What is an on-premise LLM?
An on-premise LLM is a large language model that runs on servers inside an organisation's own data centre or private network, instead of being called through a cloud provider's API. Prompts, documents and answers never leave the organisation's network. Banks, healthcare providers and government bodies choose on-premise LLMs when data cannot be sent to a third party.
- Choose by data, not by fashion. If the data may not leave your network, on-premise or hybrid is the only option a security team will approve.
- Cloud is fastest to start; on-premise is most controlled. Hybrid keeps your data and search index local while sending only the question and selected passages to a cloud model.
- Grounding matters more than model size. A smaller open-weight model that answers from your own documents, with citations, is more useful and easier to audit than a larger model answering from memory.
- Cost follows usage. Cloud costs grow with every request; on-premise costs are mostly fixed hardware and operations, so on-premise becomes more economical at sustained, high volume.
What are the options: on-premise, hybrid or cloud?
Every enterprise AI system has the same parts: a language model, a store of your documents (usually a vector database), the application that users talk to, and the data the answers are built from. The deployment choice is simply a decision about where each of those parts runs.
| On-premise | Hybrid | Cloud | |
|---|---|---|---|
| Where the model runs | Your servers | Cloud provider (OpenAI, Anthropic, AWS Bedrock, Azure) | Cloud provider |
| Where documents and the index live | Your servers | Your servers | Cloud |
| What leaves your network | Nothing | The question and the passages needed to answer it, encrypted | Documents, questions and answers |
| Model choice | Open-weight models (for example Llama, Mistral, Qwen) | Any frontier model | Any frontier model |
| Cost profile | Up-front hardware, then mostly fixed | Mixed | Pay per request |
| Time to first deployment | Longest (hardware, setup) | Medium | Shortest |
| Best for | Banking, healthcare, government, air-gapped sites | Regulated firms that want frontier-model quality | Non-sensitive data, fast pilots |
When does an on-premise LLM make sense?
An on-premise deployment is the right choice when one or more of these is true:
- The data is regulated. Customer financial records, patient information and government files often may not be processed by an outside provider. Security and compliance committees regularly stop AI projects at this point. Our article on why agentic AI projects get cancelled covers this failure in detail.
- Data must stay in the country. Data-residency rules can rule out cloud regions that are not local.
- The site is disconnected. Some networks are air-gapped by design and cannot call an external API at all.
- Usage is high and steady. Thousands of employees asking questions every day produce a predictable load that fixed hardware handles economically.
If none of these applies, a cloud or hybrid deployment is usually faster and cheaper to start. Many organisations begin in the cloud with non-sensitive documents and move sensitive workloads on-premise later; a well-built platform supports both without a rebuild.
What do you need to run an LLM on premise?
A production on-premise LLM is more than a model on a server. A working system needs six components:
- GPU servers. Smaller open-weight models (roughly 7 to 14 billion parameters) run on a single data-centre GPU. Larger 70-billion-parameter models need roughly 40 GB or more of GPU memory even when compressed, which usually means several GPUs.
- An open-weight model. Chosen for language support, accuracy on your documents and licence terms.
- A vector database and embeddings. These let the system find the right passage by meaning rather than keyword. In Tek-Insight we use the Qdrant vector database with bge-m3 multilingual embeddings.
- A document pipeline. Ingestion and section-aware chunking of policies, manuals, circulars and database records, kept up to date as documents change.
- Access control and audit logs. Role-based access so each employee only retrieves what they are allowed to see, and a record of every question and answer.
- An evaluation process. A set of real questions with known answers, scored by rules and by an AI judge, so you can prove accuracy before go-live and after every change. Fine-tuning methods such as LoRA can then improve accuracy on domain language.
How much does an on-premise LLM cost compared with the cloud?
The two models are paid for differently. Cloud LLMs charge for every request, by the amount of text sent and received, so the bill grows with usage. On-premise LLMs need an up-front investment in GPU hardware, plus power, hosting and the engineering time to operate it, but each additional question costs almost nothing.
The practical rule: for pilots, irregular usage or small teams, the cloud is cheaper. For large, steady workloads the fixed cost of on-premise hardware is spread across many requests and becomes the economical choice. Hybrid sits in between: you pay per request for the model, but keep the documents, the search index and the application on your own infrastructure.
Is an on-premise model less accurate than a cloud model?
The largest frontier models are only available in the cloud, and they are stronger at open-ended reasoning. For enterprise question answering, however, the deciding factor is usually retrieval: whether the system finds the right passage in your documents. When an open-weight model answers only from retrieved passages and cites its source, the gap to a frontier model narrows sharply, and every answer can be checked in one click. Where open-ended reasoning matters most, a hybrid deployment gives frontier-model quality while the documents stay local.
Field proof: an on-premise knowledge platform at an enterprise bank
An enterprise bank needed staff to find answers in policy documents and circulars spread across disconnected systems, without customer data leaving its network. Teknoloje deployed Tek-Insight, our agentic AI knowledge platform, inside the bank's own infrastructure. The results:
- 80% less time spent searching for information.
- 70% of routine queries resolved without a human.
- 2x faster query and decision turnaround.
- 100% data sovereignty: no banking records left the bank's network.
Read the full banking knowledge platform case study.
A five-question checklist for choosing your deployment
- May this data be processed by a third party under your regulations and contracts? If no, choose on-premise.
- Must the data stay in the country, or is the network air-gapped? If yes, choose on-premise.
- Do you need frontier-model reasoning on sensitive documents? Choose hybrid.
- Is usage high and steady across many staff? On-premise becomes more economical over time.
- Is this a pilot on non-sensitive data? Start in the cloud and keep the option to move.
Frequently asked questions
What is an on-premise LLM?
An on-premise LLM is a large language model that runs on servers inside an organisation's own data centre or private network, so prompts, documents and answers never leave that network. It is the usual choice for banks, healthcare providers and government bodies that cannot send data to a third party.
What is the difference between an on-premise LLM and a cloud LLM?
An on-premise LLM runs on hardware the organisation controls, and no data leaves its network. A cloud LLM is called through a provider's API, so the data is processed on the provider's servers. Cloud is faster to start and charges per request; on-premise needs up-front hardware but keeps full control of the data.
Can an LLM run without internet access?
Yes. An open-weight model, its vector database and the application can all run on servers with no internet connection. This is how air-gapped deployments work in banking, defence and government networks.
What is a hybrid LLM deployment?
In a hybrid deployment the documents, the search index and the application stay on the organisation's own servers, and only the question and the passages needed to answer it are sent, encrypted, to a cloud model. It combines frontier-model quality with local control of the document store.
How long does it take to deploy an on-premise LLM?
Teknoloje's typical go-live for an enterprise AI system is six to ten weeks from signed contract, covering document ingestion, search configuration, accuracy evaluation and rollout. Hardware procurement, if new GPUs are needed, can add to that time.