Storage Array Makers Race into Data Management for AI as Lock-In Concerns Mount

AI is pushing storage vendors into data management. Here's what leaders need to know

Storage is stepping out of the backroom. Vendors are repositioning as data management platforms because AI value depends on getting the right data, in the right place, with the right context.

That shift is real. It also introduces a clear tension: suppliers want you on their rails; leaders want freedom to move across clouds, sites, and tools without friction.

Why this matters to management

AI outcomes are throttled by data movement, metadata quality, and pipeline reliability. Infrastructure that used to be "set and forget" now sits in the critical path of model training, retrieval, and governance.

Your decisions here impact AI time-to-value, compliance, and long-term cost. The trap is buying for speed today and paying for lock-in tomorrow.

What the big suppliers are selling

• Dell: AI Factory built on Dell storage with a Data Lakehouse to merge stored metadata with external sources. Early-stage Project Lightning targets next-gen parallel file, plus Apex for unified pay-as-you-go control.
• Hitachi Vantara: VSP One aims for a common data plane across block, file, object, hybrid, and multicloud-less AI pitch, more estate simplification.
• HPE: Alletra all-NVMe plus Data Services Cloud Console (DSCC) as a SaaS control plane for Nimble and Primera. InfoSight adds AI-driven recommendations and some data mobility across HPE systems.
• IBM: Broad toolset spanning databases and lakehouse concepts, but no single, unified data management platform.
• NetApp: "Metadata fabric" via the NetApp Data Platform to tighten AI data pipelines. MetaData Engine extracts from ONTAP and is managed through BlueXP across on-prem and cloud.
• Pure Storage: Pure1 for ops and upgrades, Fusion to provision by performance profile across estates, and Enterprise Data Cloud to abstract provisioning away from the array.
• Vast Data: Positioning as an "AI operating system" from storage up. Vast Data Platform includes an event broker with Kafka API integration, plus AgentEngine (planned GA late 2025) for AI agent management. See Apache Kafka for the event streaming backbone many shops pair with these pipelines.
• Huawei: Data Management Engine (DME) as a central interface for Huawei and third-party storage, switches, and hosts. Includes data warehouse, vector database, catalog, lineage, versioning, and access control.

What analysts highlight

The market shift is real: value has moved from pure compute to data that is ready, portable, and trustworthy. That's why vendors are racing to own the control plane.

But there's a catch. Analysts flag long-term lock-in as a likely outcome of single-vendor platforms, while multi-supplier tools (for example, Hammerspace, Arcitecta, Komprise) aim to manage across arrays and clouds without tying you down.

The key question: Will the platform work with any data on any storage, or mainly its own? Many offerings still tilt vendor-centric rather than data-centric.

The executive playbook: decisions that matter

1) Start with data outcomes, not storage SKU

Define the AI use cases: RAG for apps, model training, nearline analytics, or cold archive enrichment. Each has different needs for throughput, latency, lineage, and retention.

2) Make metadata a first-class requirement

Your AI pipeline lives or dies by metadata. Demand open access to indexes, policies, and lineage so you can query, govern, and migrate without rewriting everything later.

3) Reduce switching costs before you buy

Ask for exportable configs, open APIs, S3/NFS/SMB compatibility, and support for open table/format standards where relevant (Iceberg, Delta, Parquet). Get the exit plan in writing.

4) Test data mobility, not just benchmarks

Run live pilots that move data across on-prem, multiple clouds, and tiers. Measure egress fees, rehydration time, index rebuild overhead, and pipeline breakage.

5) Treat governance as non-negotiable

Lineage, access control, PII handling, and policy automation must be built in. Cross-check with your AI risk framework. If you need a reference point, review the NIST AI Risk Management Framework.

A practical checklist for vendor comparisons

• Data coverage: block, file, object across on-prem and at least two public clouds.
• Open interfaces: APIs, event streams, and connectors to your existing catalog, MDM, and observability stack.
• Metadata: global indexing, search, lineage, policy-as-code, and export options.
• AI pipeline fit: native support for vector workloads, feature stores, and event streaming.
• Interoperability: third-party storage management without penalties or reduced functionality.
• Automation: placement by performance/cost profile with guardrails, not just vendor-specific magic.
• Cost clarity: egress, reindexing, snapshots, data movement, upgrades, and cross-region replication.
• Resilience: multi-site failover, ransomware restore objectives, and immutability settings.
• Exit plan: data and metadata portability, migration tooling, and timelines defined up front.

Questions to put in every RFP

• Can your control plane discover and manage third-party arrays and cloud buckets at feature parity?
• How do you expose metadata and lineage to external catalogs and observability tools?
• What are the data movement costs across tiers and clouds over three years?
• Do you support event streaming and CDC patterns used in our AI pipelines? Show the reference architecture.
• What's the rollback plan if projections on performance or cost don't hold?

Platform lock-in vs multi-supplier control

Single-vendor platforms can speed deployment and simplify support-until you need something they don't prioritize. Multi-supplier control planes add flexibility, but you'll own more integration.

Pick based on volatility. If your AI use cases and data sources will change often, bias toward open interfaces and cross-platform management. If the workload profile is stable and time to deployment is critical, a unified stack can be a reasonable trade.

Bottom line

AI is pushing storage into a new job: orchestrating data, not just holding it. Your edge comes from clear outcomes, hard requirements on metadata and mobility, and a contract that makes change affordable.

Equip your team to evaluate these platforms with confidence. If you're building an upskilling plan for leaders and data teams, explore curated options at Complete AI Training.