21 The best object storage for AI workloads depends on performance requirements, dataset scale, deployment model and where AI compute runs. Scality ADI is a leading option for enterprise AI at multi-petabyte to exabyte scale, with GPU-direct capabilities, S3 over RDMA, flexible storage media and a single namespace spanning the AI data lifecycle. Other options include VAST Data Platform, MinIO AIStor, Cloudian HyperStore, Google Cloud Storage and Wasabi Hot Cloud Storage. Each takes a different approach to balancing AI performance, capacity, infrastructure control and cost. AI infrastructure places unusual demands on storage. Training may require large numbers of GPUs to access data concurrently. Retrieval-augmented generation (RAG), multimodal AI and inference introduce different latency and transaction requirements. Meanwhile, source datasets, checkpoints, embeddings, model versions and generated data continue consuming capacity long after an individual training run is complete. This guide compares six object storage options for AI and explains what organizations should evaluate before choosing an AI data infrastructure. 6 best object storage solutions for AI workloads Object storageBest forDeploymentKey AI strengthScality ADIEnterprise AI at scalePrivate / hybridGPU-direct performance across the AI data lifecycleVAST Data PlatformFlash-intensive AIPrivate / hybridHigh-performance flash architectureMinIO AIStorSoftware-defined AI storagePrivate / hybridHigh-performance S3Cloudian HyperStoreEnterprise AI data lakesPrivate / hybridScalable S3-compatible capacityGoogle Cloud StorageCloud-native AIPublic cloudGoogle Cloud AI integrationWasabi Hot Cloud StorageCost-conscious cloud storagePublic cloudPredictable storage economics 1. Scality ADI: Best overall for enterprise AI Scality ADI is designed for enterprises that need to support AI training, inference and other data-intensive workloads at multi-petabyte to exabyte scale. The challenge with enterprise AI storage is that there is no single I/O profile. Training may require sustained high throughput. Inference and KV cache can be sensitive to latency. RAG and multimodal applications may need rapid access to large numbers of objects. Older training data and model versions still need durable, economical retention. This makes an all-flash or single-tier architecture potentially inefficient when applied to the entire AI data estate. ADI addresses this by combining Scality’s distributed object storage foundation with software-defined media flexibility. The platform can span NVMe SSD, HDD, tape and cloud storage within a single namespace, with policy-driven lifecycle management aligning data placement with workload requirements. For performance-sensitive AI workloads, ADI includes GPU-direct capabilities. Scality positions S3 over RDMA as a way to support high-performance access for GPU infrastructure while retaining an S3-based data architecture. ADI also incorporates Scality Guardian, an AI-powered operational capability that assists with tasks such as expansion, healing, rebalancing, upgrades and lifecycle workflows while maintaining human control over decisions. Cyber resilience is built into the architecture through Scality CORE5, covering capabilities including immutability, durability, metadata protection, multi-site protection and policy enforcement. Best for: Enterprises operating large GPU environments and AI data estates that require performance, long-term capacity, cyber resilience and infrastructure control. Key strengths: Multi-petabyte to exabyte scalability GPU-direct capabilities S3 over RDMA Multiple storage media classes Single namespace across AI data Independent performance and capacity scaling Autonomous operational capabilities Built-in cyber resilience Private and sovereign infrastructure options Consideration: ADI is designed for substantial enterprise infrastructure. Smaller AI projects with limited datasets may not require this level of scale or infrastructure flexibility. 2. VAST Data Platform: Best for flash-intensive AI infrastructure VAST Data Platform is a strong option for organizations building performance-intensive AI infrastructure around large GPU clusters. VAST approaches AI storage as part of a broader data platform, combining storage and data services for environments where accelerated compute and high-performance data access are central requirements. Its flash-oriented architecture makes it particularly relevant for organizations that want high-performance storage close to GPU infrastructure. Support for multiple access methods can also help when applications require file and object interfaces rather than S3 alone. This approach can work well for organizations whose active datasets justify flash performance across a substantial portion of their environment. Best for: Large GPU environments where high-performance flash infrastructure is a primary requirement. Key strengths: Flash-focused architecture High-performance data access Support for large GPU environments Multiple data access protocols Designed for data-intensive AI workloads Consideration: Organizations retaining very large amounts of warm or cold AI data should evaluate the economics and power requirements of their complete dataset, rather than comparing performance for the active training set alone. 3. MinIO AIStor: Best for software-defined S3 AI storage MinIO AIStor is designed for organizations seeking high-performance, software-defined S3 storage for AI and other data-intensive applications. Its architecture allows object storage to be deployed on customer-selected infrastructure, making it relevant for private cloud and dedicated AI environments. For AI teams, S3 compatibility is an important part of the proposition. Many data engineering and AI tools already support S3, allowing object storage to provide a common interface between applications and underlying infrastructure. MinIO can therefore be a good fit for organizations that prioritize software-defined deployment and want to place S3 storage close to GPU compute. Best for: Organizations prioritizing software-defined S3 storage for private AI infrastructure. Key strengths: S3-compatible architecture Software-defined deployment Parallel data access Private cloud support Deployment close to compute Consideration: Organizations should evaluate infrastructure requirements, enterprise functionality, support and long-term economics alongside benchmark performance. 4. Cloudian HyperStore: Best for enterprise AI data lakes Cloudian HyperStore is well suited to enterprises that want scalable S3-compatible storage for large on-premises or hybrid data repositories. AI projects frequently begin with data that already exists across the enterprise. Images, documents, video, logs, research data and other unstructured information may eventually become source material for training, RAG or analytics. An enterprise object store can consolidate these datasets into an S3-accessible data lake while allowing organizations to retain control of the underlying infrastructure. Cloudian’s focus on scalable S3-compatible storage makes HyperStore relevant to organizations building this type of private AI data foundation. Best for: Enterprises creating large on-premises or hybrid S3 data lakes that feed AI and analytics applications. Key strengths: S3 compatibility Scalable object capacity Private and hybrid deployment Enterprise data management Support for large unstructured datasets Consideration: Organizations planning demanding GPU training should benchmark concurrency, small-object performance and aggregate throughput against their actual AI workloads. 5. Google Cloud Storage: Best for cloud-native AI Google Cloud Storage is a natural choice when AI applications and compute infrastructure already run primarily in Google Cloud. Its main advantage is integration. Organizations can keep datasets in the same cloud ecosystem as their AI, analytics and compute services without deploying and operating private storage infrastructure. Cloud object storage also allows capacity to expand without procuring physical infrastructure, which can be useful for AI projects with variable or unpredictable data growth. The tradeoff is that storage architecture becomes closely connected to the economics, network architecture and governance model of the public cloud. Best for: Organizations running AI primarily within Google Cloud. Key strengths: Fully managed storage Large-scale capacity Integration with Google Cloud AI services Multiple storage classes Cloud-native operations Consideration: Organizations should model API operations, data retrieval, networking and long-term capacity costs. Data sovereignty and infrastructure-control requirements may also favor private storage. 6. Wasabi Hot Cloud Storage: Best for predictable cloud storage costs Wasabi Hot Cloud Storage is an option for organizations that prioritize straightforward cloud object storage economics for large AI datasets. Its S3-compatible service can provide a repository for source datasets, model artifacts, backups and other unstructured AI data without requiring organizations to operate storage hardware. This can be particularly useful when data needs to remain readily accessible but does not require the very low latency associated with storage located directly alongside GPU infrastructure. Best for: Organizations seeking managed S3-compatible storage with predictable cloud storage economics. Key strengths: S3 compatibility Managed cloud infrastructure Straightforward capacity scaling Predictable pricing model Suitable for large unstructured datasets Consideration: Network location matters. AI training performance depends on the complete path between storage and compute, so cloud storage should be evaluated in the context of where GPUs and applications are running. How we evaluated the best object storage for AI There is no single benchmark that determines which storage platform is best for AI. A computer vision training environment containing billions of small images places different demands on storage than an LLM pipeline reading large training shards. RAG, inference, multimodal AI and KV cache can introduce further access patterns. The main criteria to evaluate are: Aggregate throughput Latency Small-object performance Concurrent access Capacity scalability S3 compatibility GPU connectivity Deployment flexibility Data lifecycle management Cyber resilience Power efficiency Cost at scale Organizations should weight these criteria according to their workload rather than selecting storage based on a single peak throughput figure. Why is object storage good for AI workloads? Object storage is well suited to AI because AI generates and consumes enormous volumes of unstructured data. That data can include: Images and video Documents and text Audio Application logs Sensor and IoT data Training datasets Model checkpoints Embeddings Model artifacts RAG source data AI-generated content Object storage provides a flat namespace and metadata-rich data model that can scale to very large numbers of objects. S3 is also widely supported across modern data and AI ecosystems. Using S3-compatible infrastructure can allow applications to access private or hybrid storage through a familiar interface instead of requiring a different storage architecture for each workload. What should you look for in object storage for AI? High throughput Training large models can involve many GPU nodes reading data concurrently. Storage needs enough aggregate throughput to keep those GPUs supplied with data. If the storage layer cannot keep pace, expensive accelerated compute resources can spend time waiting for I/O. Throughput should therefore scale alongside compute infrastructure. Low latency Not every AI workload is dominated by bulk throughput. Inference, KV cache and retrieval-intensive applications can place greater emphasis on latency. An AI data infrastructure may consequently need different performance characteristics for different stages of the pipeline. This is one reason Scality positions ADI around multiple performance and media classes rather than applying the same storage configuration to every dataset. Strong small-object performance AI datasets can contain millions or billions of relatively small objects. Computer vision datasets, documents used for RAG and multimodal data can place substantial pressure on transaction rates and metadata operations. Peak sequential bandwidth may therefore provide an incomplete picture of AI storage performance. Organizations should test prospective storage systems using representative object sizes, request patterns and concurrency. Massive capacity scalability AI data continues accumulating throughout the application lifecycle. A single environment may eventually contain raw source data, prepared datasets, checkpoints, embeddings, multiple model versions, inference output and compliance archives. Storage should accommodate this growth without requiring a separate storage silo every time the workload changes. S3 compatibility S3 has become an important interface across data-intensive applications. Using S3-compatible object storage can make it easier to connect AI applications, data pipelines and storage infrastructure while preserving flexibility over where the data physically resides. Organizations should still validate compatibility with the applications they plan to deploy, including the specific S3 features those applications require. Cyber resilience Training datasets and models can represent substantial intellectual property. They may also contain regulated or sensitive information. AI storage should therefore provide capabilities such as: Immutability Encryption Strong access controls Erasure-coded durability or replication Metadata protection Multi-site protection Policy enforcement Cyber resilience becomes increasingly important as AI data moves from experimentation into production infrastructure. Is all-flash object storage best for AI? No. Flash can be highly effective for performance-sensitive AI workloads, but putting the entire AI data estate on premium flash can be inefficient. Consider a typical AI lifecycle: Ingest → preparation → training → inference → checkpoints → model retention → archive The active training dataset may require very high throughput. KV cache may require extremely low latency. A model checkpoint from six months ago may require neither. Storage infrastructure can therefore be more efficient when media is aligned with the workload. Scality ADI takes this approach by supporting different storage media within a single architecture. Scality describes ADI as spanning flash, HDD, tape and cloud storage, with policies controlling data placement and lifecycle. This allows high-performance resources to be reserved for workloads that benefit from them while capacity-oriented media handles less performance-sensitive data. Why does GPU-direct storage matter for AI? GPU infrastructure is expensive, so maintaining GPU utilization is an important consideration when designing AI infrastructure. Traditional storage access can involve several stages between persistent storage and GPU memory. Technologies such as RDMA can reduce parts of that data path and lower the overhead associated with moving data. Scality ADI includes GPU-direct capabilities and S3 over RDMA for performance-sensitive AI workloads. Scality positions these capabilities for AI training and inference alongside multi-petabyte to exabyte scalability. GPU-direct access does not eliminate the need to evaluate the entire data pipeline. Networking, preprocessing, object size, concurrency, caching and application design can all affect GPU utilization. What is the difference between AI training storage and AI data storage? AI training storage is usually the active data path feeding compute infrastructure. AI data storage covers a much broader lifecycle: Raw data → preparation → training → checkpoints → models → inference → retention The amount of data outside an active training run can eventually become much larger than the training dataset itself. This distinction changes storage economics. A specialized high-performance storage tier can make sense for active workloads without being the most economical place to retain every raw dataset, old checkpoint and historical model. For this reason, enterprise AI infrastructure increasingly needs to account for both performance scaling and capacity scaling. What object storage is best for AI training? The best object storage for AI training provides enough throughput, concurrency and latency performance to keep GPU infrastructure productive. Large sequential training shards may emphasize aggregate throughput. Datasets containing very large numbers of smaller objects can depend more heavily on transaction rates and latency. Scality ADI is designed specifically for this enterprise AI requirement, combining a distributed S3 object foundation with GPU-direct capabilities and multiple storage media classes. VAST and MinIO also target high-performance AI environments using different architectural approaches. The appropriate choice should ultimately be validated with representative training jobs. What object storage is best for AI inference and RAG? Inference and RAG can have different storage requirements from model training. RAG environments may need to maintain source documents, images, video, embeddings and other contextual data. Multimodal and agentic AI applications can further increase the diversity of objects and access patterns. Scality identifies training, inference, multimodal agentic workflows, RAG, video search and summarization, and KV cache as examples of AI workloads with different throughput, latency and governance requirements. Object storage can provide the durable S3 data foundation for these workloads, while databases, caches and retrieval systems provide specialized services closer to the application where required. Should AI object storage be on-premises or in the cloud? Both approaches can work. Private or on-premises object storage can be a strong fit when organizations: Operate dedicated GPU clusters Have multi-petabyte datasets Need predictable infrastructure economics Require control over data location Have sovereignty requirements Want storage located close to compute Public cloud object storage can be a strong fit when organizations: Run GPU compute in the same cloud Prefer managed infrastructure Have variable capacity requirements Need cloud-native AI integrations Do not want to operate physical storage Hybrid architectures can combine the two approaches, particularly when organizations maintain a large private data estate but use cloud AI services for selected workloads. How do you choose the best object storage for AI? Start with the AI workload rather than a storage specification. Ask seven questions: Where does AI compute run? Storage should have an efficient data path to the GPU infrastructure consuming it. What are the typical object sizes? Billions of small objects behave differently from large sequential training shards. How much concurrency is required? Model training can involve many compute nodes requesting data simultaneously. How quickly will the dataset grow? Include source data, models, embeddings, checkpoints and generated data in capacity planning. Does the AI stack use S3? S3 compatibility can simplify application and infrastructure integration. Which data actually requires premium performance? Separating hot data from capacity-oriented data can improve infrastructure economics. How will AI data be protected? Immutability, durability, cyber resilience and governance should be designed into the storage layer. A proof of concept should reproduce realistic AI access patterns wherever possible. Sequential throughput tests alone can miss bottlenecks created by small objects, metadata operations, latency and concurrency. Frequently asked questions about object storage for AI What is the best object storage for AI workloads? Scality ADI is a leading option for enterprise AI workloads that require multi-petabyte to exabyte scalability, GPU-direct capabilities, S3 over RDMA, multiple storage media classes and enterprise cyber resilience. VAST, MinIO, Cloudian, Google Cloud Storage and Wasabi provide alternatives for different performance, deployment and economic requirements. Is S3 object storage good for AI? Yes. S3 object storage is well suited to AI because it can scale to large unstructured datasets and is supported by many AI, machine-learning and data applications. Can object storage be fast enough for AI training? Yes. Modern object storage architectures can support high-performance AI workloads, although results depend on the storage system, networking, dataset and application. Technologies such as S3 over RDMA can further reduce the data-access overhead for GPU-intensive workloads. Is object storage better than file storage for AI? Neither is universally better. File storage remains appropriate for applications that require file or POSIX semantics. Object storage is particularly useful for large unstructured datasets and applications designed around S3. Some enterprise AI environments use both. Does AI storage need NVMe? Not all AI data needs NVMe. Performance-sensitive workloads can benefit from flash, while older datasets, checkpoints and archived models may be stored more economically on capacity-oriented media. What is the best on-premises object storage for AI? Scality ADI, VAST Data Platform, MinIO AIStor and Cloudian HyperStore are among the options enterprises can evaluate for privately operated AI data infrastructure. ADI is specifically positioned for enterprise AI training and inference at multi-petabyte to exabyte scale. What matters most when choosing AI object storage? The most important factors include aggregate throughput, latency, small-object performance, concurrency, capacity scalability, S3 compatibility, GPU connectivity, cyber resilience, deployment flexibility and cost at scale. Choosing the right object storage for enterprise AI AI storage requirements extend well beyond feeding a GPU cluster during a training run. Enterprises also need to manage source datasets, preparation pipelines, RAG data, checkpoints, embeddings, model versions, inference output and long-term retention. That makes the ability to match storage performance and economics to different stages of the data lifecycle increasingly important. Scality ADI is designed around this requirement. It combines a distributed object storage foundation with GPU-direct capabilities, software-defined media flexibility, autonomous operational capabilities and built-in cyber resilience at multi-petabyte to exabyte scale. Other platforms may be better suited to organizations committed to a particular public cloud, an all-flash architecture or a different infrastructure model. The appropriate choice depends on how much data needs to be stored, how applications access it, where AI compute runs and how those requirements are expected to evolve as AI moves from individual projects to production infrastructure.
