Top 10 AI Storage Solutions and Companies to Watch in 2026 in UK, US, Canada, Europe, Ireland, India, China, Taiwan and Israel

The exponential growth of artificial intelligence workloads is fundamentally reshaping enterprise storage requirements. Training large language models, processing petabyte-scale datasets, and running real-time inference engines demand storage architectures capable of delivering unprecedented throughput, minimal latency, and intelligent data management. This analysis examines the top 10 AI storage solutions and companies positioned to dominate the market across UK, US, Canada, Europe, Ireland, India, China, Taiwan, and Israel in 2026. 1. NVIDIA DGX Storage (United States) NVIDIA continues dominating AI infrastructure with its DGX SuperPOD storage architecture. The company's partnership with enterprise storage vendors created purpose-built solutions delivering 2.5 terabytes per second aggregate throughput for GPU clusters. DGX Storage integrates with NVIDIA Base Command for unified AI workflow orchestration, supporting seamless data movement between training, fine-tuning, and inference stages. NVIDIA's Magnum IO software stack optimises storage I/O patterns specifically for AI workloads, achieving 40% performance improvements over generic parallel file systems. The company secured USD 4.2 billion in enterprise AI infrastructure contracts during 2025, with major deployments at Microsoft Azure, Google Cloud, and Amazon Web Services reinforcing its market leadership position. 2. Pure Storage FlashBlade//E (United States) Pure Storage launched FlashBlade//E specifically targeting AI and machine learning workloads at scale. The platform delivers 75 gigabytes per second throughput per chassis with sub-250 microsecond latency, enabling researchers to iterate on models 3x faster than previous generation systems. Pure's Evergreen subscription model eliminates forklift upgrades, providing continuous performance improvements through software updates. The company's AIRI (AI-Ready Infrastructure) reference architecture, developed jointly with NVIDIA, powers AI deployments at 340 enterprises globally. Pure Storage reported 67% year-over-year growth in AI-related storage revenue, with particularly strong adoption in pharmaceutical research and autonomous vehicle development sectors. 3. NetApp ONTAP AI (United States) NetApp expanded its ONTAP AI portfolio with enhanced data fabric capabilities spanning on-premises, edge, and multi-cloud environments. The platform's FlexGroup technology aggregates up to 20 petabytes in a single namespace, eliminating data silos that traditionally impede AI workflow efficiency. NetApp's BlueXP management plane provides unified visibility across hybrid deployments, enabling seamless data tiering based on access patterns and cost optimisation targets. NetApp's Spot by NetApp acquisition proved transformative, delivering AI-driven workload placement that reduced cloud storage costs by 62% for participating enterprises. The company processed 890 petabytes of AI training data through its platforms during 2025, with financial services and healthcare sectors representing the largest customer segments. 4. Dell PowerScale (United States) Dell Technologies positioned PowerScale as the industry's most scalable file storage for AI workloads. The platform supports up to 93 petabytes in a single cluster with linear performance scaling, delivering 945 gigabytes per second aggregate throughput. Dell's integration with NVIDIA DGX systems and AMD Instinct accelerators provides validated reference architectures reducing deployment complexity. PowerScale OneFS 9.7 introduced AI-aware data placement, automatically distributing hot data across NVMe tiers while migrating cold datasets to cost-effective capacity storage. Dell reported PowerScale AI deployments growing 89% year-over-year, with European telecommunications and Asian manufacturing sectors driving significant adoption. 5. Weka Data Platform (Israel/United States) Weka emerged as the leading pure-play AI storage specialist, with its software-defined platform consistently outperforming traditional storage arrays in GPU utilisation benchmarks. The company's parallel file system delivers 100 gigabytes per second from a single namespace while maintaining sub-100 microsecond latencies essential for real-time inference workloads. Weka's cloud-native architecture spans AWS, Azure, Google Cloud, and Oracle Cloud with consistent performance characteristics. Founded in Tel Aviv with headquarters now in San Jose, Weka secured USD 275 million in Series E funding at a USD 1.6 billion valuation. The company counts 12 of the world's 15 largest AI research laboratories as customers, including deployments supporting GPT-5 training infrastructure and autonomous driving simulation platforms. 6. IBM Storage Scale (United States/Ireland) IBM leverages its deep enterprise relationships to deploy Storage Scale (formerly GPFS) across the world's largest AI research initiatives. The platform supports exabyte-scale deployments with policy-based data management automating lifecycle operations. IBM's Dublin research centre contributed algorithmic innovations reducing checkpoint overhead by 45% for distributed training workloads. IBM's watsonx.data integration enables seamless connectivity between Storage Scale and the company's enterprise AI platform, simplifying data preparation pipelines. The company secured contracts with the UK's National Health Service and the European Centre for Medium-Range Weather Forecasts, demonstrating Storage Scale's versatility across AI application domains. 7. DDN AI400X2 (United States/Europe) DDN (DataDirect Networks) specialises in high-performance storage for the most demanding AI workloads. The AI400X2 platform delivers 180 gigabytes per second sustained throughput with 75 million IOPS, purpose-built for training trillion-parameter models. DDN's Lustre-based architecture provides POSIX compliance essential for legacy research codebases while delivering parallel file system performance. DDN powers 8 of the world's top 10 supercomputers and maintains significant presence in European HPC centres including CINECA in Italy and Jülich Supercomputing Centre in Germany. The company expanded manufacturing operations to Ireland, serving EMEA customers with reduced latency and enhanced support coverage. 8. Huawei OceanStor Dorado (China) Huawei positioned OceanStor Dorado as China's leading enterprise AI storage platform, achieving 21 million IOPS with 50-microsecond latency. The platform's SmartMatrix architecture provides consistent performance under mixed workload conditions, essential for enterprises running multiple AI projects simultaneously. Huawei's integration with its Ascend AI processors creates end-to-end Chinese-origin AI infrastructure stacks. Despite geopolitical constraints limiting western market access, Huawei achieved dominant market share across Chinese technology giants, telecommunications operators, and government research institutions. The company invested CNY 8.7 billion in storage R&D during 2025, focusing on next-generation computational storage architectures. 9. Infinidat InfiniBox SSA II (Israel) Infinidat delivers enterprise-grade AI storage with industry-leading reliability guarantees. The InfiniBox SSA II platform provides 100% availability SLAs with autonomous self-healing capabilities, critical for production AI inference systems where downtime translates directly to revenue loss. The company's Neural Cache technology learns access patterns, delivering 12 million IOPS with predictable sub-35 microsecond latency. Headquartered in Herzliya with R&D centres across Israel, Infinidat grew enterprise AI deployments 156% year-over-year. Major wins include European banking consortiums deploying fraud detection systems and Asian telecommunications operators running network optimisation AI at scale. 10. VAST Data Platform (Israel/United States) VAST Data pioneered disaggregated shared everything architecture, eliminating traditional storage controller bottlenecks. The VAST Data Platform delivers 50 petabytes in a single namespace with linear scaling to exabyte capacities. The company's DASE (Disaggregated Shared Everything) architecture provides simultaneous file, object, and database protocol access from unified storage pools. Founded in Tel Aviv with operations expanding globally, VAST Data achieved unicorn status with USD 118 million Series D funding. The platform powers AI initiatives at leading technology companies, pharmaceutical enterprises, and autonomous vehicle developers, with notable deployments at Meta AI Research and Waymo. Regional Market Analysis United Kingdom: The UK AI storage market grew 78% in 2025, driven by investments from hyperscale cloud providers establishing London region AI infrastructure. DSIT announced GBP 900 million in AI compute infrastructure funding, with storage representing approximately 35% of total investment. Major deployments include the UK Biobank genomics AI initiative and Financial Conduct Authority regulatory AI systems. Canada: Canadian AI storage adoption accelerated through the Vector Institute and Mila partnerships with enterprise storage vendors. Toronto emerged as North America's third-largest AI infrastructure market, with Pure Storage and NetApp establishing dedicated Canadian support operations. The Canadian government's CAD 2.4 billion AI strategy includes significant storage infrastructure allocations. Europe: European enterprises prioritised data sovereignty-compliant AI storage solutions following GDPR enforcement actions. German automotive manufacturers invested EUR 2.1 billion in on-premises AI infrastructure, avoiding public cloud data residency concerns. DDN and IBM Storage Scale captured significant market share through European manufacturing and support commitments. Ireland: Ireland's position as Europe's technology hub attracted AI storage vendor investments, with Pure Storage, NetApp, and DDN establishing Dublin operations centres. Irish pharmaceutical and technology sectors deployed petabyte-scale AI storage for drug discovery and software development automation. India: Indian enterprises adopted hybrid cloud AI storage architectures, balancing performance requirements with cost optimisation. Tata Consultancy Services and Infosys partnered with global storage vendors to deliver managed AI infrastructure services. The Indian government's INR 10,372 crore AI mission includes substantial storage infrastructure procurement. China: Chinese AI storage market reached USD 8.9 billion, with Huawei, Inspur, and H3C capturing 78% market share. Domestic vendors benefited from technology localisation policies requiring Chinese-origin infrastructure for government and state-owned enterprise deployments. Alibaba Cloud and Tencent Cloud deployed exabyte-scale AI storage infrastructure supporting domestic AI development. Taiwan: Taiwanese semiconductor manufacturers required specialised AI storage for chip design automation and yield optimisation. TSMC deployed multi-petabyte AI infrastructure supporting next-generation process node development. Pure Storage and Dell established Taiwan engineering centres serving semiconductor industry requirements. Israel: Israel's AI storage innovation ecosystem produced three global leaders: Weka, Infinidat, and VAST Data. Tel Aviv attracted USD 890 million in storage technology venture investment during 2025. Israeli vendors differentiated through software-defined architectures optimised for AI workload characteristics rather than traditional enterprise storage patterns. Technology Trends Shaping AI Storage Computational storage emerged as a significant trend, with storage devices incorporating processing capabilities to filter and transform data before transfer to GPU memory. This approach reduces data movement bottlenecks, improving effective throughput by 40-60% for suitable workloads. Samsung, Micron, and emerging startups demonstrated CXL-attached computational storage prototypes targeting 2026 production availability. Intelligent data tiering evolved beyond simple access frequency metrics, incorporating workload-aware algorithms that predict data requirements based on training job characteristics. Vendors including Pure Storage and VAST Data integrated machine learning models analysing checkpoint patterns, enabling pre-staging of data likely required for upcoming training iterations. Sustainability considerations influenced enterprise storage decisions, with power efficiency becoming a primary evaluation criterion alongside performance metrics. Flash-based platforms offering 80% power reduction compared to spinning disk alternatives gained preference, particularly among European enterprises subject to carbon reporting requirements.