Glossary

AI data platform

An AI data platform is a product or integrated set of products that provides one shared data layer for AI: it stores data, serves it to GPUs and applications, and applies security and governance in a single place. It is what an organization buys or assembles to implement an architecture.

What does an AI data platform include?

A platform sits between data sources and compute. Below it is scalable storage. Alongside are metadata and discovery, so teams can find and understand data. Around it are access control, security and protection. Above it are fast connections to GPUs and links to the AI tools teams already run, usually through standard interfaces such as S3.

Vector databases, labeling tools and model-serving frameworks usually run alongside or above the platform and are not part of it. Platforms also differ in where data can physically sit, whether formats are open or proprietary, and how they connect to existing systems, which matter more to long-term flexibility than the feature list.

How is it different from a data lake?

A data lake is the storage part: a large repository where raw and prepared data land. If a platform only stores data, it is a data lake with a new name. What makes it a platform is that metadata, access control and protection are part of the layer, so each new project does not rebuild them. The same layer serves several workloads at once, such as training, retrieval and analytics, instead of each owning its own copy.

What is native and what is bolted on?

The practical test for any platform is which capabilities are built in and which the buyer assembles. Metadata handling, access control and protection against deletion or ransomware are the usual three. Bolted-on versions work, but each project then wires them again, and gaps appear at the seams, where permissions fail to carry from one tool to the next.

Gartner predicted in June 2025 that organizations will develop 80% of generative AI business applications on their existing data management platforms by 2028. The point for buyers is that the data layer tends to be extended, not replaced, so how well a platform integrates with what already exists decides how quickly it is used.

Where do platform, architecture and infrastructure differ?

AI data architecture is the plan. AI data infrastructure is the storage, network and compute that carry it out. The platform is the product layer between them that packages many of those capabilities. It adds the governance that AI data management depends on, though the policies still have to be written.

Frequently asked questions

Does a platform replace a data warehouse?

Not usually. A warehouse serves structured analytics, while an AI platform covers unstructured and semi-structured data as well. Many organizations run both and let the platform hold the raw and prepared AI data.

Can a company build its own?

Yes. Many assemble one from object storage, a catalog and access tooling. The test is the same: whether metadata, access and protection are shared or rebuilt per project.

Is a platform a single product?

It can be one product or an integrated set. The definition rests on the shared layer it provides, not on how many vendors supply it.