On-premise AI vs cloud AI: what actually changes for your data

What cloud AI is

A cloud AI service is run by a provider on its own infrastructure. You reach it over the internet, usually through a website, an app or an integration. When you ask a question, the text of that question, and any documents or records you supply with it, travel to the provider’s servers, where the model works out a response and sends it back.

The attraction is simple. There is no hardware to buy, nothing to install, and you can start in minutes. The provider looks after capacity, updates and availability. The trade-off is that the provider’s infrastructure becomes part of how your company information is handled, and you rely on its terms and arrangements to know what happens to that information.

What on-premise AI is

On-premise means the system is installed on your own premises, on a machine that sits in your building. In Seesa’s case, the models, the company memory and the agent work all run on that installation. Pentatonic supplies the hardware, installs it and manages it for you, so “on-premise” does not mean your team has to become an AI operations team.

The same words, “company AI”, therefore describe two quite different arrangements. One is a service you rent from a distance. The other is a system that lives with you and is looked after by a supplier.

What changes for your data

Four practical things move when the processing comes into your building.

• Where the processing happens. Questions are answered by models running on your installation rather than in a provider’s data centre.
• Where the company memory lives. Seesa’s security page describes company memory as stored on your installation. The records Seesa builds its answers from are held with you, not in someone else’s cloud account.
• Who sets the access rules. Each person’s access governs what their Seesa can use, and you set the permitted actions and approval rules for connected tools. Sharing an answer does not have to mean sharing the records behind it.
• Who is accountable for the hardware. The machine is physically yours to host, with a suitable location, power and network access. For systems Pentatonic supplies, Pentatonic maintains the software and configuration.

What does not change

On-premise does not mean disconnected. Seesa’s security page is direct about this: source systems, user access and maintenance may use external services. If your company keeps its email in Gmail or Microsoft 365, or its files in Google Drive, Seesa has to connect to those systems to work with them, and those systems are external by nature.

Seesa’s privacy notice adds that, for an on-premise installation, company content is stored on the customer’s encrypted appliance, while identity, access and network traffic can pass through Cloudflare. Maintenance also needs a route in: Pentatonic documents the support connection, permissions and data-access arrangements for each deployment before it goes live.

So the useful question is not “does anything ever leave the building?” but “which connections exist, what do they carry, and who agreed to them?” A good supplier can answer that for your deployment in plain terms.

What you take on, and what you hand over

Moving AI on-premise is a trade. With a cloud service you hand over the running of everything and accept the provider’s arrangements. With on-premise AI you take on some practical responsibilities and gain more say over how things are set up.

On the practical side, your team provides a suitable location, power and network access for the agreed hardware, people who can authorise the system connections you want to use, and someone to help agree permissions and check the first workflows. The installation is sized for the work: the number of people, the amount of history, how many people use it at once and what Seesa will do all affect the hardware that is recommended.

On the supplier side, standard setup covers installation, historical import, supported system connections and permission configuration, and ongoing maintenance for systems Pentatonic supplies.

Questions to ask any supplier

Whichever route you choose, these questions are worth putting in writing before you commit. They apply to cloud and on-premise AI alike.

• Where exactly are our questions processed, and where are our records stored?
• Which external services does the system rely on, and what passes through each?
• How is data encrypted, and how is user identity handled?
• How does the supplier connect for maintenance and updates, and who approves that access?
• How long is information kept, and what is the recovery plan if something fails?

Which is right for you?

Cloud AI suits teams who want to start quickly and are comfortable with a provider’s arrangements for their data. On-premise AI suits companies that want the processing and the memory to sit with them, and that are happy to host a machine in return. Neither is automatically safer; what matters is how each is configured and what has been agreed.

If you want to see how this applies to your company, the security page explains how Seesa handles access, actions and external connections, and the on-premise page sets out hardware, setup and management.