Glossary

AI Data Platform

An AI data platform is an integrated technology environment for storing, managing, processing, governing, and accessing the data used to build and operate artificial intelligence (AI) applications.

It provides the data infrastructure needed to support AI and machine learning (ML) workflows, including generative AI, model training, inference, retrieval-augmented generation (RAG), and analytics.

AI workloads often depend on large volumes of structured and unstructured data distributed across applications, data centers, edge locations, and clouds. An AI data platform helps organizations consolidate or connect these data sources while providing the performance, scalability, security, and governance required throughout the AI lifecycle.

How does an AI data platform work?

An AI data platform provides a common data layer between an organization’s data sources and the compute resources, models, and applications that use that data.

Data may originate from databases, applications, sensors, documents, images, video, logs, scientific instruments, or other systems. The platform stores or connects this information and makes it available to AI pipelines through standardized interfaces and data services.

Depending on the architecture, an AI data platform may support:

  • Data ingestion from multiple sources and locations
  • High-capacity storage for structured and unstructured data
  • Metadata management and data discovery
  • Data preparation and transformation
  • APIs and protocols for accessing data
  • Integration with CPUs, GPUs, and AI accelerators
  • Model training and inference workflows
  • Vector databases and RAG pipelines
  • Data governance, security, and access controls
  • Data protection, replication, and lifecycle management

This architecture allows AI applications and infrastructure to access the appropriate data without requiring each workload to maintain a separate data environment.

Why are AI data platforms important?

The effectiveness of an AI system depends heavily on the availability, quality, and accessibility of its underlying data. As AI projects grow, managing that data can become a significant infrastructure challenge.

Training large models, for example, can require processing billions of files or objects. Generative AI applications may need to search large repositories of enterprise documents during inference. Computer vision and scientific AI workloads can generate very large datasets that must remain accessible to GPU-intensive processing environments.

An AI data platform provides a consistent foundation for these requirements. Organizations can use it to manage growing datasets while maintaining policies for security, governance, availability, and data lifecycle management.

What are the key components of an AI data platform?

Although implementations vary, AI data platforms typically include several core capabilities.

Scalable data storage

AI workloads can generate and consume data at petabyte or exabyte scale. The underlying storage architecture must accommodate this growth without creating operational complexity or isolated data silos.

Object storage is commonly used for AI datasets because it can efficiently manage very large quantities of unstructured data while providing broad application compatibility through APIs such as Amazon S3.

High-performance data access

GPUs and other AI accelerators require a steady supply of data to operate efficiently. Storage and data services therefore need sufficient throughput, concurrency, and scalability to support demanding training and inference workloads.

Performance requirements can vary substantially between workloads. Large sequential datasets, billions of small objects, checkpoint data, and inference requests each create different access patterns.

Metadata and data discovery

AI teams need to identify which datasets are available, where they originated, and whether they are appropriate for a particular workload.

Metadata services help classify, index, search, and manage datasets. Rich metadata can also support data lineage, governance, RAG applications, and automated AI pipelines.

Data governance and security

Enterprise AI systems may process intellectual property, customer information, research data, and other sensitive content. AI data platforms therefore require controls for authentication, authorization, encryption, auditing, retention, and data sovereignty.

Governance capabilities also help organizations determine which users, models, and applications can access particular datasets.

Data protection and resilience

AI datasets can require substantial time and resources to collect, prepare, or reproduce. Protecting them against hardware failures, ransomware, accidental deletion, and other disruptions is therefore an important part of AI infrastructure design.

Capabilities such as immutability, replication, erasure coding, versioning, and geographic distribution can improve data resilience.

Integration with the AI ecosystem

An AI data platform must connect with the tools organizations use to develop and deploy AI. These may include machine learning frameworks, Kubernetes environments, GPU clusters, vector databases, data processing frameworks, model repositories, and cloud AI services.

Open APIs and widely adopted protocols can reduce the need for workload-specific data integrations.

AI data platform vs. data lake

An AI data platform and a data lake can overlap, but the terms describe different concepts.

A data lake is primarily an architecture for storing large quantities of structured, semi-structured, and unstructured data in its native or minimally processed form. It is commonly used for analytics, data science, and machine learning.

An AI data platform has a broader role. It combines data storage with services designed to make data available, manageable, secure, and useful across AI workflows. A data lake may therefore be one component of an AI data platform.

AI data platform vs. AI infrastructure

AI infrastructure includes the full collection of hardware and software required to run AI workloads, including compute, networking, storage, orchestration, and development tools.

An AI data platform focuses specifically on the data layer. It manages how data is stored, protected, governed, discovered, and delivered to AI infrastructure.

For example, a GPU cluster provides the compute resources for model training, while the AI data platform supplies the training datasets and manages their availability and protection.

What workloads can an AI data platform support?

AI data platforms can support workloads across the AI lifecycle, including:

  • Model training: Supplying large datasets to GPU and accelerator clusters.
  • Model fine-tuning: Providing domain-specific enterprise datasets for adapting existing models.
  • Generative AI: Managing documents, media, and other content used by large language models (LLMs) and multimodal models.
  • Retrieval-augmented generation: Supplying enterprise data that can be indexed, retrieved, and incorporated into model responses.
  • Inference: Providing data to production AI applications as they process requests.
  • Computer vision: Managing large collections of images and video used for training and analysis.
  • Scientific AI: Storing large datasets produced by simulations, instruments, and research workflows.
  • MLOps: Supporting datasets, model artifacts, checkpoints, logs, and other information generated throughout model development and deployment.

What are the benefits of an AI data platform?

A well-designed AI data platform can help organizations:

  • Scale data capacity as AI datasets grow
  • Improve data availability for GPU-intensive workloads
  • Reduce isolated copies and data silos
  • Provide consistent security and governance policies
  • Protect valuable training and enterprise datasets
  • Make existing enterprise data accessible to generative AI applications
  • Support AI workloads across on-premises, edge, and cloud environments
  • Simplify integration between data infrastructure and AI tools

These capabilities become increasingly important as organizations move from individual AI experiments to larger production environments involving multiple models, applications, teams, and datasets.

What should organizations look for in an AI data platform?

Organizations evaluating an AI data platform should consider both current workloads and expected data growth. Important criteria include scalability, throughput, availability, interoperability, security, and operational complexity.

The platform should also support the types of data involved in the organization’s AI initiatives. Unstructured data such as documents, images, video, audio, logs, and scientific datasets can account for a substantial portion of AI training and retrieval data.

For large-scale environments, organizations should also evaluate how the platform protects data against cyber threats and infrastructure failures, integrates with existing AI and cloud ecosystems, and manages data across multiple locations.

How does object storage support AI data platforms?

Object storage is frequently used as the foundation for AI data platforms because AI workloads often involve very large quantities of unstructured data.

An object storage system can provide a scalable namespace for datasets while exposing them through standardized APIs. This allows AI frameworks, applications, and data processing tools to work with a shared data repository.

At larger scales, object storage can also provide capabilities such as erasure coding, replication, immutability, lifecycle management, and geographic distribution. These features help organizations maintain AI datasets throughout their lifecycle while balancing performance, resilience, capacity, and cost.

AI data platform FAQ

Is an AI data platform the same as a data platform?

No. A general data platform may support databases, business intelligence, analytics, and application data. An AI data platform is designed or optimized for the data requirements of AI and ML workloads, including large-scale training, inference, generative AI, and RAG.

Does an AI data platform require GPUs?

Not necessarily. GPUs and other accelerators typically belong to the compute layer rather than the data platform itself. However, an AI data platform should be able to deliver data efficiently to GPU-based infrastructure when required.

Can an AI data platform run on premises?

Yes. AI data platforms can be deployed on premises, in public clouds, at the edge, or across hybrid and multicloud environments. Deployment choices depend on factors such as data volume, performance, sovereignty, security, and cost.

What types of data are used by AI data platforms?

AI data platforms can manage structured, semi-structured, and unstructured data. Examples include database records, documents, images, audio, video, application logs, sensor data, scientific datasets, model checkpoints, and other AI artifacts.

How does an AI data platform support generative AI?

Generative AI applications need access to data for training, fine-tuning, and retrieval. An AI data platform provides the storage, access, metadata, security, and governance capabilities required to make enterprise data available to these workflows.