What is the difference between On-Premise and Edge AI?

On-Premise AI and Edge AI both mean that AI runs locally, but they are not the same. With On-Premise AI, processing takes place on local servers within the organisation or at the site itself. With Edge AI, analysis happens as close as possible to the data source, for example directly on a camera, an edge device or a compact AI server.

The main difference is therefore where the computing power is located.

What is On-Premise AI?

With On-Premise AI, the AI software runs on servers within the organisation’s own infrastructure.

This can be:

• in a server room;
• in the organisation’s own data centre;
• on a local GPU server;
• at a central location where multiple cameras come together.

The video streams are sent to that server and analysed there. This can be interesting for organisations that want a high level of control over:

• data;
• infrastructure;
• security;
• integrations;
• storage;
• computing power.

An On-Premise environment can also process multiple cameras and AI applications centrally.

What is Edge AI?

With Edge AI, analysis takes place closer to the camera or sensor. Processing can, for example, run:

• directly on the camera;
• on an edge device next to the camera;
• on a compact local AI server;
• on hardware at the edge of the network.

As a result, not all raw video has to be sent to a central server or cloud environment first. The analysis literally takes place at the “edge” of the network.

What is the main difference?

A simple way to look at the difference is:

On-Premise AI
Local processing within the organisation’s own infrastructure, often centrally organised.

Edge AI
Local processing as close as possible to the camera or data source.

An On-Premise server may, for example, analyse fifty cameras. An Edge AI device may instead be positioned directly next to one or a few cameras and process them locally.

When should you choose On-Premise AI?

On-Premise is often a good fit when:

• many cameras need to be processed centrally;
• multiple AI models are used at the same time;
• large amounts of computing power are required;
• existing VMS or PSIM systems run locally;
• data needs to remain within the organisation’s own infrastructure;
• centralised management is important.

A central server environment is generally easier to manage when large numbers of camera streams come together at one location.

When should you choose Edge AI?

Edge AI can be a good fit when:

• limited network bandwidth is available;
• a location has temporary or limited connectivity;
• very fast local processing is required;
• only a small number of cameras need intelligence;
• video should be processed as locally as possible;
• a central AI server is not practical.

Examples include a remote location, a mobile camera system or a temporary security setup.

Is Edge automatically better for privacy?

Not necessarily. Local processing can reduce the amount of video that needs to be sent across networks, but privacy depends on much more than where the AI runs.

Other factors include:

• which data is processed;
• which metadata is stored;
• who has access;
• how long data is retained;
• what the technology is used for.

The location of AI processing is therefore only one part of a broader privacy and security architecture.

Can On-Premise also be Edge?

Yes, sometimes the concepts overlap. A compact AI server located on-site and close to the cameras can be considered both On-Premise and Edge. That is why it is more important to look at the architecture than at the label.

The real questions are:

Where is the video processed?
How far does the data need to travel?
How much computing power is required?
How is the system managed?

Which option is better?

There is no universally best option. The right architecture depends on factors such as:

• number of cameras;
• available bandwidth;
• latency;
• required AI capacity;
• security;
• management;
• integrations;
• cost;
• scalability.

In larger environments, a hybrid approach is often used, combining Edge, On-Premise and sometimes cloud.

In short:

Edge AI brings processing closer to the camera. On-Premise AI brings processing into the organisation’s own infrastructure. Which option is best depends on the environment and the application.

Want to learn more about the difference between On-Premise and Edge AI? Read this article.