Self-Hosted AI, Deployed on Your Infrastructure
We deploy open-source AI for businesses that can't put their data in someone else's cloud — Glean alternatives, private GPT, RAG over your documents, all running in your tenant. No data leaks. No per-seat lock-in. No vendor surprises.
What You Get from a Private-AI Deployment
Six outcomes regulated companies — and any team uneasy about feeding client data to OpenAI — get from a private deployment.
Data Sovereignty
Your documents, conversations, embeddings, and audit logs all live in your tenant. The LLM runs on your infrastructure, so no third-party AI provider sees a token of it.
Open-Source Stack
Onyx, LibreChat, Open WebUI, vLLM, Ollama. Battle-tested, MIT-licensed software with no vendor that can change pricing on you or shut you down.
Permission-Aware Retrieval
Connector-based RAG that respects RBAC, matter walls, and file-level permissions. Employees only see what they have access to in source apps.
Cost Predictability
One-time deployment plus a flat managed retainer. No per-seat surprises as headcount grows. At scale, 5–10× cheaper than ChatGPT Enterprise or Glean.
Compliance-Ready
HIPAA, GDPR, SOC 2, attorney-client privilege, and government data-residency rules are all addressable when the LLM and data live in your tenant.
Bring Your Own LLM
Run Llama, Mistral, Qwen via vLLM or Ollama, or route to OpenAI, Anthropic, Gemini through an LLM gateway. Switch models without rebuilding the stack.
Why Off-the-Shelf SaaS Misses on Private AI
ChatGPT Enterprise, Glean, and other multi-tenant LLM SaaS were designed for the median knowledge worker at a typical mid-market company. Once your data sensitivity, seat count, or industry constraints diverge from that median, three things break:
Private AI lives inside your tenant.
Your data, your embeddings, your audit logs, and any model fine-tunes — all under controls your security team already owns. The system gets smarter from your usage, on infrastructure you already trust, with no third-party LLM provider in the data path.
The Private-AI Solutions We Deploy
Eight ways we deploy private AI in your tenant. Pick the one that matches the job you're trying to do — or talk to us and we'll help you sequence the right combination. The first five are live engagements you can scope today; the rest are part of how we ship the full stack.
Self-Hosted Enterprise Search
Onyx (formerly Danswer) deployed in your tenant, indexed against 40+ workplace apps with permission-aware retrieval. The open-source Glean alternative — same chat-with-citations UX, your data never leaves your environment.
Private ChatGPT for Business
Self-hosted LibreChat or Open WebUI connected to your Slack, Drive, and corporate documents. BYO-LLM across OpenAI, Anthropic, or self-hosted Llama / Mistral. Replaces the ChatGPT Team subscriptions your employees are already paying for out of pocket.
Self-Hosted AI for Business — Full Stack
The full private-AI stack — LibreChat + Onyx + vLLM — deployed end-to-end as one engagement. Chat, search, BYO-LLM serving, SSO, audit, and observability wired together on day one. One stack, one bill, one rollout sequence.
Air-Gapped AI for Regulated Industries
Fully disconnected AI for classified, IL5, sovereign-cloud, and SCIF environments. Open-weight models on your GPUs, zero outbound at runtime, FedRAMP / IL5 / GovCloud aligned controls and audit logs.
Private RAG / Chat With Documents
Single-corpus document chat that stays inside your tenant. Tuned ingestion, hybrid retrieval, and BYO-embedding + BYO-LLM. Ideal for legal matter files, M&A data rooms, regulatory libraries, and research corpora.
LLM Inference & Serving Infrastructure
Production model serving on your GPUs — vLLM, SGLang, or TensorRT-LLM — with horizontal scaling, observability, and per-team routing. The serving half of the stack, sized and tuned for your traffic and your GPU budget.
vLLM Development & Deployment
High-throughput vLLM deployment with paged attention, continuous batching, and Kubernetes-native scaling. Tuned for your specific model and traffic pattern, with FP8 / FP16 / INT8 quantization sized to your GPU footprint.
Ollama Deployment & Integration
Local-LLM deployment for workstations, single GPUs, or small production instances. The fastest path to an end-to-end private-AI pilot before scaling to vLLM serving in production.
Talk to a Private AI Expert
Bring us your industry, your data-sensitivity profile, and your seat economics. We'll come prepared with the right stack — and a directional read on what you can stand up in your tenant next sprint.
Book a Strategy Session →When You Need Private AI You Own, Not SaaS
Multi-tenant LLM SaaS covers the median knowledge worker well. But teams with real data-sensitivity, scale, or industry constraints need things SaaS structurally can't deliver:
Private AI is the open-source path. Deploy it once on your infrastructure, configure it for your stack, and your private AI is a capability you own — one that gets smarter from your usage, on infrastructure you already trust, with no third-party LLM provider in the data path.
Frequently Asked Questions
At small scale (under ~50 seats), hosted SaaS is often cheaper. Past ~100 seats the math tips fast. A 100-person company on Glean at ~$60/seat/month pays $72,000/year just in software. A standard private-AI deployment plus a year of managed service runs roughly the same — but you own the stack from year two on. ChatGPT Enterprise at 500 seats is $360K–$600K/year; the deployment plus a year of managed beats one year of those licences.
Yes. Onyx alone ships 40+ connectors out of the box (Slack, Google Drive, Confluence, GitHub, Jira, Salesforce, Notion, SharePoint, and more). For legal-specific systems like iManage and NetDocuments we add custom connectors as part of the deployment. Connectors sync incrementally and respect each source's ACLs — users only see what they'd see in the source app.
Because the LLM and the data both live in your tenant, your existing certifications and BAAs carry over — there's no new third-party data processor to evaluate. We add audit logging, SSO/SAML, RBAC, and encryption-at-rest as part of every deployment, and on enterprise engagements we deliver a SOC-2-readiness pack and the documentation for HIPAA, GDPR, and regulator review.
Either. The chat and search layers (LibreChat, Onyx) are model-agnostic. You can start with OpenAI or Anthropic via your enterprise contract and add self-hosted models on vLLM/Ollama later — or go fully self-hosted from day one. We'll recommend the option that matches your data-sensitivity profile and the unit economics of your usage.
Yes — that's usually the right path. A common sequence: start with Open WebUI + Ollama (or LibreChat + a hosted LLM) for the first business unit. Add Onyx for cross-app search once that's working. Move to self-hosted vLLM serving once usage justifies the GPU spend. Each step compounds on the last; nothing has to be rebuilt.
Every engagement includes post-launch support as standard. After that, an optional managed retainer covers monitoring, version upgrades, connector additions, and quarterly reviews. Or you can take ownership in-house — we hand off a complete runbook either way.
Related Solutions in the Private-AI Cluster
Self-Hosted Enterprise Search — On-Prem Onyx Deployment for Regulated Teams
Learn more →Air-Gapped AI for Regulated Industries — Disconnected LLM Deployment
Learn more →Private ChatGPT for Business — Self-Hosted Chat for Regulated Teams
Learn more →Private RAG — Chat With Your Documents Inside Your Tenant
Learn more →Self-Hosted AI for Business — End-to-End Private AI Stack Deployment
Learn more →Private AI for Law Firms — Self-Hosted Legal AI Software Inside Your Firm's Tenant
Learn more →AI Transformation Workshop
Half-day strategy workshop to identify the highest-ROI private-AI moves for your team.
Book a workshop →AI Strategy Session
A 30-minute scoping call. Directional read on the right private-AI stack for your environment, plus a sample architecture.
Book a session →AI Consultant vs In-House Team
Honest tradeoffs comparison for build-vs-buy decisions on private AI for your organization.
Read the comparison →Ready to Deploy Private AI?
A 30-minute strategy call. We come prepared with the right stack for your industry, your data sensitivity, and your seat economics — and a directional read on what you can stand up in your tenant next sprint.