Computational Storage Market to Hit $4.3B by 2032
The computational storage market is projected to grow from $0.93 billion in 2026 to $4.30 billion by 2032, at a compound annual growth rate of 29.0%. This growth reflects increasing demand for processing data closer to where it is stored, a key architectural pattern for edge computing and efficient AI model deployment. The technology reduces data movement, which can lower latency and improve overall system performance.
- The architecture of computational storage integrates processors like multicore CPUs or FPGAs directly into storage devices, allowing for in-place data processing which reduces data movement and can improve security by keeping sensitive data on-device. - Key market players driving innovation include Intel, AMD, and Samsung, which collectively hold an estimated 40-64% of the market share. Other significant companies include ScaleFlux, Marvell, and the startup Pliops. - North America accounted for the largest market share in 2025 at 41.0%, driven by the high concentration of hyperscale data centers and the presence of major cloud service providers. - The technology is classified into devices like Computational Storage Drives (CSDs), which combine compute and persistent storage, and Computational Storage Processors (CSPs), which provide compute services without storing data themselves. - Real-world applications are emerging in IoT for automotive and aerospace to process large amounts of sensor data, and in healthcare for real-time analysis of medical imaging and patient data. - Venture capital investment in AI-related infrastructure is surging, with global VC funding for AI startups reaching $131.5 billion in 2024, representing a third of all venture investments that year. - Startups in the broader data infrastructure space are attracting significant funding; for example, data platform VAST Data raised $118 million in late 2023, nearly tripling its valuation to $9.1 billion with investors including Nvidia and Dell Technologies Capital. - This trend contrasts with traditional storage architectures where data must constantly move between the storage device and a central CPU for processing, creating latency and I/O bottlenecks.
