Key takeaways: Dell’s AI Data Platform delivers openness, modularity, and enterprise-grade features like GPU acceleration, federated analytics, and hybrid search. It empowers businesses to scale AI efficiently with flexible data access and advanced vector search capabilities.
Artificial intelligence is transforming every part of modern business. But scaling AI isn’t just about GPUs, clusters, or clever models. It starts with a data foundation that helps you move fast, not one that slows you down. And on that foundation, Dell and VAST Data take very different approaches.
A data platform built for choice, not constraints
When you’re deploying AI at scale across different teams, clouds, and tools, openness matters. The Dell AI Data Platform is engineered to give organizations:
- Open table formats
- Flexible access to data where it already lives
- Predictable performance
- No forced migrations to a vendor’s walled garden
While experts note that single-vendor, single-stack architectures can concentrate control and create lock-in risks,¹²³⁴Dell emphasizes modularity, openness, and interoperability. This gives customers more freedom in how they evolve their data strategy over time.
Where architectural choices really diverge
VAST markets a unified platform built around its DataBase, DataStore, DataEngine, and DataSpace components. But independent analysts describe the platform as still maturing toward full lakehouse and enterprise-grade database capabilities.⁵
Vector search example: Maturity matters
VAST’s current vector search (v5.4) uses brute-force search—the slowest and least advanced method for finding matches—on CPUs, with optimization still in the planning phase. Dell positions performance optimization for vector search as a clear differentiator: GPU-based acceleration, HNSW ANN, and zero-rewrite optimization are available today.6
Data access: Dell federates; VAST migrates
A major architectural difference shows up around data movement. VAST promotes SyncEngine to catalog and migrate data from file, object, and SaaS systems into the VAST environment before unified analytics can run. But analysts describe SyncEngine as a migration-centric workflow,7used to bring data into VAST’s AI OS before analytics can be applied.
Acceleration and integration: Designed for what engineers expect
Modern data and AI teams expect:
- Native GPU acceleration
- Spark-based workflows
- Granular access controls
- Jupyter and interactive computation
- Bring-your-own libraries and tools
Dell delivers these capabilities natively. VAST supports Spark through connectors and documents security filtering for vector search, but these operate within the context of its platform-centric workflow.6 Again, the difference is not whether VAST supports these tools — it’s how they’re supported, and whether they require consolidating workloads into one system first.
Enterprise-ready vector capabilities: Depth matters
Enterprises scaling retrieval-augmented generation (RAG) need features like:
- HNSW ANN (smart search indexes that speed up finding similar items)
- GPU-based index generation
- Pipeline connectors for SharePoint, OneDrive, Google Drive, GitHub, and Confluence
- Hybrid search
- Governance that propagates document-level permissions
Dell provides these capabilities comprehensively. VAST, as of version 5.4, performs brute-force vector search and has not yet evolved toward accelerated, more advanced capabilities.6
It’s important to note that this space is evolving at an extraordinary pace, with new requirements emerging every month as use cases grow more sophisticated. That’s why you need a vendor committed to delivering a truly rich, end‑to‑end vector search experience—not just a minimal feature tucked into a broader platform. Injecting data into VAST and auto‑indexing it into vector embeddings is only the first step. Real enterprise‑grade vector search requires all the additional capabilities outlined above, and that’s where Dell continues to advance while VAST falls behind.
Security & governance: Enterprise expectations as the baseline
Enterprise AI requires fine-grained controls including authentication, authorization, a transport layer that protects data as it moves between systems (TLS), and user-based filtering of search results. VAST documents supporting only row/column filtering and TLS. Dell extends governance across more data sources and engines without requiring consolidation.
Storage engines: Certified density & modular scale — Dell’s edge over VAST
At the heart of the Dell AI Data Platform are PowerScale and ObjectScale. PowerScale achieves NVIDIA Cloud Partner certification for 16,384 GPUs in just 168 rack units, while VAST requires nearly 1.8× more space for similar density — translating into up to 72% lower power consumption and up to 88% fewer network switches for Dell.⁸ ObjectScale adds cloud-native scalability with S3 over RDMA, delivering up to 230% higher throughput and up to 80% lower latency for AI pipelines compared to VAST’s CPU-bound, shared-everything design.⁹ Together, these engines provide modular flexibility and openness, avoiding the rigid architecture that locks VAST customers into costly overbuilds.
Openness, modularity, and evolution without compromise
Some platforms ask you to bet your entire architecture on a single vendor — and often require migrating your data into that environment to unlock core features. Dell takes a different path. With open formats, federated analytics, GPU-accelerated processing, and deep enterprise vector capabilities, the Dell AI Data Platform is built for teams that want to move faster without losing flexibility as AI evolves.
Bottom line
If openness, modularity, and multi-engine interoperability matter to you, Dell offers an AI data foundation that lets you build for the long term — without compromise.
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