Self-Hosted LLM Infrastructure
Last verified: October 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.
When self-hosting actually pencils out
We start most of these conversations by trying to talk teams out of it, because the common reason for self-hosting is a cost intuition that does not survive arithmetic. Hosted APIs bill per token and scale to zero; your own inference stack bills for accelerators whether or not traffic arrives. Below a certain sustained utilisation the hosted option is cheaper, and the crossover is a real number you can compute from your own traffic profile before committing to anything. We would rather do that calculation with you for free than build something you regret.
The reasons that do hold up are usually not about price. Data residency and regulatory constraints that make sending content to a third party impossible. A workload that is steady and high enough to keep accelerators genuinely busy. Latency requirements that rule out a network hop. A need to run a specific fine-tuned or open-weight model that no provider hosts. Where one of those applies, the economics follow rather than lead, and the engagement is about building it properly.
The build itself is more infrastructure than machine learning: model and quantisation choice traded against the quality you can afford to lose, a serving layer configured for your actual batch and concurrency profile rather than a benchmark's, capacity planning that accounts for how badly utilisation varies through a day, and the same observability and eval layer any production AI system needs. The ongoing cost people underestimate is not the hardware, it is that someone now owns an inference platform — upgrades, incidents, capacity — and that ownership should be budgeted before the first GPU is ordered.
When this is not the right engagement
We start most of these conversations by trying to talk teams out of it. It is a poor fit when:
- The motivation is purely cost and your utilisation is low or spiky. The arithmetic usually favours the API, and we will show you why.
- There is no platform team to operate it after handover. Self-hosted inference is a system someone has to run, not a project that finishes.
- You need frontier-model capability. Open-weight models are strong and closing the gap, but on the hardest tasks the difference is still real.
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."
Is self-hosting actually cheaper?
Below a certain sustained utilisation, no. A hosted API bills per token and scales to zero; your own stack bills for accelerators whether or not requests arrive. The crossover is a real number computable from your own traffic profile, and we will do that calculation with you before anyone commits to hardware. The reasons that do hold up are usually residency, a steady high workload, a latency floor, or a specific model no provider hosts.
What does it cost to run once it is built?
More than the hardware line suggests, and this is the part most business cases miss. Someone now owns an inference platform: upgrades, incidents, capacity planning, and the quality regression testing that comes with changing a model version. Budget that ownership before the first accelerator is ordered. Where no platform team exists to inherit it, we would rather tell you the answer is the API than build something that decays after handover.
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.