Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Giorgio Regni, Founder and Chief Technology Officer of Scality
AI has pushed storage into a front-line strategic role. Global regulations are redefining how data must be governed. Cyber risk increasingly determines who can operate, who gets insured, and who customers trust.
Industry observers widely expect 2026 to be the year when these forces converge. Performance, compliance, and innovation will intersect with a new overarching requirement: provability. For IT leaders and architects, success will shift from building faster systems to building systems that can demonstrate, with evidence, how they behave transparently, efficiently, and securely.
Together, these changes signal the beginning of an accountability-driven era for data infrastructure. Here are the key trends shaping what comes next.
1. AI Token Economics Will Make Storage the Next Efficiency Frontier
As the economics behind generative AI mature, every token produced by a model will be tied to measurable cost. By 2026, “cost per token” is expected to become a central planning metric, exposing inefficiencies that were previously overlooked:
- Latency bottlenecks that reduce GPU utilization
- Data sprawl that inflates I/O overhead
- Misaligned tiering that wastes energy and budget
To address this, organizations will need fine-grained visibility into data access patterns and storage behavior. Automated tiering, predictive caching, and real-time analytics will shift from optional tools to foundational capabilities.
In this environment, economic modeling will extend deeper into the data layer. Storage will evolve from a static cost center into a dynamic lever for improving AI performance, giving rise to a new class of service providers optimized for AI’s massive data demands.
2. NeoClouds Will Emerge as GPU-Era Data Aggregation Platforms
A new generation of GPU-optimized service providers (often referred to as NeoClouds) will gain momentum. Their competitive edge will come not from raw compute density, but from how effectively they move, prepare, and deliver data at the speed modern GPUs require.
Their architectures are expected to align around a three-tier storage design:
- Ultra-fast NVMe for active training and intermediate results
- Massive object stores for corpora, checkpoints, and ongoing optimization
- Deep cold storage for lineage-preserving retention and regulatory compliance
The glue between these layers will be intelligent data mobility engines that automatically orchestrate data movement based on activity, temperature, and training lifecycle.
By the end of 2026, the defining capability for leading AI-focused providers will be continuous data motion, specifically the ability to prefetch, stage, and clean data at GPU speed across regions and clusters.
3. Data Pipelines Will Overtake Models as the Real Competitive Advantage
As foundation models become increasingly accessible, differentiation will move upstream to the private data pipelines that feed them. Enterprises will compete on the relevance, cleanliness, freshness, and full lineage of their data, not simply on the model itself.
By 2026, advanced AI organizations will operate data pipelines as persistent, governed systems built to support:
- Continuous ingestion across diverse sources
- Automated enrichment, labeling, and normalization
- Versioned storage with fine-grained access control
- Complete provenance and lifecycle tracking
Object storage will serve as the backbone for this architecture thanks to its scale and metadata flexibility. Metadata services will capture complete lineage, including who created the data, how it evolved, who approved it, and which systems consumed it.
Model performance will matter, but the ability to prove data integrity and traceability will matter more. And orchestrating this lifecycle at scale will require the next major evolution in infrastructure.
4. Kubernetes Will Become the Operating System of AI Data Infrastructure
Kubernetes is rapidly maturing from a stateless container orchestrator into the central control plane for stateful, AI-driven data services. By 2026, it is expected to function as the operational backbone for environments where compute, storage, and governance intersect.
Databases, message queues, vector stores, and AI feature stores increasingly run as stateful services inside Kubernetes. This creates new demands on storage systems:
- Persistent volumes must remain stable across dynamic scaling
- Application-aware snapshots must protect transactional integrity
- Elasticity must extend from compute into storage
Object storage will continue to provide the durable foundation beneath these workloads. Through CSI drivers and operators, it will automate provisioning, backups, and recovery across clusters.
Kubernetes will unify scheduling, data management, and policy enforcement, effectively becoming the operating system for AI-era data infrastructure.
5. Storage Performance Will Be Evaluated Through Consumption-Based SLAs
Traditional storage metrics like IOPS and latency are losing relevance in AI-centric environments. In 2026, organizations will shift toward consumption-based SLA metrics that measure infrastructure by business impact rather than raw performance.
Key metrics will include:
- Time to clean restore following a cyber incident
- GPU utilization per dollar of storage spend
- Energy consumed per terabyte retained over time
These KPIs quantify what truly matters: cost transparency, operational efficiency, sustainability, and resilience. To meet these expectations, storage platforms will need embedded telemetry, observability, and automated reporting that enable independent verification.
Peak speed will matter less than whether systems can prove how effectively they convert performance into results.
6. Data Freedom Will Become Both a Legal Mandate and a Competitive Advantage
New regulations (particularly in Europe) are driving a shift toward data mobility, interoperability, and open ecosystems. The EU Data Act, among others, will accelerate demand for architectures that enable seamless data movement across clouds, sovereign environments, and on-prem systems.
Organizations will increasingly require:
- Open data contracts
- Interoperable APIs
- Standardized metadata semantics
- Built-in cross-cloud federation
- Policy-driven data mobility
Supporting this will require a global namespace that preserves object identity, context, and version history across environments. The ability to prevent “data drift” will become essential for compliance and operational consistency.
By late 2026, leadership in cloud storage will be measured by proof of data freedom. This includes the ability to move and govern data anywhere without rewriting applications or sacrificing control.
7. Cyber Resilience Will Become a Regulated, Auditable Standard
Cyber resilience is shifting from a best practice to a mandatory, verifiable condition of doing business. Regulations such as the SEC cybersecurity rules and the EU’s NIS2 directive will require organizations to demonstrate recoverability, not just incident reporting. Cyber insurers will reinforce this shift by requiring technical evidence that backup and recovery systems actually function under stress.
This will push enterprises toward:
- Immutable object storage as a baseline
- Cryptographically verifiable retention policies
- Automated recovery testing
- Tamper-proof logs and object-level versioning
By 2026, cyber resilience will influence who gets insured, who stays compliant, and who earns customer trust. Storage infrastructure will be a central component of this new accountability model.
Accountability: The Unifying Principle of 2026
Across every trend, one theme stands out: accountability is becoming the defining principle of next-generation data infrastructure. Token economics, data lineage demands, interoperability mandates, and cyber regulations all point toward a future where organizations must prove the integrity, efficiency, and reliability of their data operations.
Once a quiet, hidden layer, storage is now becoming the central control point for:
- Trust
- Efficiency
- Governance
- Interoperability
- Resilience
In 2026, leaders will be those who can demonstrate credible, measurable accountability across the data lifecycle. Because in the AI-driven era, competitiveness isn’t just about having data but about proving that the data is controlled, secure, portable, and trustworthy.
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ABOUT THE AUTHOR
Giorgio Regni leads the company’s long-term technology vision and innovation strategy, drawing on decades of experience in distributed systems, object storage and cloud infrastructure. Regni founded Scality in 2009 with a mission to solve the challenges of storing and managing massive amounts of unstructured data at scale. Today, his leadership continues to shape Scality’s RING and ARTESCA product lines-trusted by some of the world’s largest enterprises, service providers, and public sector organizations.
Regni is passionate about open standards, high-performance computing, and designing software architectures that stand the test of time. He holds a Master’s degree in Computer Science from �cole Centrale Paris.
