Workplace-app connectors out of the box: Slack, Drive, Confluence, GitHub, Jira, Salesforce, SharePoint, Notion, and more — plus custom connectors for industry systems.
Cheaper than ChatGPT Enterprise + Glean per-seat economics once you scale past ~100 users, with the gap widening every year.
Chat, search, embeddings, vector store, model serving, and audit logs stay in your tenant — no third-party vendor in the data path.
What you get from a self-hosted AI deployment
Six outcomes companies see when they move private AI off ChatGPT Enterprise + Glean and onto a unified self-hosted stack they fully own.
End-to-End Private AI Stack
Chat UI (LibreChat / Open WebUI), enterprise search (Onyx), and model serving (vLLM / Ollama) deployed end-to-end as one integrated stack — not three open-source projects you wire together over two quarters.
Permission-Aware Retrieval
Connectors sync incrementally and respect each source's native ACLs. Users only see results from documents they already have permission to view in the source app — no share-everything default.
Chat with Cited Answers
Chat over your corporate knowledge with grounded answers that link back to the source paragraphs. No hallucinated facts, no opaque “the AI said it” answers — every claim is traceable.
Self-Hosted in Your Tenant
Runs in your VPC, on-prem, or air-gapped. Documents, embeddings, chat history, and model weights never leave the environment your security team already owns.
Bring Your Own LLM
OpenAI, Anthropic, Gemini, Bedrock via your enterprise contract — or self-hosted Llama, Mistral, Qwen, DeepSeek via vLLM / SGLang / Ollama. Switch models or A/B test without rebuilding.
Flat Licensing Economics
One-time deployment plus an optional managed retainer. No per-seat surprises as headcount grows. At scale, 5–10× cheaper than ChatGPT Enterprise + Glean per-seat list.
Why ChatGPT Enterprise, Glean, and other multi-tenant AI SaaS miss
ChatGPT Enterprise, Glean, and other multi-tenant AI SaaS were built around a single assumption: it’s fine for your prompts, documents, embeddings, and chat traffic to sit in the vendor’s cloud. That works at small scale, but it leaves three problems on autopilot the moment the business gets serious: (1) per-seat pricing that punishes you for rolling AI out to the people who’d benefit most; (2) data exposure that fails legal, compliance, or InfoSec review past pilot; and (3) ranking, model selection, and assistant behavior tuned for the median customer, not yours.
A self-hosted AI deployment — LibreChat + Onyx + vLLM as one integrated stack — is the open-source answer. Same connector breadth, same permission-aware retrieval, same chat-with-citations UX, plus BYO-LLM routing and on-prem model serving. Except it runs in your tenant on infrastructure you already own, and it gets smarter from your usage, not the next vendor customer’s.
Inside a self-hosted AI deployment — the 8 capabilities we build
Eight capabilities your self-hosted AI stack delivers in one unified deployment — private chat, enterprise search, BYO-LLM serving, and the operational layer that holds them together. The whole point of the stack is that it’s all wired up on day one, not three open-source projects you spend two quarters integrating.
1. Private chat UI for every team
LibreChat or Open WebUI deployed in your tenant — branded, SSO’d, and connected to whichever LLMs you want (cloud APIs, self-hosted, or both). Slack and Teams bots ship alongside, so adoption isn’t gated on opening yet another tab. Each team can spin up custom assistants with system prompts, tools, and document access scoped to what they actually need — and the chat history stays in your database, not the vendor’s.
2. Enterprise search across 40+ workplace apps
Onyx (formerly Danswer) indexed and connected to Slack, Drive, Confluence, Notion, Jira, GitHub, Salesforce, SharePoint, Linear, Zendesk, and 30+ other apps — with permission-aware retrieval so users only see results from documents they already have access to in the source app. Custom connectors for industry systems (iManage, NetDocuments, Epic, Workday, ServiceNow) are part of every engagement that needs them.
3. Self-hosted LLM serving with a BYO-LLM gateway
vLLM, SGLang, or Ollama serving Llama, Mistral, Qwen, DeepSeek, or any open-weight model your security team will sign off on — sized to your traffic, with quantization choices that fit your GPU budget. Route per-team to cloud APIs (OpenAI, Anthropic, Gemini, Bedrock) where your contract makes sense, and to local models where data sensitivity or cost demand it. One gateway, one audit trail, one bill.
4. Private RAG over your corporate documents
Vector store (pgvector or Qdrant) running in your tenant, with embeddings generated by a model you choose — including fully self-hosted embeddings for air-gapped deployments. Chat answers come back with inline citations linked to the source paragraphs in the original document. No documents ever leave your environment to be embedded or indexed by a third party.
5. Custom assistants per team, governed by your IAM
Dedicated assistants for sales, support, legal, engineering, finance, and ops — each scoped to the document sources, system prompts, model choice, and tools that team actually needs. The legal assistant has access to the matter-management system the engineering assistant doesn’t; the sales assistant gets Salesforce and call recordings, not the secret-keeper system. Group membership in Okta or Azure AD drives which assistants a user sees.
6. Native Slack and Microsoft Teams integration
Use the assistant directly from Slack DMs, channel mentions, or Microsoft Teams threads. The same permission-aware retrieval applies — a question in a channel only surfaces results the asker has access to in the source apps. Adoption is dramatically higher than “yet another tab” deployments because the AI shows up where work already happens.
7. Air-gapped, on-prem, and sovereign-cloud deployment
The full stack — chat UI, search, embeddings, vector store, LLM serving — deploys in one Kubernetes namespace or Docker Compose stack. Run in your VPC, on bare-metal in your data center, or fully air-gapped for classified environments. No outbound internet required at runtime once models and connectors are configured, and an internal artifact mirror handles upgrades.
8. Enterprise SSO, audit, governance, and observability
SAML SSO and OIDC integrations for Okta, Azure AD, JumpCloud, and Google Workspace. Role-based admin controls and policy-driven model routing. Full audit trails of who asked what, what they were shown, and which model answered. Token usage, latency, and cost dashboards per team and per assistant — the operational layer your CISO, FinOps lead, and head of AI all need.
Talk to a self-hosted AI deployment expert
Bring us your seat count, your connector list, your model preferences, and your data-residency profile. We’ll come prepared with the right self-hosted AI deployment shape — cloud, on-prem, or air-gapped — and a directional read on what you can stand up in your tenant next sprint.
Ask us about
- Full-stack self-hosted AI deployment — chat, search, and model serving
- Migrating from ChatGPT Enterprise + Glean without losing institutional knowledge
- Permission-aware retrieval against Slack, Drive, Confluence, iManage
- Bring-your-own-LLM routing across OpenAI, Anthropic, and self-hosted models
- Air-gapped and on-prem deployment for regulated industries
- Custom assistants per team, with audit logs and RBAC
When you need a self-hosted AI deployment, not SaaS
ChatGPT Enterprise, Glean, and other multi-tenant AI SaaS cover the median knowledge worker well — generic chat across a few apps, off-the-shelf ranking, 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 SaaS structurally can’t deliver:
- Chat, search, embeddings, and model weights inside your tenant — not in a vendor’s multi-tenant cloud
- Permission-aware retrieval against every source — not a one-size share-everything default
- Custom connectors for industry-specific systems — iManage, NetDocuments, Epic, Workday, ServiceNow
- Audit logs your CISO and regulator can audit — not a vendor SOC report
- Bring-your-own-LLM with self-hosted Llama / Mistral / Qwen on the same chat surface as OpenAI / Anthropic / Gemini
- Flat licensing economics — not per-seat charges that compound as headcount grows
A self-hosted AI deployment is the open-source path. Deploy it once on your infrastructure, configure it for your stack, and your AI becomes a capability you own — not a vendor subscription that scales linearly with seat count.
Frequently asked questions
Related solutions in the private-AI cluster
Additional resources
AI Transformation Workshop
Half-day strategy workshop to map your connector landscape, LLM routing strategy, and self-hosted AI deployment shape. Book a workshop →
AI Strategy Session
60-minute scoping call. We’ll talk through your current chat + search stack, seat economics, and data-residency profile, then sketch the right self-hosted AI deployment. Book a session →
AI Consultant vs In-House Team
Honest tradeoffs on bringing a self-hosted AI stack in-house versus engaging a partner for build + managed retainer. Read the comparison →
Ready to deploy self-hosted AI?
A 45-minute strategy call. We’ll walk through your seat count, connector landscape, model preferences, and data-residency profile — then come back with a concrete deployment shape, sizing for your traffic, and a realistic rollout sequence.