Teams are now tasked with synchronizing multiple components—storage systems, streaming pipelines, inference runtimes, vector databases, and orchestration layers—to implement a single AI-driven workflow. This complexity is hindering deployments and escalating costs.

VAST Data asserts it has the answer: its newly unveiled unified "AI Operating System," which merges storage, data management, and agent orchestration into one platform. The idea is appealing, yet in a market increasingly leaning toward open, composable systems, VAST’s tightly integrated model raises significant concerns.

VAST's Unified AI Approach

VAST, renowned mainly for high-performance storage solutions, is making a bold leap up the tech ladder. The company's AI Operating System unites storage, real-time data processing, a vector-enabled database, and a native agent orchestration engine within a singular integrated platform.

The proposition is clear-cut: unify AI infrastructure into a single control plane capable of functioning across cloud, edge, and on-premises settings. This method aims to simplify deployment processes, remove integration issues, and cut down latency in AI activities.

The platform includes a runtime for deploying AI agents, low-code interfaces for creating agent pipelines, and a federated data layer that manages compute tasks based on data location and GPU availability. For businesses grappling with AI infrastructure fragmentation, this might drastically decrease time-to-deployment and operational expenses.

The Challenge of Openness

The AI infrastructure sector is progressively shaped by openness and interoperability. Most corporate teams are constructing on adaptable frameworks. Modular tools permit the combination of components like retrievers, vector databases, embedding models, and agent frameworks according to specific needs and existing infrastructure investments. This strategy makes sense in a swiftly changing enterprise AI setting.

VAST adopts a distinct approach, presuming firms will centralize these aspects under a single provider. This premise carries risks. Flexibility, not uniformity, has marked the AI tooling scene in recent times. Although VAST supports standard data formats like S3, Kafka, and SQL, its deeper integration points, especially concerning agent orchestration, remain proprietary.

The Nvidia Dependency Issue

VAST's strategy seems closely linked to Nvidia's ecosystem. In its announcement, the company highlighted its infrastructure deployments in GPU-rich settings, such as CoreWeave and leading hyperscalers. Its backing of VLLM (a high-performance inference engine optimized for NVIDIA hardware) and focus on GPUDirect-style optimizations suggest considerable reliance on NVIDIA's architecture.

This isn’t inherently negative. After all, Nvidia dominates enterprise AI infrastructure. However, it might restrict VAST's appeal for firms exploring alternate accelerators, like AMD Instinct, Intel Gaudi, or AWS Trainium. It also introduces possible overlap with Nvidia's offerings.

As Nvidia launches Enterprise AI, NIMs, and Dynamo, the chipmaker is essentially offering its own AI operating system, enabling a wide partner network to deliver comparable functionalities. Some users might prefer pairing Nvidia’s software suite with top-tier infrastructure tools.

Though VAST seems aligned with Nvidia's AI approach currently, this may not always be the case. When queried about its ties to the Nvidia ecosystem, VAST replied via an anonymous spokesperson stating, “We have always emphasized that our software stack supports industry standards and caters to our clients’ needs. This implies we aim to qualify hardware from various vendors, including Nvidia, AMD, and others, to meet whatever our clients require.”

Competitive Landscape

VAST is endeavoring to surpass conventional rivals by tackling AI infrastructure comprehensively. Yet this also places it in direct competition with vendors possessing stronger application-layer ecosystems and more targeted storage strategies. Finding a direct rival to what VAST introduced is challenging, as VAST competes against more modular methods.

Much of the drive in enterprise AI infrastructure stems from combining top-notch capabilities into what Nvidia refers to as an "AI factory." Leading OEMs are following Nvidia's lead, with Dell Technologies recently unveiling its AI Factory 2.0. This allows firms to deploy a verified hardware infrastructure while retaining the ability to use the best data management tools for their target workload.

Building on the AI factory, cutting-edge infrastructure companies like WEKA are incorporating advanced AI-focused features, such as its recently launched Augmented Memory Grid. This feature offers a seamless extension of the per-GPU context window in an LLM by utilizing its data infrastructure as an extension of the GPU's key-value cache.

At the other extreme, companies like IBM are pushing the limits of enterprise-safe agentic AI with tools like its watsonx Orchestrate tool, unveiled at its recent IBM Think client conference.

IBM’s approach supports an agentic framework that’s open, backing Nvidia and the more open llamastack frameworks, while smoothly integrating into nearly any enterprise AI environment.

There are numerous other instances in this swiftly evolving field.

Analyst's Perspective

VAST positioning its new AI OS as "the OS for the thinking machine" is undoubtedly ambitious. The platform tackles a genuine market demand: diminishing vendor complexity and removing integration hurdles in AI infrastructure. For entities operating at vast GPU scales with strict control demands, like in specialized GPU cloud providers where VAST has achieved early success, this approach will prove beneficial.

VAST's AI Operating System mirrors the increasing acknowledgment that AI infrastructure necessitates fundamental architectural shifts. The company is making a credible attempt to construct that groundwork from scratch. For organizations seeking unified control over AI data pipelines at enterprise scale, it might represent a compelling option.

However, for the wider market, particularly those valuing open frameworks, multi-vendor adaptability, or modular innovation, VAST's strategy might seem overly limiting. The platform will require swift development to incorporate external agent frameworks, emerging standards like MCP, and integration pathways that let enterprises retain their current orchestration investments. VAST claims it will follow the market.

If VAST can open its ecosystem while keeping architectural consistency, it could pioneer a new category of enterprise AI infrastructure. But success is far from assured. Current market trends favor flexibility over consolidation, but this inclination is likely to change over time.

Although enterprises might be cautious in adopting VAST’s new solution, the company is placing a robust long-term bet. Many customers today will discover value in what VAST is delivering with its AI OS, a list that will expand over time.

Almost every technological shift leads to consolidation, with AI expected to follow the same route. VAST entered early, claiming the first-mover advantage. It’s a strong move for an ambitious company, one worth observing unfold.

Disclosure: Steve McDowell is an industry analyst, and NAND Research is an industry analyst firm, that engages in, or has engaged in, research, analysis, and advisory services with many technology companies, including VAST Data, Dell Technologies, IBM, WEKA. Mr. McDowell does not hold any equity positions with any company mentioned.

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