Primary Storage in 2026: Trends, Priorities and Strategic Shifts

As we close out 2025, the storage landscape has undergone a significant transformation. Enterprises have adopted a mix of block, file and object storage architectures while integrating AI-driven solutions to optimize performance and automate management. These changes reflect a growing urgency for infrastructure that is not only scalable and intelligent but also secure enough to handle the demands of modern workloads.

Looking ahead to 2026, priorities are shifting toward delivering exceptional performance, enabling flexibility across hybrid environments, ensuring AI readiness for emerging applications, and embedding sustainability into every layer of storage strategy. This blog examines the key trends and strategic imperatives that will shape primary storage in the year ahead.

A continued shift toward Flash and NVMe

There was a time when 20K RPM drives were considered the top performance tier for spinning drives. Physics showed it just wasn't meant to be, and from that point, SSDs started their march, consuming spinning drives along the way. Even in an alternate universe where 20K drives didn't turn into barbecues, a single read/write head moving across a platter wasn't going to win thanks to its limitations. To that end, we no longer see any HDD-based block storage systems, and we haven't for some years. Similarly, high-performance file workloads, such as those used for virtualization datastores and databases, are on all-flash systems.

Up until now, flash drives were either single-level cell (SLC), multi-level cell (MLC, or two bits per cell), or triple-level cell (TLC) with, you guessed it, three bits per cell. A couple of years ago, quad-level cell (QLC) drives started to appear in storage systems. They maintained the excellent random read/write performance of flash drives and drove down the cost-per-gigabyte. These drives are designed for workloads with a two-millisecond response time, reserving TLC for the most demanding needs.

QLC is starting to show up in volume and is peeling off the workloads that previously ran on 10K and 7200 RPM drives, but mostly the latter since the former is generally on flash by now. QLC-based systems are a great fit for general-purpose user and departmental shares on NAS and density block workloads. These QLC drives are available in sizes of 30, 60 or 120TB, with 240TB in sight, making them significantly denser and thus more power-efficient than spinning drives, which currently top out at 30TB.

Spinning drives still make sense where rack density and performance are not important. Flash's cost profile improves with compression and deduplication, so for workloads that don't reduce, like security camera footage, spinning drives work well. Furthermore, sequential workloads like video recording cooperate with spinners since the head doesn't have to thrash about.

Communication to the storage system and drives happens over a protocol, similar to a language like French or Spanish. In the enterprise space, that protocol has been SCSI for a loooong time (1986 officially, but development started in 1979). With the rise of flash drives, SCSI began to become a bottleneck in leveraging flash's capabilities. Consequently, the industry developed the NVMe (Non-Volatile Memory Express) protocol, which is simpler and reduces CPU overhead. Later, the NVMe spec was updated to allow it to run over a fabric. That fabric could be RDMA over ethernet (RoCEv2), the venerable storage-only Fibre Channel (FC-NVMe), or standard ethernet (NVMe over TCP); all can pass NVMe inside their frames.

These days, within the array, controller-to-drive communication is NVMe. However, despite NVMe-over-fabric capabilities of the storage systems, many customers are still using SCSI between the host and the array. In the case of ethernet, it's likely because of iSCSI's mature ecosystem, particularly around security and operating system supportability. However, on the host side, there is real value to be had with the simplification of the storage stack; NVMe requires fewer cycles to operate, freeing them for applications. As NVMe-over-TCP matures and as hardware refresh cycles happen, this situation will change and NVMe will become the protocol of choice.

AI-driven storage architectures

Building on the advancements in flash and NVMe, the explosive growth of AI workloads is reshaping storage architectures at their core. By 2026, storage systems will have evolved from passive capacity repositories into intelligent, programmable infrastructures optimized for high-throughput, low-latency operations. The most significant shift is the widespread adoption of disaggregated and AI-native storage models. By decoupling compute from capacity, these architectures enable independent scaling to support dynamic AI pipelines and the unpredictable I/O patterns of large-scale model training. This alignment with GPU-driven compute clusters is critical for emerging edge and sovereign cloud strategies, ensuring low latency where it matters most.

Building on this architectural foundation, organizations are rethinking their priorities around performance, resilience and efficiency. AI workloads demand massive parallelism and rapid checkpointing, making autonomous storage systems essential. These systems leverage AI for orchestration, reducing operational complexity and ensuring that hot datasets for training reside on ultra-fast media, while colder data is moved to cost-effective tiers. Concurrently, ensuring robust AI governance is critical; storage platforms now embed zero-trust security and verifiable audit logs to meet compliance standards. (We have a deeper discussion of cyber resilience strategies later in this blog.)

Perhaps most excitingly, storage is becoming an active participant in the AI pipeline. Beyond simple caching, next-generation systems are adopting computational capabilities to perform event-driven data preparation directly on the appliance. For example, when a new file is uploaded, the storage system can automatically trigger a workflow to chunk, tokenize and generate vector embeddings for Retrieval-Augmented Generation (RAG) pipelines without external server involvement. These innovations, along with GPU-direct storage integration, position storage as a strategic enabler of AI-driven business outcomes rather than a mere backend component.

For more information on disaggregated storage and its role in AI, read WWT's blog on this trend.

Object storage dominance

As AI-driven architectures continue to evolve, object storage is also experiencing a significant transformation. Object storage has, for most of its life, been relegated to the archive tier. Its limitless capacity, unbound by things like inode counts (limits on the number of files a system can track) and a hierarchical structure, makes it simple for both programmers and admins to work with using HTTP GETs and PUTs. Add its capabilities for custom metadata, and it makes sense that object storage is where the bulk of the planet's storage growth is happening. In fact, 402 million terabytes are created daily, with 80% of this amount being unstructured. Of that 80%, 225 million terabytes per day land on object storage. To cap it off, over 50% of annual object growth is on flash, and that flash is growing at a rate of almost 11% annually.

As discussed earlier in this blog, the landscape and performance requirements for storage systems are evolving — and object storage is no exception. As these systems move into tier-one applications, they must deliver significantly lower latency compared to the traditional "cheap-and-deep" solutions of the past. While storage OEMs have offered flash-based options for some time, adoption was initially limited. Today, however, we're seeing object storage capabilities integrated into traditional dual-controller unified platforms, which are generally high-performing systems.

One component of the shift to all-flash object is that applications are directly using objects for their primary storage rather than an archival tier. Updates to the S3 protocol demonstrate significant performance improvements, in addition to supporting larger files, meaning object storage is now capable of meeting the needs of tier-one applications. From there, tools like Hadoop and Spark utilize these to directly access object storage, rather than moving data in and out of higher-performance tiers first. Data movement is expensive at multiple levels in the data center stack, so ways to eliminate or minimize it are beneficial to the entire data center ecosystem.

As with all things, this doesn't mean a wholesale rejection of NAS in the future. Rather, this will be another tool in the toolbox for addressing IT needs. Objects typically operate on an eventual consistency model (where updates may take time to appear across all systems) compared to a file's strict consistency. Applications must be designed with that in mind, and not all workloads can tolerate that.

Software-defined and cloud-integrated storage

In parallel with the rise of object storage, enterprise IT is experiencing an unprecedented surge in data volumes, driven by cloud-native applications, AI workloads and real-time analytics. Traditional storage architectures, built on proprietary hardware and rigid configurations, struggle to keep pace with these demands. To overcome these limitations, organizations are increasingly adopting software-defined storage (SDS) and cloud-integrated solutions, which deliver agility, scalability and cost efficiency far beyond what legacy systems can offer.

Software-defined storage fundamentally changes how storage is managed. By decoupling storage software from the hardware layer, SDS creates a programmable, flexible platform that runs on commodity servers. This architectural shift brings several advantages.

Hardware independence eliminates vendor lock-in, enabling organizations to select the best components tailored to their specific needs.
Dynamic scalability enables seamless expansion without disruptive infrastructure overhauls.
Centralized, policy-driven management simplifies provisioning and resource allocation through automation, improving operational efficiency.
Finally, native-cloud integration supports replication and migration across on-premises and public cloud environments — including AWS, Azure and IBM Cloud — enabling true hybrid and multicloud strategies.

Building on SDS, cloud-integrated storage extends these benefits by bridging on-premises infrastructure with public or hybrid cloud resources. This integration allows organizations to burst workloads to the cloud during peak demand, ensuring consistent performance and availability. It also provides global visibility and management of distributed data assets, enhancing collaboration and governance. Cost optimization becomes easier through pay-as-you-go models and tiered storage architectures, aligning expenses with actual usage.

These innovations are not just technical upgrades — they address critical enterprise priorities:

Agility for modern applications: SDS supports both traditional and cloud-native workloads, reducing silos and enabling modern development practices.
Cost efficiency: Commodity hardware and streamlined operations lower capital costs, while cloud models offer flexible operating expenses.
Data protection and compliance: Advanced SDS features, such as ransomware detection and automated recovery, combine with cloud-native redundancy and encryption for robust security.
Support for AI and analytics: A unified global data platform accelerates innovation by eliminating data silos and enabling advanced analytics and AI workloads.

This chart highlights the key differences between traditional storage and software-defined storage (SDS) & cloud storage.

Sustainability and efficiency

As organizations continue to adopt software-defined and cloud-integrated storage solutions, a common theme that has emerged over the past several years is the increased emphasis on the sustainability and environmental impact of IT. It's no secret that data centers are resource-intensive, and the explosive growth of A.I. infrastructure has brought this consumption broadly into the public view. In fact, data center demand has outpaced the utilities' ability to provide the vast quantities of energy required to sustain this pace of growth. Many large projects are on hold simply because the energy to power them can't be provided. To remain competitive, all aspects of IT will need to find ways to operate within the resources available today.

From a storage perspective, we will likely see a two-pronged approach to address the energy crunch we're seeing:

The continued shift toward energy and cooling-efficient hardware: New generations of SSDs are providing greater density, lower power consumption and lower cooling requirements. Additionally, increasingly recyclable and eco-friendly materials are being used in the manufacture of all data center components.
Intelligent workload placement and timing powered by AI: 70-80% of enterprise data is "cold" and rarely accessed. By leveraging AI itself, it'll be possible to better predict and isolate hot workloads so that only the minimum of high-performance storage will be needed at a given time.

More broadly, we are expecting an increase in data centers powered by green and renewable energy sources. AI can be leveraged to optimize the utilization of fluctuating resources, such as wind and solar, thereby minimizing reliance on less sustainable energy sources.

Security, compliance and governance in the digital age

In today's digital-first world, security, compliance and governance (SCG) have evolved from optional best practices into critical components of enterprise IT strategy. With increasing scrutiny from regulators, heightened customer expectations and evolving cyber threats, organizations must prioritize SCG to safeguard sensitive data and build lasting trust. This strategic shift is largely driven by regulatory pressure to adhere to laws such as GDPR, CCPA and HIPAA, where non-compliance exposes organizations to substantial fines and reputational damage. Beyond compliance, robust governance frameworks are essential for mitigating risks — ranging from ransomware to insider threats — while ensuring that security objectives align with business growth and operational agility.

Implementation strategies

To achieve these goals, organizations are adopting established frameworks such as the NIST Cybersecurity Framework, ISO 27001, SOC 2, and PCI DSS. Implementation begins with regular risk assessments to identify compliance gaps and vulnerabilities, followed by the development of clear roles and security controls tailored to organizational needs. Maintenance of this posture requires continuous monitoring through automated tools for rapid threat detection, alongside comprehensive incident response planning to ensure swift recovery in the event of a breach.

Vendor-specific capabilities

Leading storage vendors have integrated these principles directly into their platforms to support enterprise SCG requirements:

NetApp aligns with major compliance frameworks, including ISO/IEC 27001, FedRAMP and GDPR, with its ONTAP platform validated under the NSA's CSfC for classified data. Their security architecture incorporates Zero Trust principles, offering features like SnapLock for compliance-grade retention and Autonomous Ransomware Protection (ARP) for inline defense. Additionally, NetApp leverages AI-driven tools for automated data discovery and classification to streamline audit readiness.
Pure Storage emphasizes a "Security by Design" philosophy, employing a secure SDLC validated against NIST SP 800-218. Their platform features always-on AES-256 encryption, role-based access control (RBAC), and SafeMode immutable snapshots to enhance resilience against ransomware. Pure centralizes policy enforcement across hybrid environments through Pure Fusion and integrates with the Rubrik Security Cloud for air-gapped backups and automated compliance reporting.
Dell Technologies utilizes a Zero Trust architecture that spans servers, storage and networking. Their approach focuses on robust supply chain security — such as firmware signing — and advanced threat detection. Dell adheres to global privacy laws and offers comprehensive governance frameworks for multicloud environments, ensuring immutable backups and automated recovery capabilities are available across the entire data estate.

Key industry trends

Across the landscape, several universal trends are shaping how SCG is delivered:

Immutable snapshots and isolated recovery: The adoption of Air-Gapped Backups and Isolated Recovery Environments (IRE) has become standard practice to combat ransomware and meet retention mandates.
AI and automation: Tools are increasingly using AI for data classification and policy automation, integrating governance directly into orchestration platforms.
Data-centric security: Solutions are shifting their focus from perimeter defense to protecting data wherever it resides (on-premises or in the cloud), combining discovery, access governance and threat detection.
Continuous auditing: Periodic checks are being replaced by ongoing validation against standards like ISO and NIST CSF to ensure real-time compliance.

Strategic planning and future-proofing

In order to align with the future of enterprise IT, we're seeing four major initiatives for 2026:

AI-ready architectures: NVMe-based tiered storage supporting parallel file systems is a requirement for AI workloads needing rapid data retrieval for training and inference. Beyond the sheer performance requirements above, storage solutions now include observability, security and orchestration features tailored to AI workloads.
Disaggregated storage: We're seeing a shift away from hyperconverged platforms to reduce vendor lock-in. Organizations are seeking storage that can scale and be purchased independently of the rest of the technology stack. It is essential to note that these architectures must offer the benefits of HCI while also providing the agility of choice and scalability. Treating storage as a commodity allows organizations to adopt a multi-vendor strategy to further increase options in the future.
Software-defined storage: As software-defined options have matured in terms of scale and performance, many organizations no longer see the need for monolithic, purpose-built storage arrays. Software-defined allows an organization to use a standardized hardware platform throughout their environment for both compute and storage. Scaling up in this model is generally as straightforward as adding another compute node with integrated storage into the existing cluster. SDS also allows for point #4.
Hybrid and multicloud: We're seeing many large organizations come to embrace a "cloud smart" model, where appropriate cloud-ready workloads are placed into public cloud but legacy and high-performance workloads remain on-premises. High-performance on-premises storage can be used for in-process AI workloads, while lower-cost cloud object storage can be used for archival, offering the best of both models.

WWT is ready to assist organizations on their journey into these initiatives above. Our Advanced Technology Center (ATC) houses an unparalleled selection of multi-vendor storage technologies that can be leveraged for storage workshops and proofs-of-concept to demonstrate their capabilities with near-production workloads.

Conclusion

Primary storage is entering a pivotal phase in 2026 where performance, flexibility and intelligence are no longer optional — they are foundational. The continued rise of flash and NVMe, the integration of AI-driven architectures, and the dominance of object storage signal a future where speed and adaptability define success. At the same time, sustainability, security and governance are becoming strategic imperatives rather than afterthoughts. For IT leaders and architects, the challenge is clear: build storage environments that are future-ready, cloud-integrated and capable of supporting the next wave of data-driven innovation. Those who act now will position their organizations to thrive in an era where storage is not just infrastructure but a catalyst for transformation.

Key takeaways

Flash and NVMe remain central to delivering high-performance primary storage.
AI-driven storage architectures will accelerate automation and predictive analytics.
Object storage continues to dominate for scalability and cloud integration.
Software-defined and cloud-integrated solutions are critical for flexibility.
Sustainability and efficiency are now strategic priorities, not optional goals.
Security, compliance and governance must be embedded into every storage decision.
Future-proofing strategies will separate leaders from laggards in 2026.

Call to action

The storage decisions you make today will define your organization's ability to innovate tomorrow. Evaluate your current infrastructure against these emerging priorities and identify gaps in performance, flexibility and sustainability. Engage with your technology partners, explore AI-ready solutions, and ensure your storage strategy aligns with long-term business objectives. Share your thoughts and experiences in the comments — what trends do you see shaping your storage roadmap for 2026?