Huawei and the new enterprise storage market

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Huawei and the new enterprise storage market
AI is reshaping the enterprise storage market, putting a premium on very fast, efficient storage that delivers data for AI processing while ensuring cyber-resilience, minimizing data center space take-up and lowering energy consumption.
Organizations are adopting generative AI large language models with their natural language interfaces, and also agents which can reason, talk to one another, and initiate actions, becoming quasi-digital employees. Both models and agents need very high-speed data access to trusted data that is protected against malware and system outages.
According to IDC, “The enterprise storage market, which spent much of the prior two years in the shadow of explosive server and GPU investment, is now benefiting from two reinforcing tailwinds: enterprises refreshing storage infrastructure that was deferred while AI server spending took priority, and a new wave of AI-driven storage demand emerging from training pipelines, inferencing workloads, and unstructured data activation use cases.”
IDC notes “Component supply constraints, particularly for NAND flash and DRAM, continue to push system-level prices upward, further amplifying revenue growth beyond what shipment volumes alone would suggest. Thus, the market is expected to continue in a positive trajectory of 7.3 percent compound average growth through 2030.”
Huawei is the number 2 storage vendor worldwide, according to IDC. Huawei has developed its OceanStor Dorado all-flash block, unified file, and object storage, and its OceanStor Pacific scale-out file, object and big data storage arrays, into substantial enterprise storage systems.
It is a serious player. For example, Norway’s National Library is developing a large language model (LLM) that understands the Norwegian language. The process involves in-house computation and over 2 petabytes of flash capacity spread across multiple OceanStor Dorado all-flash arrays. This is low-latency storage for the data pipelines and training preparation.
Ruijin Hospital in Shanghai uses an OceanStor Pacific system as a scale-out storage platform for a digital pathology data lake for the retention of more than 1 million scanned slides a year. The hospital’s multimodal RuiPath AI pathology model uses this data with Huawei’s ModelEngine toolchain for data prep, training, and inference.
Gartner’s August 2026 Enterprise Storage Magic Quadrant positions Huawei as a top-3 player in the enterprise storage market. Huawei has been included in Gartner’s Enterprise Storage Magic Quadrant for more than a decade. The research firm says Huawei’s OceanStor Dorado All-Flash Storage, OceanStor Pacific Storage, and AI Data Platform products broadly focus on mission-critical, OLTP, high-performance unstructured data, and enterprise AI workloads. Also, Huawei continues to invest heavily in AI-native storage architectures featuring UnifiedBus networks, Context Memory Storage, and vertically integrated, high-density SSD hardware to mitigate supply chain volatility.
The analysts preparing this report make several strategic assumptions:
• By 2029, consumption-based storage as a service (STaaS) will replace 50 percent of on-premises enterprise storage and data services infrastructure capital expenditure (capex), an increase from 25 percent in early 2026.
• By 2029, over 80 percent of on-premises unstructured data will be deployed on a consolidated storage platform instead of separate file and object products, up from 50 percent in 2025
• By 2029, 70 percent of organizations will deploy purpose-built AI storage tiers specifically for large-scale AI inference and retrieval-augmented generation (RAG), up from less than 10 percent in 2026.
• By 2029, 100 percent of storage products will include cyberstorage capabilities focused on active defense beyond recovery from cyber events, up from 25% in early 2026.
• For 2027, heads of I&O must budget for 250 to 300 percent of their 2025 storage spend to accommodate surging memory and storage prices, driven up by surging demand for AI server and storage systems.
This latter point echoes the IDC comments and reinforces the need for data storage efficiency to minimize expensive NAND usage.
Gartner’s analysts say “The enterprise storage platforms market is undergoing a structural transformation unlike any before, driven by four major market forces: extreme supply chain volatility and costs, the transition to production-scale artificial intelligence, hypervisor replatforming, and cyber-data-resilient architectures.” The old split between monolithic high-end arrays and dual-controller mid-range and low-end arrays is no longer relevant. Now customers are looking for scale-out storage with fast access NVMe SSD storage for both mission-critical block data and AI processing needing large amounts of unstructured data. Disaggregated compute and storage architectures are being adopted as they allow compute and storage to be scaled independently. Disk-based systems are now being used for nearline or warm data storage.
Deciding which vendors to include in a review of suppliers and products for particular workloads has become more complex as a result.
Magic Quadrant reports provide a fairly detailed snapshot of different vendors’ positioning in a market segment: a market map. It shows which vendors look viable and well positioned in a market and so it’s a starting point for an organization looking to purchase IT equipment.
A companion Critical Capabilities document from Gartner examines the same market sector in a deeper product dive, enabling a Magic Quadrant vendor shortlist to be analyzed further to see if products are suited to RFP requirements. Organizations can use the Quadrant to shortlist vendors they can use as a partner, and the Critical Capabilities to see how a vendor’s product actually rates on the workload scenarios it cares about.
The 2026 Enterprise Storage Critical Capabilities edition evaluated the eight enterprise storage suppliers in the Magic Quadrant across 10 critical capabilities and seven use cases, with the critical capabilities being:
• Performance and scalability
• Resiliency and efficiency
• Ecosystem
• Hybrid IT operations management
• Public cloud integration
• Object storage
• Cyber and data resilience
• Virtualization and containers
• Database integration
• AI application integration
The seven use cases are:
• On-line transaction processing (OLTP)
• Virtualization and container management
• High-performance file
• Object-native applications
• Hybrid platform services
• Hybrid cloud storage
• AI
Gartner’s analysts apply the ten critical capabilities to the seven use cases, giving each critical capability a weighting, and then calculate a score for the individual suppliers in each use case.
Huawei led in three of the use cases, more than any other vendor: OLTP, High-Performance File, and AI application integration.
A radar diagram shows the overall picture:
The Gartner analysts note that vendors are integrating cyber resilience, disaggregated scale-out architectures and native AI data services across their enterprise storage platforms. They are “tuning their storage platforms for high performance file processing and demanding AI training or inference pipelines,” and “increasingly embedding native, hardware-accelerated or AI-driven ransomware detection directly into their storage operating systems, which enables faster threat detection.”
They say “Huawei offers the OceanStor Dorado series for all-flash block and unified file storage, and the OceanStor Pacific series for scale-out file, object and big data workloads. … Data Management Engine (DME) IQ, a centralized AIOps engine, provides automated fault diagnosis, capacity forecasting and closed-loop life cycle management across physical and hybrid cloud storage environments and globally manages and orchestrates Huawei’s storage portfolio.”
The Gartner analysts tell us Huawei received its highest use-case scores for online transaction processing, high performance file and artificial intelligence, indicating its strength in transactional
database environments and demanding, large-scale unstructured data environments, now increasingly being used for AI inferencing workloads.
The integration of the native shared-storage engine over the OceanStor block layer enables database instance failures to recover within seconds, while OceanStor Dorado NAS provides an optimized NFS+ client for database nodes to minimize operational latencies.
The amount of stored data used for AI models and agent processing could grow to eclipse other storage workloads. AI is revolutionizing storage in three ways. It requires hyper-fast data access to vast unstructured data stores. Secondly, and in the face of AI-powered cyber-attacks, it’s enabling machine speed cyber-resilience capabilities to detect attacks faster, block them and recover data. Thirdly, it’s being used to make storage system management and monitoring easier and optimize data placement.
Huawei is active in each of these three areas and eager to improve its MQ and CC positioning.
Sponsored by Huawei

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