Start with one question: where does the data go?
Every AI request sends text, documents or records to a model and gets a response back. The real difference between the options is where that model runs, who operates it, and what the operator is allowed to do with what you send. Keep those three things in mind as you read the options below.
Option 1: a hosted AI service on business terms
Your application sends requests to a major AI provider over the internet. On business or API plans, providers commonly state in their terms that they do not train on your data and limit how long they keep it. The exact terms vary by provider, product and plan, so they need to be read, not assumed.
Best for: most projects, especially when you need the most capable models and the data can be shared under those terms.
Option 2: a hosted model inside your own cloud account
Some cloud platforms let you use AI models through a managed service in your own cloud account, under the same agreement, billing and access controls as the rest of your infrastructure. Your data is processed by the cloud provider rather than sent to a separate AI company.
Best for: businesses already on that cloud whose policies are built around their existing cloud agreement.
Option 3: a model you run in your own cloud
An openly licensed model runs on servers in your cloud account that you control. No outside AI provider sees the requests. You also take on the work a provider would otherwise do: choosing and updating the model, sizing servers, monitoring and security.
Best for: sensitive data that should not reach any AI provider, or steady high volume where running your own servers costs less per request.
Option 4: a model on your own hardware
The model runs on machines in your office or data center, sometimes with no internet connection at all. This gives the most control and carries the most responsibility, including buying hardware, keeping it running and replacing it.
Best for: environments where data must stay on premises, or where there is no reliable connection.
What private does not guarantee
Running a model privately changes who can see the data. It does not, on its own, make a system safe or compliant:
- A private model is only as secure as the servers, accounts and access controls around it
- Smaller models that are practical to run privately can be less capable on complex tasks
- Logs, backups and connected systems can still hold sensitive data and need the same care
- Whether a setup meets your legal or contractual obligations is a question for your own advisors
Questions to ask before you choose
- What data will the AI see, and what do our policies and contracts say about sharing it?
- Do the provider’s terms for this exact plan say how our data is used and how long it is kept?
- How capable does the model need to be? Have we tested a smaller model on our real work?
- How many requests will we send each month, and what does each option cost at that volume?
- Who will update, monitor and secure a model we run ourselves?
- Can we change options later without rebuilding the application?
Many good systems mix options: a private model for the steps that touch sensitive data and a hosted service for the steps that need the strongest reasoning.