Best AI storage services

AI storage services provide highly scalable, durable, and high-performance infrastructure specifically designed for massive AI and machine learning workloads, offering features like object and file storage, rapid data access, and integration with AI platforms to manage vast datasets for model training and inference.

Key providers include NetApp, Dell, Pure Storage, VAST Data, and WEKA, with services often leveraging technologies like SSDs, NVMe, and data reduction for optimal speed and efficiency.

Key characteristics of AI storage include:

• High scalability and durability: Designed to handle the immense, ever-growing data volumes required for AI, with systems that can scale from terabytes to exabytes and are highly resilient.
• High performance: Features technologies like SSDs and NVMe for low latency and high throughput, ensuring rapid data access crucial for AI training and inference.
• Data reduction: Incorporates techniques like deduplication and compression to efficiently manage data, reduce storage footprint, and lower costs.
• Specialized architectures: Utilizes object storage, parallel file systems, and other scalable architectures to distribute data and support parallel processing by AI applications.
• AI-native integration: Seamlessly integrates with AI/ML platforms and tools, such as Google's Vertex AI, Snowflake's AI Data Cloud, and others, allowing for direct use of stored data.
• Data security: Implements robust security measures, including access controls and encryption, to protect sensitive AI datasets and ensure data integrity.

As AI deployments grow across industries, the limitations of conventional networked storage become evident. AI storage services bridge this gap by optimizing data layout, incorporating advanced caching, and leveraging parallelism to minimize latency and maximize throughput.

A defining feature of AI storage services is their ability to scale seamlessly as data volumes increase, which is essential due to the ever-expanding size of AI datasets. These systems are architected to allow organizations to add storage capacity on demand, without service interruptions or complex migrations. This flexibility is crucial, as the rapid accumulation of training and inference data would quickly outpace traditional storage limitations.

Durability goes hand in hand with scalability, ensuring that data remains intact and accessible over long periods. AI storage solutions often employ redundant data placement, error correction, and automated failover mechanisms to protect against data loss from hardware failure or corruption. The combination of these capabilities forms a resilient storage backbone that can support persistent, mission-critical AI projects.

Performance is central to AI storage services because AI workloads, especially training large models. often require sustained high-throughput data streams. These platforms are optimized for low-latency access and parallel data flows, so that computations do not stall waiting for input. To accommodate this, technologies like NVMe flash, high-speed networking, and advanced file or object systems are integrated directly into the storage architecture.

Equally important is the efficient serving of data to multiple GPUs or processing nodes simultaneously, avoiding bottlenecks that can diminish the return on expensive AI hardware. AI storage services continuously monitor and optimize performance using intelligent caching, prefetching, and workload-aware algorithms to ensure that pipelines operate at full speed.

AI storage solutions typically support data reduction to help manage the costs of storing large datasets. Methods like deduplication, compression, and erasure coding help minimize the storage footprint while maintaining data integrity. This is important when handling large numbers of images, video frames, and log files typically used in AI projects.

Data reduction can also reduce the burden on networking infrastructure, enabling faster movement of training and test sets between storage and compute nodes. This allows organizations to get more value from their existing investments, deferring or eliminating the need for constant expansion or expensive hardware upgrades.

Unlike conventional storage, AI storage services are often built on specialized architectures that directly support the unique needs of machine learning and deep learning workflows. This may involve the use of parallel file systems, tiered storage that dynamically shifts “hot” and “cold” data, or direct integration with GPU servers for optimized data processing paths.

Because AI workloads have highly variable read and write patterns, these specialized architectures must be adaptive and intelligent. They prioritize rapid response to unpredictable workloads, often embedding telemetry and analytics to automatically adjust storage performance and layout based on current usage. As new use cases emerge, these architectures evolve to support the latest AI frameworks and hardware accelerators.

AI storage services are increasingly designed to integrate natively with popular AI and data analytics platforms, such as TensorFlow, PyTorch, and distributed training orchestration tools. API-level support and plug-ins reduce the friction of deploying data pipelines, allowing seamless workflows from data ingestion and preprocessing to model deployment.

Native integration accelerates time-to-insight and simplifies overall management for data science teams. Such integration can also provide deeper visibility into data movement and utilization patterns, enabling smarter data placement and workflow automation.

Data security in AI storage services addresses the privacy, compliance, and governance challenges that arise from storing sensitive training data, intellectual property, and personal information. Encryption at rest and in transit is standard to prevent unauthorized data access. Role-based access controls and auditing features further ensure that only authorized users and services can retrieve or modify the data.

Modern AI storage solutions also address emerging threats by supporting regulatory compliance certification (such as HIPAA or GDPR) and utilizing anomaly detection to flag unusual data access patterns. Automated data protection and backup routines preserve data history, while integrations with identity management platforms enable centralized control over access.

NetApp provides a comprehensive AI storage solution designed to support the entire AI lifecycle, from data collection and preparation to training, inference, and archiving. Its unified platform seamlessly integrates across on-premises, hybrid, and multi-cloud environments, offering flexibility and scalability for AI workloads. NetApp leverages its ONTAP data management software to deliver high performance, data protection, and operational simplicity.

Key features include:

• Unified AI data platform: Combines file, block, and object storage under a single platform, ensuring seamless data management across on-premises and cloud environments.
• Hybrid and multi-cloud readiness: Offers native integrations with major cloud providers, enabling flexible deployment and data mobility for hybrid and multi-cloud AI workflows.
• High performance and scalability: Delivers low-latency, high-throughput data access to meet the demands of AI and machine learning workloads at scale.
• Data protection and security: Includes advanced data protection features such as snapshots, replication, and encryption to safeguard critical AI data.
• Simplified management: Centralized control through NetApp ONTAP and Cloud Manager, enabling automated workflows, efficient resource allocation, and streamlined operations.

Limitations (as reported by users on G2):

• Initial setup: Some users report that product training, available from NetApp, is helpful to properly configure and optimize for AI workloads.
• Learning curve for management tools: A few users mention that mastering a NetApp data management interface, such as ONTAP, can take time, especially for those new to the platform.
• Limited small file optimization: Some reviewers note that NetApp's performance for workloads involving large volumes of small files may not match that of AI storage solutions specializing in smaller volumes.

AI storage solutions from NetApp are well-suited for organizations seeking a unified, scalable, and secure platform to support their AI initiatives, though potential users should consider the setup complexity and cost when evaluating the platform.

Pure Storage provides a unified, as-a-service storage platform spanning on-premises and public clouds. It consolidates block, file, and object storage under a common operating system with centralized control and automation. The platform uses an Evergreen architecture for non-disruptive upgrades and management through Pure1 and Pure Fusion. It is a proprietary stack delivered via subscriptions, associating adoption with vendor lock-in.

Key features include:

• Unified data platform: Provides one platform across on-prem and cloud with a common OS, supporting block, file, and object protocols.
• Delivered as a service: Offers on-demand capacity and data services with automated provisioning, scaling, and updates, eliminating manual maintenance and planned downtime tasks.
• Intelligent control plane: Centralized management with Pure1 and Pure Fusion for visibility, automated workflows, self-service upgrades, and anomaly alerts across environments.
• Evergreen architecture: Supports non-disruptive component replacement and Purity OS upgrades, extending array lifecycles and avoiding migrations during hardware or software changes.
• Resilience and data services: Provides data reduction, protection, security, and high availability targets, plus backup, disaster recovery, and cloud integration with SLA-backed delivery.

Limitations (as reported by users on G2):

• High and rising cost: Users frequently mention that Pure Storage is expensive. While many see a strong return on investment, the pricing can be a barrier for smaller organizations. Some also note that costs have increased over time, contrary to broader market trends in storage.
• Limited mobile and offline access: A few users reported difficulty accessing certain files from mobile devices or working offline. These limitations can affect workflows that rely on flexibility or real-time mobile access.
• Interface limitations for advanced features: Creating snapshots and performing certain file operations could be more intuitive. Some users expressed the need for easier management tools for these tasks.
• Lack of granular control options: Reviewers noted the inability to throttle specific LUNs or perform certain actions directly from the desktop. These limitations may affect environments needing fine-grained performance control or desktop integration.

WEKA offers a high-performance, software-defined data platform purpose-built for AI, machine learning, and deep learning workloads. WEKA consolidates multiple storage types into a unified system that works seamlessly across on-prem and cloud environments. However, it focuses on the training throughout and doesn’t support the full AI lifecycle.

Key features include:

• Unified AI data platform: Supports the entire AI pipeline on a single platform, combining file and object storage with POSIX compliance
• Cloud-native and on-prem ready: Deployable across public cloud and on-premises infrastructure for flexible, hybrid AI operations
• High throughput and low latency: Delivers fast, consistent data access for performance-intensive AI workloads at terabyte to exabyte scale
• Optimized for small file workloads: Effectively handles large volumes of small files, overcoming limitations of legacy storage systems
• Simplified AI DataOps: Integrates compute, storage, and fast networking to accelerate AI data movement and model iteration cycles

Limitations (as reported by users on G2):

• High cost: Users mention that WEKA is more expensive than some competing solutions. While the performance is praised, the pricing may be a concern for teams with limited budgets.
• Limited review data: There are relatively few user reviews available on WEKA, making it harder for buyers to evaluate the platform based on a broad set of user experiences.

VAST Data delivers an AI data platform to support the scale, speed, and resilience required by modern AI workloads. VAST addresses the limitations of traditional storage architectures with a flash-first, single-tier architecture that eliminates legacy bottlenecks. Its disaggregated design separates compute and storage, allowing independent scaling. However, it doesn’t support the entire AI data pipeline.

Key features include:

• Flash-first architecture: Eliminates spinning disks with a universal, high-performance flash storage layer designed for AI speed and efficiency
• Single-tier design: Unifies all workloads under one simplified data infrastructure; no need for tiering or multiple storage systems
• Disaggregated compute and storage: Scales linearly without forced upgrades, enabling flexible and predictable resource expansion
• High availability and durability: Supports 24/7/365 operations with advanced data protection and reduction technologies
• Optimized for AI pipelines: Designed to accelerate model training, testing, and inference with high-throughput, low-latency access

Limitations (as reported by users on G2):

• Complex setup: Several users report that the initial deployment can be technically challenging and time-consuming, particularly when working with QLC-based configurations.
• High pricing: Some users note that VAST Data is more expensive compared to other solutions on the market, which can impact adoption in cost-sensitive environments.
• Occasional performance lag: A few reviewers mention intermittent lag or scalability issues, though these are not consistently reported and may depend on specific workloads or environments.

The Dell AI Data Platform integrates PowerScale, ObjectScale, and a Dell Data Lakehouse to support the AI lifecycle, from ingesting and processing data to securing it across environments. However, it can be a legacy-heavy solution, making it less suitable for hybrid and multi-cloud AI.

Key features include:

• Open and flexible architecture: Avoids vendor lock-in and adapts to changing AI and business needs
• High-performance storage infrastructure: PowerScale and ObjectScale enable scalable, high-throughput data handling
• Integrated data lakehouse: Supports structured and unstructured data for a complete AI pipeline
• Data placement and processing optimization: Efficiently lands data and extracts insights across hybrid environments
• Cybersecurity integration: Includes robust data protection measures to defend against threats and unauthorized access

AI storage services play a crucial role in enabling the performance, flexibility, and resilience that modern AI workloads demand. As organizations scale up their use of machine learning and data-intensive models, traditional storage solutions often fall short in handling the volume, velocity, and variability of AI data.

By leveraging purpose-built architectures, intelligent data management, and deep integration with AI ecosystems, these services provide the foundation for efficient model development, faster time to insight, and sustained innovation across industries.