When a company asks us, “Should we use AI?” the conversation almost always ends up at the same crossroads: do we install something on our own servers, or do we use an online service like ChatGPT or Claude?
The honest answer is: it depends. But it doesn't "depend" in the generic sense—it depends on three very specific things: what data do you put in there?, how many times do you use it, and what do you have to do?Everything else is just a side dish.
In this article, we try to provide a practical guide. No review of all available models, no abstract benchmarks. Just the criteria that matter when you're a company with a limited budget and a need for results.
LLM on-site (with tools like Ollama) means downloading an AI model and running it on your computer or server. The data never leaves your network. You have complete control, but you need adequate hardware and someone to manage it.
LLM in the cloud (OpenAI, Anthropic, Google) means using an external service over the internet. You send a request, and receive a response. You don't have to install anything, but your data passes through the provider's servers, and you pay as you go.
Neither option is better than the other. They're different tools for different situations.
We've summarized the comparison into the criteria that, in our experience with Italian companies, really make a difference in the choice.
| Criterion | Local LLM (Ollama) | LLM Cloud (OpenAI, Anthropic) |
|---|---|---|
| Data privacy | Your data stays on your network. Nothing leaves. Ideal for confidential documents, legal data, and intellectual property. | The data transits through external servers. Providers offer contractual guarantees, but the data leaves your perimeter. |
| Quality of responses | Good and rapidly improving, but still a step below the flagship cloud models for complex or creative tasks. | Generally superior, especially in complex reasoning, writing, and analysis. The best models are still here. |
| Costs in the first month | Higher: requires hardware and configuration. Approximate cost: €1.000 to €5.000 for a basic configuration. | Lower prices: you only pay for what you use. For initial testing volumes, it's a few dozen euros per month. |
| Costs after 12 months | It becomes cost-effective if used intensively and consistently. The hardware pays for itself, with almost zero marginal cost. | It grows with use. If AI becomes part of everyday processes, the bill rises. At high volumes, it can exceed the cost of the space. |
| Boot speed | Slower. It takes days or weeks to configure, test, and integrate. | Faster. You can have a working prototype in an afternoon. |
| Necessary skills | You need in-house technical expertise or a partner to manage the stack. It's not a "set and forget" solution. | Minimal to get started. Well-documented APIs, and many tools offer no-code interfaces. |
| Business Integrations | Maximum flexibility: You can connect AI to any internal system. But you have to build the connections yourself. | Pre-built integrations with many tools (CRM, email, ticketing). Less flexibility, more speed. |
| Supplier dependency | None. You can change templates at any time. The code and data are yours. | Present. Changing providers requires adaptation. You're tied to their prices, models, and policies. |
| Proven Reliability | It depends on your infrastructure. If the server goes down, the AI stops. | High. Large providers guarantee high uptime and manage everything themselves. |
Instead of thinking abstractly, let's look at three real-world situations we often encounter.
A chatbot on the website that answers questions about products, opening hours, and return policies. The data involved is public: catalog, FAQs, and general information.
The right choice: cloud. There's no sensitive data involved, the initial volume is low, and you need a model that writes well in Italian and is convincing to customers. A cloud service can be activated in a few days, costs little to start with, and the quality of the responses is immediately good. Complicating things with a local installation for this use case makes no sense.
An internal assistant who reads legal documents, contracts, and confidential business correspondence. The data is sensitive and subject to confidentiality.
The right choice: local. Here, data can't leave the company—period. Even though cloud providers offer guarantees, the reputational and legal risk of having confidential contracts stored on external servers isn't justifiable for most companies. An on-premises model with Ollama, connected to your document archives, provides you with contextualized answers without exposing anything to the outside world. It requires an initial investment, but the peace of mind is worth the cost.
Automations that connect management, email, documents, and report generation. The data is a mix: some sensitive (invoices, customer data), some non-sensitive (templates, report structures).
The right choice: hybrid. Use the cloud for non-sensitive parts—generating text, summarizing generic emails, and drafting reports. Use on-premises for anything involving financial data, customer records, or confidential information. This separation requires a little more architecture, but it's the best approach for balancing security, cost, and quality.
Start with the problem, not the technology. Don't ask yourself, "Which LLM should I use?" Ask yourself, "Which process wastes the most time or money?" AI is a tool: you first need to know what you're building with it.
Take a 4-6 week test. Pick a use case, test it, and measure the results. You don't need a €50.000 project—you need a controlled experiment that tells you if AI makes a difference in that specific process.
Don't decide alone. The choice between on-premises and cloud involves IT, management, and often even legal counsel. It's a business decision, not just a technical one.
Review your choice every 6 months. The AI world is moving fast. What makes sense in the cloud today might be worthwhile on-premises in six months—and vice versa. Build so you can pivot without starting from scratch.
We help Italian companies understand where AI can make a difference and implement it the right way—on-premises, in the cloud, or both. If you want to understand which approach makes sense for your business, Book a free consultation with us.
