20 AI has turned storage into a front-line issue. Regulations are rewriting the rules for data governance. And cyber risk now determines who can even stay in business. At Scality, we see 2026 as the year these forces converge. Performance, compliance, and innovation will finally meet accountability. For IT leaders and architects, these trends signal a shift from building faster systems to building provable ones, where transparency and control are the new performance metrics. Here’s how we expect the landscape to evolve over the next year… 1. AI token economics will make storage the next source of efficiency gains Generative AI is about to put a price tag on every AI token produced. By 2026, “cost per token” will become a new planning metric, forcing enterprises to quantify data cost at a granular level. This will expose hidden inefficiencies — latency, data sprawl, and poor tiering — that directly impact model performance and ROI. To compete, organizations will need fine-grained visibility into how data is accessed and consumed, linking storage behavior to AI output. Automated tiering, predictive caching, and real-time analytics will become default tools for optimization. For storage architects, 2026 will mark a cultural pivot. Economic modeling will no longer stop at compute or network utilization. The leaders will be those who can connect every token processed to the behavior of the underlying data, turning storage from a cost center into a controllable lever of AI efficiency. And as those efficiencies reshape how infrastructure is built, a new breed of service providers will emerge to meet AI’s insatiable appetite for data. 2. NeoClouds will be built for data aggregation at GPU speed A new generation of GPU-optimized service providers (“NeoClouds”) will rise to meet that demand. Their differentiation won’t come from compute density but from how intelligently they move information. To keep thousands of GPUs fed, NeoCloud architectures will adopt a three-tier storage model: Ultra-fast NVMe layers for immediate training, caching, and intermediate results. Massive-scale object storage tiers for datasets, checkpoints, and long-term retention, keeping active corpora available for reuse and ongoing model optimization. Deep cold storage for persistent data state and learnings that must be preserved for retraining, lineage, or compliance The connective tissue will be intelligent data mobility engines that automatically move information between tiers based on activity, temperature, and model lifecycle stage. A global namespace will unify these layers, ensuring consistent access even as data shifts across regions and clusters. For these GPU-era providers, the competitive edge will be data choreography — the ability to prefetch, stage, and clean data at the precise rhythm of model training. By the end of 2026, the defining trait of leading service providers will be continuous data motion. The winners in the NeoCloud era will master the orchestration of data flow at GPU speed. But raw infrastructure alone won’t define success. The edge will shift to how organizations collect, curate, and control the data flowing through it. 3. Data pipelines will replace models as the real competitive edge As foundation models become broadly available, they stop being a moat. The real differentiation shifts to the private corpora and pipelines that feed them — the relevance, cleanliness, freshness, and lineage of enterprise data. By 2026, serious AI organizations won’t treat data prep as a project. Their pipelines will operate as persistent, governed systems that integrate: Continuous ingestion from many sources Labeling and enrichment Transformation and normalization Storage, versioning, and controlled access Every meaningful dataset (and every variant) will need to be traceable to its origin and recoverable on demand. Who created it, which systems touched it, how it changed over time, and which models or applications consumed it can’t be “best effort” metadata anymore; it becomes mandatory context. Object storage sits at the heart of this architecture because it can pair exabyte-scale durability with flexible metadata and versioning. Surrounding it, metadata services must capture full provenance and policy: producers, transformations, approvals, usage, and retention constraints. AI governance, auditability, and safety will depend on this stack. And that’s the pivot. In 2026, the sophistication of your model will matter less than your ability to prove the integrity, lineage, and control of the data behind it. And managing that data lifecycle at scale will demand orchestration, which is exactly where Kubernetes enters the picture. 4. Kubernetes will become the operating system of AI data infrastructure Kubernetes has matured far beyond its origins as a stateless container orchestrator. By 2026, it will serve as the central control plane where compute, storage, and data governance finally converge. Today, databases, message queues, and AI feature stores increasingly run as stateful services inside Kubernetes clusters. That evolution places new demands on the storage layer: Persistent volumes must stay consistent as clusters scale dynamically Snapshots need to be application-aware to protect transactional integrity Elasticity must extend from compute into the data tier itself Object storage provides the durable foundation for these workloads. Through CSI drivers and operators, it integrates directly with clusters to automate provisioning, backup, and recovery. As AI data services expand, Kubernetes will unify compute scheduling with data management and policy enforcement, turning storage into an active participant in cluster operations rather than a passive layer beneath them. By 2026, Kubernetes will function as the de facto “OS” for stateful AI data infrastructure, dissolving the boundary between infrastructure and data services into a single operational domain. But if Kubernetes is becoming the brain of AI infrastructure, organizations will need a new way to measure whether all that intelligence is actually delivering value. 5. Storage performance will be measured by consumption-based SLA metrics For years, storage vendors have competed on throughput, IOPS, and latency benchmarks. That era is ending. AI workloads, regulatory oversight, and sustainability mandates are forcing a shift from raw performance to measurable business outcomes. By 2026, success will be defined through consumption-based SLA metrics that tie infrastructure behavior directly to value, such as: Time to clean restore after a cyber event GPU utilization per dollar of storage spend Energy consumed per terabyte retained These metrics move the conversation from “how fast” to “how effectively.” They quantify what really matters: resilience, efficiency, and cost transparency. To stay relevant, storage vendors will need to embed telemetry, observability, and automated reporting into the fabric of their platforms so that these measurements can be captured and verified natively. By 2026, storage will be judged not by peak speed, but by how intelligently it converts performance into provable results. And as measurement becomes the new mandate, one principle will define the leaders: the ability to move data freely, securely, and on their own terms. 6. Data freedom will become a legal and competitive requirement The EU Data Act is poised to make data freedom a legal and operational requirement, forcing enterprises to rethink how they manage and move information. That shift will end the era of vendor-defined silos. Enterprises will demand open data contracts, interoperable APIs, and standardized metadata semantics to move information seamlessly across hyperscale, on-premises, and sovereign environments. Storage platforms will have to evolve fast to connect ecosystems without compromising compliance. Next-generation architectures will build in native replication, policy-based data movement, and cross-cloud federation by design. Underneath it all lies a new architectural requirement: a global namespace that preserves object identity and context wherever data resides. Version control, unified metadata, and lifecycle automation will act as safeguards against “data drift,” maintaining integrity as data travels. By the end of 2026, the benchmark of leadership in cloud storage will be proof of data freedom — the ability to move, share, and integrate data anywhere, without rewriting applications or surrendering control. But as freedom and interoperability expand, so will scrutiny. 7. Cyber resilience will become a regulated, auditable standard of trust By 2026, cyber resilience will become a verifiable condition for doing business. New regulations like the SEC cybersecurity rules and the EU’s NIS2 directive will require organizations to prove recoverability, not just report incidents. At the same time, cyber insurers will tighten underwriting, embedding technical audits that demand evidence storage and recovery controls actually work. Storage infrastructure will sit at the center of this new accountability model. To stay compliant and insurable, enterprises will adopt immutable storage by default, cryptographically verifiable retention, and automated recovery validation. Features like object lock, versioning, and tamper-proof logs will move from “nice-to-have” to mandatory proof of data integrity. As insurers codify resilience benchmarks, architectures will converge around verifiable resilience — immutable object stores for long-term protection, fast recovery tiers for business continuity, and audit-ready metadata for claims validation. In 2026, cyber resilience will determine who gets insured, who stays compliant, and who customers ultimately trust. Why accountability is the unifying force across 2026 data infrastructure trends Across every prediction runs a shared theme: accountability, the defining principle of next-generation AI-ready data infrastructure. Whether through token economics, data pipelines, or verifiable cyber resilience audits, 2026 will reward organizations that can prove the integrity, efficiency, and trustworthiness of their data operations.Storage may have started as the silent partner in infrastructure, but as AI, compliance, and cyber risk reshape the enterprise, it’s becoming the new control point for enterprise trust and efficiency. In 2026, accountability will be the defining metric of progress, and storage will be where it’s measured.
