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AI in Your Product
Private LLM deployment: AI features that keep your data on infrastructure you control
When data must not go to a commercial AI API, we run open-source language models on your own servers or private cloud — or set up commercial models inside your own cloud account — and connect them to your product, with honest advice on what each option can and can't do.
What is a private or self-hosted LLM?
A self-hosted LLM is a language model that runs on servers you control — your own hardware or a private cloud account — so prompts and data never pass through a third-party AI service. It's usually an open-weight model, such as models from the Llama, Mistral, Qwen or Gemma families, served behind your own API. A middle option is a commercial model deployed in your own cloud account, where the provider contractually doesn't keep or train on your data.
Do you need a private LLM?
- "Our contracts or regulators don't allow customer data to go to an external AI API."
- "Clients ask where their data is processed, and 'a US AI company' isn't an acceptable answer."
- "We process medical, legal or financial documents."
- "Per-request API costs are getting large at our volume."
- "We need AI to work in a network with no internet access."
What do we build?
- Model selection and testing on your real tasks, comparing open models against a commercial baseline so you see the trade-off in quality.
- Hosting on your servers, a private cloud or a GPU provider in the region you need, sized for your volume.
- A serving layer with an API your applications call, request limits, logging and authentication.
- Integration with your product, n8n workflows or a RAG system over your documents.
- Monitoring of speed, errors and usage, with alerts.
- Documentation of where data flows and what is stored, for your security and compliance reviews.
How does a private LLM project work?
- Requirements. We list what data is involved, which tasks the model must do and how many requests to expect.
- Test. Candidate models are tested on a set of your real examples, side by side.
- Host. The chosen model is deployed where your requirements allow, sized for your volume.
- Integrate. Your product or workflows call the model through a private API.
- Monitor. Speed, errors and usage are tracked, and the model can be swapped later without changing your product.
Which option is right for you?
| Option | Where data goes | Quality | Cost pattern |
|---|---|---|---|
| Commercial API (standard) | The AI provider | Highest | Pay per request |
| Commercial model in your cloud account | Your cloud provider, under your agreement | Highest | Pay per request |
| Open model on a private GPU cloud | Servers you rent and control | Good for most focused tasks | Server cost, regardless of volume |
| Open model on your own hardware | Never leaves your network | Good for most focused tasks | Hardware up front, then running costs |
Many teams need the second option, not the fourth. We'll tell you which one your requirements actually call for.
What are the trade-offs of self-hosting?
Open models have improved quickly and handle focused tasks — classification, extraction, summaries, answering from retrieved documents — well. The largest commercial models are still ahead on complex reasoning and long, open-ended work, so we test on your actual tasks before you commit.
Self-hosting also means owning the infrastructure: GPUs cost money whether they're busy or not, and someone has to keep the model server updated and running. At low volume, a commercial model in your own cloud account is often cheaper; at steady, high volume, self-hosting can be cheaper per request.
What do we build it with?
We pick tools to fit your hardware, scale and requirements. For example:
Model licences differ, so we check that the licence of the chosen model allows your use before deployment.
How long does it take and what does it cost?
A first private model deployment connected to one use case typically takes 3–6 weeks. Setup starts at $3,000, and monthly care starts at $300 per month. Servers, GPUs or cloud usage are paid directly to your hosting provider; we estimate them with you before you choose an option.
Monthly care covers updates, monitoring, testing new model versions and keeping the integration working. Payments are milestone-based, and the launch includes one month of free bug fixing.
When is a private LLM not a fit?
Good fit when…
- Data can't go to an external AI service
- You need to say exactly where data is processed
- Tasks are focused and repeat at high volume
- Someone can own the infrastructure, or you want us to
Not a fit when…
- A commercial API with a data agreement meets your rules
- Volume is low and unpredictable
- You need the very best reasoning on open-ended tasks
- Nobody will maintain servers and you don't want care
Frequently asked questions
What is a self-hosted LLM?
A self-hosted LLM is a language model that runs on servers you control, such as your own hardware or a private cloud account, so prompts and data are not sent to a third-party AI service. It is usually an open-weight model served behind your own API.
Are open-source models as good as ChatGPT or Claude?
For focused tasks such as extraction, classification, summaries and answering from retrieved documents, they are often good enough. The largest commercial models are still ahead on complex reasoning, so we test candidate models on your real tasks before you decide.
Is self-hosting an LLM cheaper than using an API?
It depends on volume. GPUs cost money even when idle, so at low or uneven volume an API is usually cheaper. At steady, high volume, self-hosting can cost less per request. We compare both at your expected volume.
Can we use commercial models without sending data to the AI company?
Often yes. Services such as Azure OpenAI, AWS Bedrock and Google Vertex AI run models inside your own cloud account and region under your agreement with that cloud provider, which meets many companies' data requirements without self-hosting.
How much does a private LLM deployment cost?
At Webziper, a private model deployment starts at $3,000 to set up, with monthly care from $300 per month. Server, GPU or cloud costs are paid directly to the hosting provider and depend on the option and volume.
Need AI without sending data out?
Tell us what data is involved and what your rules require. We'll recommend the simplest option that meets them.
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