Local AI is artificial intelligence that runs on your company's own servers, or on a dedicated server you control, instead of in a provider's cloud. Your data never leaves your infrastructure, the cost doesn't depend on usage, and nobody can swap the model out from under you. In exchange, you have to provide the hardware, choose the model carefully and maintain it. It makes sense when AI will work with sensitive information or high volumes. For occasional tasks, or when you need the most powerful model on the market, the cloud is still simpler.
Until recently, using AI in a company meant sending data to an outside service. Today there are open models you can download and install on your own server that handle much of the daily work: reading documents, sorting emails, extracting data from invoices, answering questions about internal manuals. This guide explains what local AI is, when it makes sense and when it doesn't, what it takes to get it running, and how to approach it without turning it into an endless project.
What is local AI?
In practice, a language model is a large file (the model's "weights") plus a program that runs it. When you use a cloud service, that file lives on the provider's servers, and you send it your questions and your data. With an open model, you download the file and run it on a machine you own.
An open model is one whose creator has published the weights under a license that allows their use. There are several model families in different sizes, from companies such as Meta, Mistral, Google, Alibaba and OpenAI, which released two open models under the Apache 2.0 license in August 2025. Licenses differ: some restrict commercial use, so read them before choosing.
Two more pieces are needed around the model: a program that serves it to other systems over the network (tools such as Ollama, llama.cpp or vLLM), and the integrations that connect it to your ERP, CRM, document storage or email. That last part is what turns an installed model into something your team uses every day.
Why companies choose local AI
- Data stays in-house. Contracts, medical records, payroll, customer data: nothing goes to a third party. That simplifies GDPR compliance in Spain and Argentina's Personal Data Protection Law (Law 25,326), and avoids debates over international data transfers.
- Predictable cost. In the cloud, you pay per use: the more you use, the more you pay. With local AI, the main cost is the server, and processing a thousand documents costs about the same as processing a hundred thousand.
- Vendor independence. The model that works today keeps working tomorrow. Nobody retires it, changes how it behaves or raises its price.
- Works offline. For a plant, a warehouse or a branch with a poor connection, having AI on the internal network is a real advantage.
- Can be customized. Your own model can be specialized with your company's vocabulary and documents, which is more limited in the cloud.
When it doesn't make sense
Local AI isn't for every case, and selling it as if it were is the fastest way to end up with an expensive server nobody uses.
- Occasional use. If AI will be used only a few times a day, paying per use in the cloud is cheaper than buying and maintaining a server.
- Tasks that need the best available model. Long analyses, multi-step reasoning or complex programming: the most powerful cloud models are still ahead of what runs on a reasonable server.
- No one to maintain it. A local model needs updates, monitoring and backups, like any system. Without someone to do that, the savings disappear.
What companies use it for
The use cases that work best have something in common: they're focused, repetitive tasks involving data that shouldn't leave the company.
- Searching internal documents. An assistant that answers questions about manuals, procedures, contracts or a customer's history, citing the document behind each answer. This technique is known as RAG (retrieval-augmented generation).
- Reading and extracting data. Supplier invoices, delivery notes, purchase orders or forms that arrive as PDFs and that someone types into the system by hand today.
- Sorting and routing. Emails, complaints or tickets that organize themselves by topic and urgency and reach the right person.
- Summarizing and drafting. Meeting minutes, case summaries, first drafts of replies that a person reviews.
- Transcription. Calls or meetings turned into text without the audio leaving the company.
What it takes to get started
The server
GPU memory is what matters most, because the model has to fit entirely in it to respond quickly. For reference: OpenAI's 20-billion-parameter open model runs within 16 GB of memory, and the 120-billion-parameter one needs a single 80 GB GPU, a data center card. Between those two extremes, many mid-sized models run well on a workstation with one or two professional GPUs.
The server can sit in your office or a data center, or be a rented dedicated server. What matters is that it's yours or under your control, and that it's properly managed: backups, security updates, monitoring.
The model
You don't need the biggest one. To extract data from an invoice or sort an email, a well-chosen small model responds faster and gets the job done. The right way to decide is to test two or three candidates on real company documents and measure how often they get it right.
The integrations
A model alone does nothing. It has to connect to the systems where the data lives and where the results go: the invoice it reads lands in the ERP, the sorted email reaches the right inbox, the assistant only sees the documents each user is allowed to see. That's where most of the work is, and it's what determines whether the project is useful.
The maintenance
Open models improve fast. Every so often, it's worth testing a new one against the current one and switching if it performs better. You also need to monitor answer quality, server load and security, as with any critical system.
Local, cloud or both
| Local AI | Cloud AI | |
|---|---|---|
| Where the data is | On your infrastructure | Sent to the provider |
| Cost | Server and maintenance, regardless of usage | Per use: grows with volume |
| Quality | Very good on focused tasks | Highest on complex tasks |
| Setup | Weeks: server, model, integrations | Days: an account and the integrations |
| Control | Full: the model doesn't change unless you change it | The provider decides versions and prices |
In practice, many companies end up with a hybrid setup: anything involving sensitive data or high volumes runs locally, and occasional tasks or those needing the most powerful model go to the cloud, with data anonymized when necessary. Designing it this way from the start avoids rebuilding integrations later.
How to start without an endless project
- Pick one concrete use case with a cost you can measure today: hours of manual data entry, response time, errors.
- Gather 50 to 100 real examples (invoices, emails, questions) for testing.
- Test two or three models on those examples and measure accuracy and speed.
- Size the server to the chosen model and your volume, not the other way around.
- Integrate with one system first, the one where results will be most visible.
- Decide who reviews: at first, a person validates the answers, and the review data tells you how much you can automate.
- Only then add more use cases. With the server and integrations in place, each new use case costs much less than the first.
How we help
At DCisGlobal, we implement local AI as part of the services we already provide, not as a separate product. We size and run the server through managed hosting, connect the model to your systems through custom software development, and keep it current (new models, security, answer quality) through support and maintenance.
If you want to know whether local AI, cloud AI or a hybrid setup makes sense for your company, start with a technology assessment: we review what data you'd use, your volumes and which systems would need to connect.
Further reading
- AI process automation.
- AI for back-office processes: which tasks you can already take off your team's plate.
- AI chatbots for customer service: how far to go.
- AI-assisted software development: what changes in custom software.
