| Study Period | 2022 - 2033 |
|---|---|
| Market Size in 2026 | USD 1.24 Billion |
| Market Size in 2027 | USD 2.37 Billion |
| Market Size by 2033 | USD 6.83 Billion |
| Projected CAGR | 21.85% |
| Largest Region | North America |
| Fastest Growing Region | Middle East and Africa |
| Market Structure | Moderately Fragmented |
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The global computational storage market size is estimated at USD 1.24 Billion in 2026 and is projected to reach USD 2.37 Billion in 2027. Market revenue is expected to climb to USD 6.83 Billion by 2033, advancing at a CAGR of 21.85% during 2027-2033. Hyperscale cloud operators, AI infrastructure builders, and database-heavy enterprises are paying for drives and accelerator cards that compress, filter, and encrypt data where it is stored, because every host processor core freed from that work can return to billable workloads.
Power limits at AI data centers are turning storage-side processing into a budgeted purchase for cloud and colocation operators. According to the International Energy Agency, global data center electricity demand rose about 17% in 2025 to 485 TWh, and the agency expects consumption to reach about 950 TWh by 2030. Where grid connections cap the number of servers an operator can energize, moving compression and query filtering onto the drive raises useful work per watt. Operators now judge storage on output per rack as well as cost per terabyte, which favors vendors able to prove savings on customer workloads.
North America accounts for 41.0% of 2026 revenue, led by the U.S., where hyperscale campuses buy computational drives and offload cards in fleet volumes and enterprises use them to speed database and analytics work. The Middle East & Africa is projected to grow fastest, at a 24.5% CAGR during 2027-2033, as Saudi Arabia and the U.A.E. build sovereign AI data centers whose storage tiers are chosen at the design stage. Computational storage market demand in North America therefore comes largely from refresh cycles, while Gulf orders tend to arrive as large, project-based specifications.
Cloud operators that held back from computational drives because each vendor needed its own driver and toolchain now have a common way to load and run code on the device. Once a drive exposes compute through the same host interface it uses for reads and writes, an operator can qualify one software path and buy hardware from several vendors against it. That change could move purchasing from single-supplier pilots toward multi-vendor framework contracts.
NVM Express lists a Computational Programs Command Set and a Subsystem Local Memory Command Set, both at revision 1.0, among the eleven specifications that made up the NVMe family as of August 2024. The first set lets a host execute programs on the device, and the second gives those programs memory inside the drive. Drive and controller vendors that implement both sets could see shorter qualification cycles at large buyers, while proprietary offload cards are likely to face closer price comparison as standards-based alternatives reach the market.
Database and AI operators pay for server processor time that is spent decompressing, scanning, and discarding data before any useful query runs. Moving those filter and compression steps into the drive returns host cores to revenue-earning work and cuts the volume of data crossing the PCIe bus. For a cloud provider renting compute by the hour, each freed core can be resold, so drive-level offload becomes a measurable cost line in server design instead of a niche performance option.
Samsung's own testing of its second-generation SmartSSD, announced in July 2022, found that scan-heavy database queries ran in less than half the time, with energy use cut by up to 70% and processor utilization by up to 97%, compared with conventional data center SSDs. Results of that kind come from vendor benchmarks on favorable workloads, so operators typically rerun them on their own query mix before committing to volume orders. Suppliers that publish reproducible test kits and support open benchmarking are likely to move through that evaluation stage faster.
Storage arrays, backup appliances, and drive firmware rely on public-key cryptography for key exchange, firmware signing, and secure boot, and those functions are expected to move to post-quantum algorithms. The new schemes use larger keys and signatures, and some need more computation per operation, which adds load to host processors already busy with storage services. Storage processors that run these operations in hardware give array makers a way to adopt the new algorithms without adding server cores, creating a second purchase reason beyond compression.
NIST published the initial public draft of its report on the transition to post-quantum cryptography standards, NIST IR 8547, on November 12, 2024, identifying the quantum-vulnerable standards that information technology products and services will need to move away from. Eideticom already promotes a Silicom crypto adapter running its NoLoad engine as ready for post-quantum cryptography, so the offload function exists in commercial form. The opening is widest among federal agencies, defense contractors, and banks, where cryptographic transition plans tend to be audited and funded.
Enterprises without large in-house engineering teams find it hard to justify in-storage processing when each offload function must be wired into their database, file system, or AI pipeline. A hyperscaler can spread that porting cost across thousands of servers, but a mid-sized bank or hospital IT department pays it on a few racks, so the payback period stretches. Many enterprise IT teams therefore stay with standard NVMe drives and host-based software compression, which keeps a large part of the server base outside the market.
The common software interface arrived late. SNIA released version 1.0 of its Computational Storage API in October 2023, more than a year after it published version 1.0 of its architecture and programming model, so independent software vendors have had a short window to build support. Drives that compress data transparently, with no change to host software, avoid this barrier, which lowers the adoption hurdle for enterprise IT teams. Programmable offload is likely to stay concentrated among operators with their own software teams until database and storage software vendors ship native support.
Hardware accounts for 68.5% of computational storage market revenue in 2026, because operators buy the capability as a physical device, whether a drive, an add-in card, or an array with compute built in. Only units with on-board compute are counted, so standard SSDs installed alongside them fall outside this category. The processing silicon is priced into each unit, so a deployment carries a premium over a standard SSD of the same capacity. Hyperscale buyers typically qualify computational drives through their usual server-validation process, which keeps spending flowing through hardware purchase orders.
Software & firmware is set to post the fastest growth, at a 31.4% CAGR during 2027-2033, as value shifts toward the code that runs on the device. Arm states that its Cortex-R82 processor supports rich operating systems such as Linux and simplifies computational storage development, which lets drive makers offer on-drive runtimes, containers, and application libraries. Those layers can be licensed or sold as subscriptions on top of hardware already installed, giving vendors recurring revenue from each drive across its service life.
Fixed-function architecture holds 63.5% of 2026 revenue, because hardwired compression, encryption, and erasure coding deliver savings without changes to applications. As per ScaleFlux, the compression features of its CSD 3000 drives require zero application changes. Buyers can drop such drives into existing server fleets, measure the capacity and endurance gain, and expand orders without retraining developers, which shortens procurement cycles in both enterprise and cloud accounts.
Programmable architecture is forecast to rise at a 28.6% CAGR during 2027-2033, driven by workloads where the offload logic changes over time. AI retrieval, vector search, and database query pushdown need filters that are rewritten as models and schemas change, which hardwired engines handle poorly. FPGA- and processor-based devices let operators update that logic in the field, so the same hardware stays useful across several software generations. Hyperscalers with in-house engineering teams can recover the higher unit price through workload-specific gains.
PCIe / NVMe accounts for 64.0% of 2026 revenue, since most enterprise servers shipped recently already provide NVMe drive bays and PCIe slots. Computational drives in U.2, E1.S, and E3.S form factors fit those bays directly, so buyers add compute to storage without redesigning servers or changing cabling. The installed base of NVMe hosts and operating system drivers also lowers qualification effort, which keeps this interface the default for new deployments of compute-enabled drives.
Compute Express Link (CXL) should see the strongest expansion, at a 34.2% CAGR during 2027-2033, as AI clusters seek memory-semantic access to data held near the processor. The CXL Consortium released the CXL 4.0 specification on November 18, 2025, doubling bandwidth to 128 GT/s from 64 GT/s while keeping backward compatibility with earlier versions. Faster links make near-memory computing devices more practical for AI inference, and suppliers with CXL controller and memory expertise are better positioned as server platforms adopt newer link generations.
AI & machine learning acceleration accounts for 32.5% of 2026 revenue, the largest share, because AI clusters move very large volumes of data per server. Training pipelines read datasets repeatedly, and inference systems need fast access to model context, which makes pre-filtering and caching near the storage tier worth paying for. GPU time is the most expensive resource in these clusters, so a device that keeps accelerators supplied with data can pay back quickly.
The same category is also projected to grow fastest, at a 29.5% CAGR during 2027-2033, as large language model inference and retrieval-augmented generation push more AI working data from memory onto flash. KIOXIA's AiSAQ software, issued in a new open-source version in July 2025, runs vector searches directly on SSDs and reduces host memory requirements. Once vector indexes and cached model context sit on drives, processing inside those drives can cut traffic back to GPU servers, so storage-side caching and filtering are likely to become standard line items in inference cluster designs.
Cloud service providers & hyperscale data centers account for 42.0% of 2026 revenue, because they operate at the scale where offload savings justify custom qualification. A hyperscaler can test a computational drive against its own database or AI services and then deploy it across tens of thousands of servers, multiplying the per-server gain. These operators also design their own server and storage platforms, so they can write compute features directly into drive orders placed with NAND flash and controller suppliers.
Automotive & autonomous systems is set to expand fastest, at a 27.8% CAGR during 2027-2033, from a smaller base. Development fleets and driver-assistance programs record camera, radar, and lidar data continuously, and in-vehicle drives that filter or compress that data before upload reduce the cost of moving and storing it in the cloud. Automotive-qualified products take longer to design in, so suppliers that win early vehicle programs tend to keep the business over several model years.
The following segments are analysed in this report.
North America holds 41.0% of global revenue in 2026, the largest regional position, because the operators that buy offload drives and cards at fleet scale are concentrated there. U.S. hyperscale cloud providers design their own servers and qualify drives directly with suppliers, and many of the companies selling computational drives, controllers, and accelerators are based in California. The U.S. is the region's largest market, supported by AI data center construction and by financial and healthcare organizations running large analytics workloads.
Electricity supply shapes how that spending develops. The U.S. Department of Energy, drawing on a Lawrence Berkeley National Laboratory study, estimates that data centers used about 4.4% of total U.S. electricity in 2023 and could use 6.7% to 12% by 2028. Where new grid connections are slow to obtain, operators look to raise output from capacity already connected, and offload devices that cut processor work per query serve that goal. North American demand is therefore likely to remain refresh-led and efficiency-driven, with Canada adding a smaller base of cloud and enterprise buyers.
The Middle East & Africa is set to expand fastest, at a 24.5% CAGR during 2027-2033, as Gulf governments fund national AI computing capacity from the ground up. In Saudi Arabia, the Public Investment Fund launched HUMAIN in May 2025 to consolidate the country's data center initiatives and buy the hardware they need, the fund states. New AI campuses there and in the U.A.E. select their storage architecture at the design stage, so offload-capable storage can be specified into the first build. The U.A.E. stands out as the region's fastest-growing market, with Israel and South Africa providing established enterprise demand.
OpenAI announced in May 2025 that a 1 GW Stargate UAE cluster would be built in Abu Dhabi, with the first 200 MW expected to go live in 2026. Clusters of that size need high-throughput storage close to the GPUs from the first day of operation, and greenfield builds let operators choose offload-capable drives and data processing units without replacing installed equipment. Suppliers that sell through the U.S. cloud providers and integrators building these sites have a route in, although export-licensing conditions for advanced chips could shift project timelines.
Asia-Pacific draws on two sources of demand at once, a memory and drive manufacturing base and a fast-expanding group of cloud and AI users. South Korea and Japan host leading NAND flash producers whose drives form the hardware base for in-storage processing, and their engineering teams work closely with regional server builders. China, India, and Southeast Asian markets add the user side, as cloud providers and public programs expand AI computing capacity that needs fast data pipelines.
India shows the pace of public investment. The Press Information Bureau reported on February 17, 2026 that the government would expand IndiaAI compute capacity beyond the existing 38,000 GPUs, with 20,000 more to be added in the coming weeks. That figure sizes the GPU base, not purchases of offload storage, but shared GPU clusters of that scale need fast storage, which creates openings for offload drives and cards as these facilities grow. Suppliers with local support teams are likely to compete well in such publicly funded tenders.
Europe trails North America in scale but faces closer regulatory attention to data center energy use, which strengthens the case for offload hardware. Operators in Germany, the Netherlands, and the U.K. run colocation and cloud campuses where electricity is costly and grid capacity is limited, so efficiency gains translate directly into lower operating costs. Banks, automotive engineering groups, and research institutes also keep analytics on premises, which holds part of the region's demand outside hyperscale accounts.
Under Commission Delegated Regulation (EU) 2024/1364, operators of data centers with an installed IT power demand of at least 500 kW must report energy and sustainability indicators to a European database. The rule requires disclosure and does not mandate any particular storage technology, but published indicators give operators a reason to show lower energy per unit of work. Storage suppliers that document power savings in a form operators can feed into these reports could gain an advantage in European tenders.
Latin America accounts for a smaller part of global demand, with purchases concentrated among cloud regions, telecom operators, and large banks in Brazil, Mexico, and Chile. Much of the new capacity is colocation built for international cloud providers, so in-storage processing enters the region largely through hardware specifications those providers set elsewhere. Local enterprises tend to adopt later, once offload features ship as standard in the arrays and servers they already buy.
Tax policy is now working in favor of data center investment in Brazil. According to the Chamber of Deputies, Lei 15.504/2026, signed on September 15, 2026, created the REDATA regime, which suspends federal taxes on equipment and components bought for qualifying data centers that use renewable or low-emission power and direct at least 10% of their processing and storage capacity to the domestic market. Lower equipment taxes reduce the landed cost of servers and drives, which could favor storage suppliers with local channel partners once the regime's implementing rules are in place.
The following geographies are analysed in this report.
Competition in the computational storage market is moderately fragmented, with no supplier leading across every layer. NAND flash supply is held by a small group of memory makers, which gives drive vendors with their own flash an advantage in cost and supply security, while the controller, accelerator, and software layers remain open to specialists. Switching costs come mainly from qualification, because an operator that has validated a drive, its firmware, and its software path is slow to repeat that work. The structure is evolving, as open command sets and programming interfaces lower the cost of adding a second source.
Suppliers fall into three groups that compete in different ways. Memory and drive makers such as Samsung Electronics, SK hynix, and KIOXIA compete on flash cost, capacity, and the ability to build compute into drives that buyers already qualify. Specialists including ScaleFlux and Eideticom compete on specific offload functions, such as transparent compression and cryptographic processing, and often reach customers through server and storage OEMs. Silicon platform providers, including NVIDIA, Marvell Technology, AMD, and Arm, supply the processors, controllers, and data processing units on which much of the market's hardware is built.
For buyers, this layering puts the strongest negotiating position with hyperscale operators that can qualify several drive and accelerator sources against a common software interface. Smaller enterprises usually buy computational features inside arrays and servers from established system vendors, which gives those vendors a route to package offload capability into existing contracts. New entrants face a long qualification cycle at each large account, and the specialists' main exposure is that memory makers and platform providers can fold comparable functions into their own drives and processors.
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