Analyst(s): Brad Shimmin
Publication Date: February 27, 2026
VAST Data’s VAST Forward event revealed a suite of technologies intended to collapse the traditional AI stack and redefine the role of infrastructure. By embedding vector acceleration directly into the VAST Data Platform and orchestrating global namespaces via Polaris, VAST is challenging the necessity of external vector databases and cumbersome ETL middleware. This architectural shift positions the VAST Data platform as a foundational operating system for the agentic AI era.
What is Covered in This Article:
- The consolidation of the AI data pipeline by embedding vector database acceleration and semantic search capabilities directly into the platform.
- A new global control plane built to manage data gravity and sovereignty across distributed clusters and diverse cloud environments.
- Deep hardware acceleration utilizing BlueField-3 DPUs and specific NVIDIA libraries (CUDS, CUVS) to create an integrated data stack for AI workloads.
- The introduction of zero-trust governance and autonomous reinforcement learning within the platform architecture facilitates model refinement.
- The growth of VAST’s community through native integrations with strategic partners like CrowdStrike and 12 Labs.
The News: At its VAST Forward event, VAST Data unveiled significant updates to its platform, including the Policy Engine, Tuning Engine, Polaris, and a deepened technical partnership with NVIDIA. These enhancements aim to streamline the AI infrastructure stack by moving intelligence closer to the data, effectively reducing the reliance on external vector databases and complex Extract, Transform, Load (ETL) processes. Alongside these technical reveals, VAST shared impressive business milestones: the company has surpassed $500 million in contracted Annual Recurring Revenue (ARR), reached $4 billion in all-time software bookings, and achieved positive operating income. These figures suggest VAST is entering a phase of financial sustainability as a primary platform player in the high-growth AI infrastructure market.
Collapsing the Stack: VAST Data’s Bid to Own the AI Data Loop
Analyst Take: The AI Storage market currently feels like a noisy theater of incremental speed bumps and rebranded legacy arrays. VAST Data’s latest move indicates a desire to exit this commodity race entirely. Rather than competing solely on how quickly they can feed a GPU (a metric rapidly hitting its point of diminishing returns), VAST is repositioning itself as an intelligent data service. The storage layer is shifting from a passive bucket into an active participant in the AI inference loop.
According to Futurum 1H 2026 Data Intelligence, Analytics, & Infrastructure Market Sizing & Five-Year Forecast, the Data Intelligence, Analytics, and Infrastructure market should reach $541.1 billion this year. This growth follows a broader transition from experimental pilots to production-grade agentic workflows. VAST is catching this wave by addressing what Futurum sees as a shift from data technicians to “AI Shepherds”, where the data professional’s focus moves from manual plumbing to governing the behavior of autonomous systems.
Collapsing the Pipeline with VAST Database Acceleration
The most technically significant announcement from VAST involves the native acceleration of vector search and tables. VAST is effectively consolidating the traditional AI data pipeline by embedding vector acceleration directly into the VAST Data platform. This approach taps into the surplus compute power within VAST’s Disaggregated Shared Everything (DASE) architecture (specifically the stateless protocol servers) to manage vector indexing and search natively.
By incorporating NVIDIA’s CUVS (vector search acceleration) and CUDS (data frame acceleration) libraries, VAST allows enterprises to bypass the complex ETL processes typically required to move data to external vector databases. This solves the stale data problem often encountered in Retrieval-Augmented Generation (RAG) because the index resides alongside the data. When the storage platform handles the semantic heavy lifting, it eliminates the latency and fragility inherent in shifting data blocks between disconnected silos.
Polaris and the Management of Data Gravity
While AI training clusters are massive and centralized, data creation remains inherently distributed across edges, factories, and regional offices. Polaris addresses this reality through a global control plane and namespace implementation. Unlike traditional asynchronous replication, Polaris provides a unified view across on-premises clusters, public clouds, and Neo-clouds. It orchestrates metadata globally while execution remains distributed.
This is a strategic move for VAST because it decouples the compute strategy (where GPUs are used) from the data strategy (where data actually lives). This allows enterprises to operate across multiple cloud providers without the friction of large-scale data migrations. This architectural flexibility reflects the 77% of organizations in our recent survey who are prioritizing open, decoupled architectures to avoid vendor lock-in.
The NVIDIA Stack: Moving Beyond Compatible to Foundational
The announcement of an integrated AI data stack with NVIDIA moves the VAST Data platform from compatible to foundational. This integration uses NVIDIA BlueField-3 DPUs to offload storage and networking tasks, supporting high-performance GPU environments with minimal overhead.
This positions VAST as a streamlined option for high-performance AI infrastructure. By establishing reference architectures with partners like Cisco and Supermicro, the company is directly challenging the dominance of high-performance, but often overly complex, parallel file systems. For CIOs, this reduces the risk of build-your-own and ensures that storage does not become the bottleneck in an expensive GPU cluster.
Trust and Autonomy in the AI Operating System
VAST is introducing two new capabilities to address AI governance: the Policy Engine and the Tuning Engine. The Policy Engine mediates every action in the system, acting as a zero-trust layer that can redact or transform data flowing through it based on corporate requirements. This ensures sensitive data is governed before it ever reaches an endpoint.
The Tuning Engine facilitates a continuous reinforcement learning loop, collecting telemetry to fine-tune models within the customer’s specific environment. This aligns with our observation that 54% of data professionals cite data governance as a primary driver for their current strategy. If a system can facilitate recursive computation and model improvement while maintaining zero trust, VAST transitions from a component vendor to a comprehensive data platform.
Digging a little deeper, the Tuning Engine fundamentally changes the relationship between infrastructure and model accuracy by automating the traditionally manual, high-friction process of Reinforcement Learning from Human Feedback (RLHF). Instead of treating model fine-tuning as a bespoke science project requiring massive data exports to separate compute clusters, VAST’s architecture captures user interactions and inference outcomes directly within the data path.
By embedding this telemetry collection natively into the platform, the system creates a closed loop where models can update and refine their weights based on real-world usage—effectively turning the storage layer into an active gymnasium where models work out and get stronger without constant intervention from data engineers.
This approach is critical for democratizing AI adoption at the last mile, as it lowers the barrier to entry for businesses that lack an army of PhDs. Currently, most enterprises are stuck deploying static models that either overwhelm the model’s context window or degrade in relevance the moment they enter production, because the cost and complexity of continuous fine-tuning are too high.
By offloading the heavy lifting of reinforcement learning to the platform itself, VAST enables organizations to deploy what are, in effect, self-learning agents that naturally adapt to specific corporate vernacular and workflows. It shifts the focus from building the perfect model to simply using the system, ensuring that the intelligence can improve as a natural byproduct of doing business.
The Cosmos Ecosystem Reality Check
Cosmos represents VAST’s attempt to create network effects through formal partnerships with cloud providers and ISVs like CrowdStrike. While the vision of a Data OS is compelling, storage companies often struggle when they attempt to become platform vendors. Cosmos v2 is an effort to expand this ecosystem, deriving value from deep-stack integrations.
The success of this initiative will be measured by the depth of these engineering integrations. Partnering with 12 Labs to support computer vision models at the edge and on-premises is a strong starting point. However, VAST must prove that these integrations offer better operational efficiency than the standalone services they are designed to replace.
What to Watch:
- The Vector Database Market Response: As the VAST Data platform further integrates and optimizes vector search acceleration, incumbents in the vector database market may be forced to move further into application logic or deeper into model orchestration to maintain their edge.
- Global Metadata Latency: While the Polaris vision of a global namespace is architecturally ambitious, the physics of global metadata coherence across high-latency links remains a persistent challenge for any vendor.
- Procurement Shifts: With new VAST accelerated node types and NVIDIA integration, procurement teams may begin to view storage and compute as a single, bundled decision. This could pressure traditional storage vendors who lack deep DPU-level integration.
See the complete press release on VAST Data unveiling secure, trusted, self-learning agentic AI.
Disclosure: Futurum is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of Futurum as a whole.
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Author Information
Brad Shimmin is Vice President and Practice Lead, Data Intelligence, Analytics, & Infrastructure at Futurum. He provides strategic direction and market analysis to help organizations maximize their investments in data and analytics. Currently, Brad is focused on helping companies establish an AI-first data strategy.
With over 30 years of experience in enterprise IT and emerging technologies, Brad is a distinguished thought leader specializing in data, analytics, artificial intelligence, and enterprise software development. Consulting with Fortune 100 vendors, Brad specializes in industry thought leadership, worldwide market analysis, client development, and strategic advisory services.
Brad earned his Bachelor of Arts from Utah State University, where he graduated Magna Cum Laude. Brad lives in Longmeadow, MA, with his beautiful wife and far too many LEGO sets.
