Table of Contents
Glossary
AI Storage
AI storage is data storage infrastructure designed to support the performance, scale, and data access requirements of artificial intelligence (AI) and machine learning (ML) workloads.
It provides the capacity, throughput, availability, and data management capabilities needed to store and access the large volumes of structured and unstructured data used throughout the AI lifecycle.
What is AI storage?
AI systems depend on data at nearly every stage, from preparing datasets and training models to inference, retrieval-augmented generation (RAG), and retaining generated outputs. AI storage provides the underlying infrastructure for storing this data and making it available to compute resources such as GPUs and AI accelerators.
Unlike conventional enterprise workloads, AI applications can involve billions of files and objects, petabytes of data, highly parallel access patterns, and rapid changes in how data is read and written. An AI storage architecture must therefore balance capacity, performance, scalability, availability, and cost.
Object storage is particularly relevant for AI because much of the information used by AI systems is unstructured, including documents, images, video, audio, application data, sensor data, model checkpoints, and other large datasets.
How does AI storage work?
AI storage connects large data repositories with the compute infrastructure responsible for processing AI workloads. Data may be collected from applications, devices, databases, data lakes, archives, and other enterprise systems before being prepared for AI use.
A typical AI data workflow includes:
- Data ingestion: Data is collected and written to a storage platform.
- Data preparation: Raw information is cleaned, transformed, classified, or organized into datasets suitable for AI processing.
- Model training: GPUs or other accelerators repeatedly access training datasets while building or fine-tuning models.
- Checkpoint storage: Intermediate model states are saved so training can be resumed or recovered.
- Inference: Trained models access data to generate predictions, classifications, recommendations, or other outputs.
- Data retention: Models, datasets, outputs, logs, and supporting information are retained for reuse, governance, analysis, or compliance.
Storage requirements vary across these stages. Training can require high throughput and parallel data access, while long-term datasets may prioritize capacity, durability, security, and cost efficiency.
Why is storage important for AI?
AI infrastructure is often designed around expensive GPU resources. If storage cannot deliver data quickly enough, compute resources can spend time waiting for data rather than processing it.
Storage can therefore influence both AI performance and infrastructure utilization. The storage layer must be capable of serving large datasets across many concurrent compute nodes while continuing to protect and manage the underlying data.
Storage also plays an important role beyond performance. Organizations need to retain growing collections of source data, training datasets, model versions, embeddings, checkpoints, and AI-generated information. As these repositories expand, scalability and operational efficiency become increasingly important.
What data does AI storage handle?
AI storage environments can contain several types of information, including:
- Training and validation datasets
- Documents and enterprise content
- Images, audio, and video
- Scientific and sensor data
- Application and machine-generated data
- Model files and model weights
- Training checkpoints
- Vector and embedding source data
- RAG knowledge repositories
- Inference inputs and outputs
- Logs and telemetry
- Archived datasets and model versions
Many of these datasets are unstructured and can grow to petabyte scale or beyond, making scalable object storage a common component of AI data architectures.
AI storage and object storage
Object storage stores information as objects within a flat namespace rather than organizing data through traditional hierarchical file systems. This architecture is well suited to very large collections of unstructured data.
For AI workloads, object storage can provide several advantages:
- Scalability: Capacity can expand as AI datasets grow.
- Large namespaces: Object architectures can support very large numbers of objects without relying on complex directory structures.
- S3 compatibility: S3 APIs provide a widely adopted method for applications and AI tools to access object data.
- Data durability: Data protection mechanisms help preserve valuable datasets and models.
- Metadata: Objects can include metadata that supports data organization, discovery, and management.
- Cost efficiency: Large datasets can be retained on storage infrastructure designed for high-capacity environments.
These characteristics make object storage suitable for data lakes, AI training repositories, RAG data, model repositories, and other large-scale AI datasets.
AI storage for model training
Training AI models can create demanding storage access patterns. Multiple GPUs may need to read portions of a dataset simultaneously, creating large numbers of parallel requests.
If storage throughput is insufficient, GPUs may become underutilized while waiting for data. AI training storage therefore needs to deliver sufficient aggregate throughput as clusters expand.
Training also generates model checkpoints at regular intervals. These checkpoints can be large, particularly for large language models (LLMs), and may create bursts of write activity. Efficiently storing and retrieving checkpoints can help teams recover from interruptions and manage long-running training jobs.
AI storage for RAG
Retrieval-augmented generation allows an AI application to retrieve relevant information from an external knowledge source and provide that context to a generative AI model.
RAG architectures can involve several storage technologies. Vector databases typically manage embeddings and similarity searches, while object storage can retain the underlying source content from which those embeddings are created.
This source content can include PDFs, documents, images, recordings, datasets, and other enterprise information. Maintaining scalable and protected repositories for this data can help organizations build and update AI knowledge bases as their information changes.
AI storage security
AI datasets can contain intellectual property, business records, customer information, research data, and other sensitive information. Security is therefore an important consideration when designing AI storage infrastructure.
Relevant capabilities can include:
- Encryption for data at rest and in transit
- Identity and access management
- Role-based access controls
- Audit logging
- Data immutability
- Object locking
- Versioning
- Replication
- Backup and recovery
- Protection against ransomware and destructive attacks
Organizations should apply the same security, governance, and compliance requirements to AI data that they apply to other critical enterprise information.
AI storage challenges
Several infrastructure challenges become more significant as AI environments grow.
Data growth: Training data, model files, checkpoints, and generated content can rapidly increase storage requirements.
GPU utilization: Storage performance can affect how consistently data reaches expensive AI compute resources.
Parallel access: Large GPU clusters can generate substantial numbers of simultaneous storage requests.
Data movement: Moving petabyte-scale datasets between storage systems, clouds, and compute environments can introduce cost and operational complexity.
Data protection: AI repositories can contain valuable source data and intellectual property that require strong security controls.
Infrastructure silos: Separate storage platforms for training, analytics, archives, and other workloads can increase data duplication and management overhead.
What should organizations look for in AI storage?
An AI storage platform should be evaluated according to the workloads it needs to support rather than performance alone. Important considerations include:
- Capacity and performance scalability
- Aggregate throughput
- Concurrent access capabilities
- S3 API compatibility
- Support for large object counts
- Data durability and availability
- Security and ransomware protection
- Integration with AI and data management ecosystems
- Deployment flexibility
- Hardware efficiency
- Operational simplicity
- Total cost of ownership
Organizations should also consider how storage requirements may change as AI projects move from experimentation into production and datasets increase in size.
AI storage and Scality
Scality provides enterprise object storage for large-scale unstructured data environments, including data used by AI and machine learning applications. Scality RING provides an S3-compatible storage foundation that can consolidate large datasets and make them accessible to applications and AI infrastructure.
For organizations building AI data pipelines, object storage can provide a persistent data layer for training datasets, RAG source data, model artifacts, checkpoints, and other AI-related information. This approach can help organizations manage data growth while maintaining enterprise requirements for availability, durability, security, and infrastructure flexibility.
Frequently asked questions
What is AI storage?
AI storage is storage infrastructure designed to store, protect, and provide access to the large datasets, models, checkpoints, and other information used by artificial intelligence and machine learning workloads.
Why does AI need specialized storage?
AI workloads can involve petabytes of data, large numbers of objects, highly parallel access, and substantial throughput requirements. Storage infrastructure must be able to scale alongside AI compute resources while maintaining reliable access to data.
Is object storage good for AI?
Object storage is well suited to many AI workloads because it can provide scalable capacity for large volumes of unstructured data, support very large namespaces, and expose data through widely adopted APIs such as S3.
Does AI storage improve GPU utilization?
Storage can affect GPU utilization because GPUs need a consistent supply of data to remain productive. Sufficient storage throughput and parallelism can reduce periods in which compute resources wait for data.
What is the difference between AI storage and a vector database?
AI storage retains datasets, documents, media, models, checkpoints, and other underlying data. A vector database stores and searches vector embeddings used for similarity-based retrieval. The technologies can work together in AI architectures such as RAG.
What is the role of object storage in generative AI?
Object storage can provide a scalable repository for training data, enterprise knowledge, model artifacts, checkpoints, and generated content. It can also serve as the source-data layer behind RAG and other generative AI applications.