In the cloud
The model runs on the provider's servers and is used through an API. You pay per use, there is no hardware to buy, and you can use the most capable models, such as those from Anthropic, OpenAI and Google.
Guides · AI
An AI model on your own servers or in the cloud: how do you choose?
by Salvo De Luca, founder and CEO of Dialogicom · published 5 November 2026 · updated 6 November 2026
It depends on four things: how confidential the data is, how capable the model needs to be, how many requests there will be and who will look after it. The cloud gives you the most capable models with no hardware to manage. A model on your own servers keeps the text in-house, but needs hardware and care. Often the right answer is to use both.
The two options
What it means in practice.
On your own servers
An open source model, meaning one whose weights can be downloaded under a licence that allows its use, installed on your server or in a data centre you choose. The text does not go to a model provider.
The weights are the numbers the model learned during training: having them means you can run it wherever you like. "Local" here means on-premise, in your company or on a server you control, not necessarily on the user's own computer.
Confidentiality
Where the text goes.
This is often where the discussion starts. In the cloud, the text reaches the provider, which often has its servers in the United States. Business services have precise terms on how long they keep data and whether they use it for training: read them for the service you choose, before you start.
On your own servers the text stays with you, but security becomes your job: access, updates, backups, control over who gets in. Neither option makes a use of data compliant on its own. We design the technical side; the legal assessment belongs to your adviser.
Cost
Pay per use, or a fixed cost.
In the cloud you pay for what you use: few requests cost little, many requests cost proportionally more. On your own servers the cost is mostly fixed: a server with a suitable graphics card, power, space and the time of whoever looks after it. You pay the same whether you use it a lot or a little.
- Low or highly variable volumes: the cloud is usually the better deal.
- High, steady volumes on tasks a smaller model handles well: your own server may pay off.
- Either way, estimate on your real case, with your own requests, before buying hardware.
Quality
How capable the model needs to be.
Today the largest cloud models are the most capable at hard tasks: long reasoning, polished writing, many languages. The open source models that run on a reasonably sized server are smaller. For precise, repetitive tasks, such as classifying a request, extracting fields or summarising a short text, they are often enough.
The way to find out is not to read the leaderboards. It is to try the candidate models on the same real cases, with the same instructions, and compare the results with people who know the work.
Maintenance
Who keeps it running.
In the cloud the provider looks after the model, but models are replaced and retired: the software has to be able to move to a new version, and each move calls for new quality tests. It is also wise to plan a fallback: if a provider does not answer, the software should be able to switch to another model or hand the work to a person.
On your own servers the model stays the same until you change it, which is an advantage when you need stability. In exchange it is up to you, or whoever supports you, to update the system, drivers and software, and to make sure the service stays up.
The middle way
The hybrid setup.
Often you do not choose a single option: tasks are split by data. Tasks that touch confidential data go to the model on your servers. The others go to the cloud, where the model is more capable and you only pay when you use it.
For this to work, requests to the models have to go through one place that knows which task goes where. A task then moves from one option to the other by changing the configuration, not the program. You can also start in the cloud with test data, measure the quality, and then check whether a model on your own servers is enough.
Our experience
Both options, in our projects.
We have delivered projects with open source models installed on customers' servers, when the data was not to leave the company. And we work every day with the cloud models of Anthropic, OpenAI and Google in our own applications.
Limits
What to keep in mind.
- Open source model licences are not all the same: some restrict certain uses, and must be read before choosing.
- A model on your own servers is not automatically safer: it is if the server is well run.
- Cloud models and prices change often: a choice made today needs reviewing over time.
- Hardware for models should be sized on the real load, not the imagined one.
Frequently asked questions
The questions we get about this choice.
The model usually is; using it is not. You need hardware, power and the work of whoever installs and maintains it. It is worth doing the sums on your own volume of requests.
For the smaller models a good server can be enough. The larger ones need one or more graphics cards with plenty of memory. Sizing is done after choosing the model on your cases.
Yes, and it is often the most careful way: you try the task with test or non-confidential data, measure the quality, then check whether a model on your servers gives good enough results. If the software is well designed, the move does not rewrite it.
Whichever gives the best results on your cases, with a licence suited to your use and a size that fits the hardware. Models change often: that is why the choice comes after a trial, not before.
Not sure which option is right for you?
Tell us what data you have and what you want to do with it: we will suggest the most suitable option, with its costs and its limits. We reply within 24 working hours.