Self-Hosted LLM Infrastructure
Last verified: June 2026· engagement
For data-sensitive teams (healthcare, finance, defense, EU): local inference, RAG pipelines, and fine-tuning workflows on infrastructure you control.
- Outcome
- A working private AI stack on your cloud or on-prem
- Timeline
- 8–16 weeks
- Pricing
- $75–250k build, $10–20k/mo ops
- Buyer
- CTO, Head of Platform (regulated)
The problem
Your data can’t leave your environment — healthcare, finance, defense, or EU residency rules — so the hosted-API path is closed. But standing up private inference, RAG, and fine-tuning correctly is its own specialty.
What we do
- Stand up local or VPC inference sized to your latency and throughput needs.
- Build RAG pipelines over your private data with the right retrieval layer.
- Set up fine-tuning workflows so you can adapt open-weight models safely.
- Wire in observability and cost controls from day one.
How it fits together
What you get
Built on our open source
fast-litellm — Rust acceleration for LiteLLM — faster connection pooling, rate limiting, and memory-intensive workloads.
Common questions about this engagement
Do you work under NDA?
Yes — we sign your mutual NDA before any data or repo access. For audits we prefer read-only access to start; for builds we work in a clean repo under your ownership.
Will we own what you ship?
Always. You own the code, the runbooks, the dashboards. We are explicitly set up to hand off and transition out, not to create dependency.
Vendor-agnostic — what does that mean in practice?
We integrate with what you already run — OpenAI, Anthropic, open-weight models on your cloud, your CI/CD, your observability stack. If a hosted vendor solves it, we will not reinvent it; if a self-hosted tool is the right answer, we will not pretend the hosted one is.
How is this different from a Big Four consulting deck?
We are the engineers doing the work, not analysts handing recommendations to a different team. The deliverable is working software in your repo, not a slide deck.
Can you work with our in-house AI team instead of replacing them?
Yes — most of our engagements pair with an internal owner and ramp them up to run the system after we leave. Many of our best engagements start with "we hired an AI team, help us get them productive."
Guides for this work
Self-hosted LLMs: when it pays off, and how
Self-hosting an LLM means running open-weight models on GPUs you own or rent instead of calling a hosted API — it wins on high, steady volume or hard compliance, and loses almost everywhere else.
LLM costHow to reduce LLM API costs
Most production LLM spend is avoidable: it comes from sending the wrong model too many tokens, too many times. Cutting it is an engineering problem — routing, caching, batching, and context control — not a matter of waiting for prices to drop.
Let’s scope it on a call
Thirty minutes with an engineer. We’ll tell you straight whether this is the right first move for your team.