Self-hosted LLM
Also: Private LLM, On-premise LLM
What is Self-hosted LLM?
A self-hosted LLM is a language model you run on infrastructure you control, such as your own GPUs or your cloud account, so prompts and outputs never pass through a third-party API.
What Self-hosted LLM means
A self-hosted LLM is a large language model deployed on hardware you own or rent directly: an on-premise GPU server, a dedicated node in your AWS, Azure or GCP account, or a colocation rack. The model weights sit on your disk, an inference server such as vLLM or TGI serves them over an internal endpoint, and your applications call that endpoint exactly as they would call a hosted provider. The difference is that nobody outside your network sees the traffic.
It is not the same as a "private endpoint" from a hosted provider. Those still send data to the provider's infrastructure under a contract. Self-hosting means the model runs inside your perimeter, which is why banks and hospitals ask for it. It also means you inherit the work: GPU capacity planning, model upgrades, patching and monitoring.
The practical trade is control and predictable cost versus operational load. Below a certain volume a hosted API is cheaper. Above it, or where regulation forbids egress, self-hosting wins.
Who it really matters to
- CTO / Head of Engineering: decides whether the team can carry inference infrastructure, or whether a managed private deployment inside the client's cloud is the safer middle ground.
- CISO: removes an entire class of third-party data-sharing risk, but adds a GPU host that must be hardened and patched like any other server.
- CFO: converts a variable per-token bill into a fixed monthly infrastructure line, which is only a win once volume is high enough to fill the hardware.
- Compliance officer: makes it far simpler to answer regulator questions about where customer data is processed and who can access it.
Why it exists
Self-hosted LLMs exist because some organisations cannot, by policy or regulation, send customer records to an external model provider. RBI-regulated lenders, hospitals holding patient notes and companies bound by client contracts all hit this wall. Running the model yourself removes the egress question entirely. The trade-off is real: you give up the newest frontier models the day they launch, you need people who can size and operate GPUs, and a lightly used cluster is expensive per request. The decision should be driven by a benchmark on your own tasks, not by a preference for one deployment style.
Where it is applied
- An NBFC running document extraction over KYC files without any customer identity data leaving its data centre.
- A hospital network summarising discharge notes and clinical protocols on a GPU inside its own network.
- A SaaS company serving enterprise tenants who contractually require that their data is never processed by a third-party AI vendor.
- A logistics operator parsing shipment documents and exception emails on a single L4 node in its VPC.
- A university processing student records for admissions triage under its data-protection commitments to minors.
Is Self-hosted LLM a skill?
Technique / practiceA deployment pattern rather than a product: choosing, serving and operating a model on your own infrastructure. Eazyware delivers it under Private Agentic AI, with the client owning the weights, the serving stack and the runbooks.
Eazyware service that covers it: Agentic AI Solutions (self-hosted). Starting prices are on the pricing page.
Frequently asked questions
Is a self-hosted LLM as good as GPT-class APIs?
On narrow, well-defined tasks such as extraction, classification and grounded question answering, a well-chosen open-weight model often matches hosted APIs once evaluated on your own data. On broad open-ended reasoning the frontier hosted models still usually lead. Benchmark before deciding.
What does self-hosting cost compared with an API?
It depends almost entirely on utilisation. A dedicated GPU costs the same whether it serves one request or a million, so self-hosting is cheaper only when your volume keeps the hardware busy. Low-volume workloads are almost always cheaper on a hosted API.