantees. Prompts routed to self-hosted models never leave your network. The infrastructure your security team already owns becomes the perimeter.
Yes. LibreChat and Open WebUI both ship as Docker and Kubernetes deployments. Pair with self-hosted vLLM or Ollama for fully air-gapped chat that never touches the public internet. Common pattern: chat UI in your VPC, model serving on your GPU cluster, observability via your existing tooling. Pure on-prem and air-gapped variants are part of every regulated-industry engagement.
Every engagement includes deployment, model-gateway setup, SSO and RBAC configuration, branding, and a team-onboarding playbook. After that, an optional managed retainer covers monitoring, version upgrades, model additions, custom-agent buildouts, and quarterly reviews. Or you can take ownership in-house — we hand off a complete runbook and Infrastructure-as-Code repo either way.
Related Solutions in the Private-AI Cluster
Self-Hosted Enterprise Search, Deployed in Your Tenant
Learn more →Private RAG — Chat With Your Documents Inside Your Tenant
Learn more →Air-Gapped AI for Regulated Industries — Disconnected LLM Deployment
Learn more →Self-Hosted AI Deployment for Business
Learn more →Private AI for Law Firms — Self-Hosted Legal AI Software
Learn more →Private AI Contract Review, Analysis & Lifecycle Management
Learn more →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
30-minute scoping call. We'll talk through your current chat 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 30-minute strategy call. We walk through your current chat stack, seat economics and model preferences, then come back with a concrete deployment shape, a model-routing plan and a rollout sequence you can start on.