Glossary

AI Data Infrastructure

AI data infrastructure is the storage, compute, networking, and data architecture that enables organizations to collect, manage, process, and protect the data used for artificial intelligence (AI) and machine learning (ML) workloads. It provides the foundation for moving large datasets through AI pipelines, from data ingestion and preparation to model training, inference, and long-term retention.

AI workloads place different demands on infrastructure than many traditional enterprise applications. Training datasets can contain billions of files and objects, data volumes can reach petabyte or exabyte scale, and GPU-based compute environments require data to be available with sufficient throughput to keep expensive processing resources productive.

As AI initiatives grow, organizations therefore need infrastructure that can scale capacity and performance while maintaining data durability, security, accessibility, and operational efficiency.

What are the components of AI data infrastructure?

AI data infrastructure typically combines several interconnected layers.

Data storage

Storage provides persistent capacity for the datasets used throughout the AI lifecycle. These datasets can include documents, images, video, audio, application data, sensor information, model checkpoints, embeddings, and generated data.

AI environments commonly use multiple storage technologies depending on workload requirements. Object storage is particularly well suited to large-scale AI datasets because it can provide high capacity, metadata-rich organization, durability, and horizontal scalability.

Compute infrastructure

AI compute infrastructure provides the processing resources used to train and run models. It can include:

GPUs
CPUs
AI accelerators
GPU clusters
High-performance computing (HPC) systems
Cloud compute resources

Compute and storage must work together efficiently. Large GPU clusters can process data extremely quickly, making storage throughput and data availability important factors in overall AI infrastructure performance.

Networking

High-speed networking connects storage, compute, and other components of the AI environment. Network bandwidth and latency can affect how quickly datasets reach compute resources, particularly when many GPUs or compute nodes access data concurrently.

Data management

Data management technologies organize and govern information across its lifecycle. Capabilities can include metadata management, data classification, access controls, lifecycle policies, replication, versioning, and data movement.

Effective data management becomes increasingly important as organizations maintain multiple datasets, model versions, checkpoints, and AI-generated outputs.

Data protection and security

AI datasets can contain proprietary, regulated, confidential, or otherwise sensitive information. Infrastructure therefore needs controls that protect data against unauthorized access, accidental deletion, infrastructure failure, and cyber threats such as ransomware.

Common capabilities include encryption, identity and access management, immutability, replication, auditing, and recovery mechanisms.

How does AI data infrastructure support the AI data pipeline?

AI data moves through several stages, and infrastructure requirements can change at each stage.

1. Data ingestion

Information is collected from sources such as enterprise applications, databases, IoT systems, archives, websites, sensors, and third-party datasets.

The infrastructure must be capable of ingesting large amounts of structured and unstructured data without creating unnecessary bottlenecks.

2. Data preparation

Raw information is cleaned, transformed, labeled, filtered, or otherwise prepared for AI use. Data preparation can create additional copies and intermediate datasets, increasing overall storage requirements.

3. Model training

During training, GPUs or other accelerators repeatedly access training datasets while model parameters are adjusted.

Large training environments can place substantial throughput demands on the underlying data infrastructure. Storage must supply data efficiently enough to minimize situations where GPUs are waiting for input.

4. Checkpointing

AI training processes periodically save model state as checkpoints. These checkpoints allow training to resume after an interruption rather than restarting from the beginning.

Large models can generate substantial checkpoint data, making scalable storage capacity and write performance important considerations.

5. Model inference

After training, models are used to process new information and generate predictions, classifications, recommendations, or generative AI outputs.

Inference environments can have different performance characteristics from training environments but still require reliable access to models and supporting datasets.

6. Data retention and reuse

Training data, model versions, checkpoints, and generated information may need to be retained for future training, governance, auditing, compliance, or analysis.

Long-term retention makes storage economics and lifecycle management increasingly important as AI environments expand.

Why is storage important for AI infrastructure?

Storage is a central part of AI data infrastructure because AI systems depend on access to large and continuously growing datasets.

GPU performance receives significant attention in AI architecture, but compute resources cannot process information that the surrounding infrastructure cannot deliver efficiently.

Storage for AI workloads may need to support several requirements simultaneously:

Petabyte-scale or larger datasets
Billions of files and objects
High aggregate throughput
Large numbers of concurrent requests
Rapid data ingestion
Model checkpoint storage
Long-term dataset retention
Geographic or infrastructure-level data protection
Integration with cloud-native and AI applications

The appropriate storage architecture depends on the workload. High-performance storage can support active training datasets, while scalable object storage can provide a durable data foundation for large data repositories and multiple stages of the AI lifecycle.

What is the role of object storage in AI data infrastructure?

Object storage stores information as objects rather than organizing it into traditional file-system directories or fixed disk blocks. Each object can contain the data itself, associated metadata, and a unique identifier.

This architecture is useful for AI because many AI datasets consist primarily of large quantities of unstructured data.

Examples include:

Images
Video
Audio
Documents
Log files
Scientific data
Sensor data
Training datasets
Model artifacts
AI-generated content

Modern object storage platforms can scale horizontally across large numbers of storage devices and nodes. This allows organizations to increase capacity as AI datasets grow without repeatedly redesigning the underlying storage architecture.

Rich metadata can also help organizations classify, locate, govern, and manage data across large AI repositories.

AI data infrastructure vs. traditional data infrastructure

Traditional enterprise infrastructure is often designed around transactional databases, virtual machines, business applications, and relatively predictable storage workloads.

AI infrastructure can introduce different requirements.

Traditional data infrastructure	AI data infrastructure
Primarily application-centric	Primarily data- and compute-intensive
GB-to-TB datasets are common	TB-to-PB+ datasets are common
CPU-oriented compute	GPU and accelerator-heavy compute
Structured data is prominent	Large volumes of unstructured data
Predictable application I/O	Highly parallel data access
Moderate data growth	Potentially rapid dataset growth
Application backup and recovery	Dataset, model, and checkpoint protection

The two environments are not mutually exclusive. Most enterprises operate AI infrastructure alongside existing databases, applications, file systems, cloud services, and storage platforms.

What is an AI data lake?

An AI data lake is a large-scale repository used to consolidate datasets that can be accessed by AI, ML, analytics, and other data-intensive applications.

Object storage is commonly used as the foundation for data lakes because it can accommodate large amounts of structured, semi-structured, and unstructured information while supporting extensive metadata.

An AI data lake can help organizations avoid maintaining separate data silos for every AI project. Multiple applications and compute environments can instead access shared datasets according to organizational security and governance policies.

This approach can also make existing enterprise data more accessible for new AI initiatives.

What is an AI data pipeline?

An AI data pipeline is the sequence of processes used to move and transform data from its original source into a form that AI systems can use.

A pipeline can include:

Data collection
Ingestion
Validation
Cleaning
Transformation
Labeling
Storage
Training
Evaluation
Inference
Retention or archival

AI data infrastructure provides the underlying resources that allow these processes to operate at the required scale.

What is GPU data storage?

GPU data storage refers to storage infrastructure designed to provide data to GPU-based computing environments.

GPUs can process many operations in parallel, which makes them particularly effective for AI training. However, large GPU clusters can consume data at very high rates.

If storage or networking cannot provide sufficient throughput, GPUs may spend part of their time waiting for data. This can reduce utilization of costly compute resources and increase the time required to complete AI workloads.

Storage architecture therefore needs to be evaluated alongside GPU and network architecture when designing AI systems.

What should organizations consider when building AI data infrastructure?

Scalability

AI datasets can grow rapidly as organizations add data sources, retain model versions, generate synthetic data, and expand the number of AI projects.

Infrastructure should be capable of scaling without requiring frequent migrations or major architectural changes.

Performance

Performance requirements depend on the AI workload. Training, inference, checkpointing, preprocessing, and archival can each create different I/O patterns.

Organizations should evaluate aggregate throughput, concurrency, latency, and performance at the expected production scale.

GPU utilization

GPUs represent a significant portion of AI infrastructure investment. Data infrastructure should provide sufficient throughput to support the targeted level of GPU utilization.

Data durability

Training datasets and model artifacts can represent substantial investments in collection, preparation, and compute time. Storage systems should protect this information against hardware failures and other forms of data loss.

Cyber resilience

AI data can become a valuable target for cyberattack. Organizations should consider technologies such as immutable storage, access controls, encryption, replication, and recovery processes when designing AI environments.

Data governance

Organizations need to understand which data is being used for AI, who can access it, where it resides, and how long it should be retained.

Metadata, identity controls, auditing, and lifecycle management can help support these requirements.

Cost efficiency

AI infrastructure costs extend beyond GPUs. Storage capacity, networking, power, data movement, cloud services, and long-term retention all contribute to total cost of ownership.

Separating high-performance compute from scalable persistent storage can help organizations match infrastructure resources to different stages of the AI lifecycle.

How does AI data infrastructure support generative AI?

Generative AI systems can require large datasets for training, fine-tuning, retrieval, and inference.

Infrastructure may need to manage:

Foundation model training data
Enterprise datasets
Vector and embedding source data
Retrieval-augmented generation (RAG) datasets
Fine-tuning datasets
Model checkpoints
Model versions
Generated content

For enterprise generative AI, infrastructure also plays an important role in controlling access to proprietary information used by AI applications.

How does AI data infrastructure support RAG?

Retrieval-augmented generation (RAG) combines a generative AI model with information retrieved from an external data source.

Instead of relying entirely on information encoded during model training, a RAG application retrieves relevant enterprise data and provides it as additional context to the model.

AI data infrastructure can provide the underlying storage for source documents, datasets, metadata, and other information used by RAG pipelines.

As RAG environments grow, scalable data storage and management can help organizations maintain large knowledge repositories while controlling access to the underlying information.

AI data infrastructure and hybrid cloud

AI data can exist across on-premises infrastructure, private clouds, public clouds, edge environments, and colocation facilities.

A hybrid approach can allow organizations to place compute resources where they are most appropriate while maintaining control over persistent datasets.

For example, an organization might maintain a large AI data repository on premises while using different GPU environments for individual training or inference workloads.

Consistent APIs and data management practices can help applications access information across these environments without creating unnecessary data silos.

How does Scality support AI data infrastructure?

Scality provides enterprise object storage for organizations managing large and growing volumes of unstructured data.

Scality RING can provide a scalable storage foundation for AI data repositories, including training datasets, model artifacts, checkpoints, and other unstructured information. Its object storage architecture is designed to support large-scale environments while providing capabilities for data durability, security, and long-term retention.

For AI infrastructure architectures, object storage can complement GPU compute and high-performance storage by providing persistent capacity for datasets across the AI lifecycle. This allows organizations to retain and protect large data repositories while making information available to AI and data-intensive applications.

Frequently asked questions

What does AI data infrastructure mean?

AI data infrastructure is the combination of storage, compute, networking, and data management technologies used to collect, process, protect, and deliver data for AI and machine learning workloads.

What infrastructure is needed for AI?

AI infrastructure typically includes GPU or accelerator-based compute, scalable data storage, high-speed networking, data management software, and security and data protection capabilities.

Why does AI require so much storage?

AI models can require very large training datasets. Organizations may also retain multiple dataset versions, model checkpoints, embeddings, model artifacts, and generated data, causing storage requirements to grow quickly.

Is object storage good for AI?

Object storage can be well suited to AI environments that need to manage very large quantities of unstructured data. Its horizontal scalability, metadata capabilities, and object-based architecture make it useful for AI data lakes, training repositories, model artifacts, and long-term retention.

What is the difference between AI infrastructure and AI data infrastructure?

AI infrastructure broadly encompasses all hardware and software required to develop and operate AI systems, including compute, networking, storage, frameworks, and applications. AI data infrastructure focuses specifically on the systems responsible for storing, managing, protecting, and delivering the data used by those AI systems.

What is AI-ready data infrastructure?

AI-ready data infrastructure is an architecture designed to support the scale, performance, accessibility, security, and governance requirements of production AI workloads. It should accommodate growing datasets and integrate with the compute platforms and applications used throughout the AI lifecycle.

Storage software built for data that has to keep running.

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