Why I’m A Big Fan of Vast Data’s Advancements in Next-Gen AI Storage

Hey everyone, if you’ve been following the explosion in AI tech, you know that storage isn’t just about dumping files anymore – it’s the backbone for training models, querying massive datasets, and keeping everything secure. I’ve been diving deep into Vast Data’s platform lately, and I have to say, their R&D work on AI-focused storage has me hooked. It’s not perfect for every scenario, but for AI workflows, it’s a massive step forward.

In this blog, I’ll walk through why I think Vast Data stands out and spotlight a few examples I’ve seen recently. I’ll also cover how it streamlines AI development, enables smart querying and handles security better than I expected. And to keep it fair, I’ll point out where I think it falls short –

Streamlining AI Development

One of the biggest headaches in AI work is juggling data across systems (Data Siloes). Traditionally, you’d store a file – like a scanned receipt from a dinner in Spain – in one place. You’d then run OCR (optical character recognition) to pull out the text, and stash that metadata in a separate database. But when you move the file – your links break, and everything’s a mess.

Picture this: your data is everywhere, your files are in one system, your text is in another, and your pipelines feel like they’re held together with duct tape. I’ve dealt with this while trying to process something as simple as a scanned receipt or a medical image. You end up with OCR in one tool, storage in another, and constant worry that something’s going to break. The solution I’ve seen flips this on its head. You ingest data once, and it’s processed, embedded, and indexed right away using smart microservices for things like OCR or vision-language models. That receipt gets searchable metadata in multiple languages, sitting right next to the original image. For medical scans – annotations, AI analysis, and raw files are all in one place, ready to go. For model training, you can grab a snapshot of the data and move it to GPU-ready buckets without touching live systems. It keeps things stable and GPUs fed, which saves you from the usual pipeline nightmares.

But here’s a key point I want to emphasize: You don’t want to train AI models directly on your live production systems. Imagine running heavy GPU processing on a hospital’s electronic medical records (EMR) or a bank’s transaction database. It could grind everything to a halt. Vast Data helps avoid that by letting you create snapshots of your datasets. These are essentially point-in-time copies you can drop into on-premises S3-compatible buckets for training. Those copies support high-speed (GPU Direct) transfers of data to your GPUs, keeping them fully utilized. You can have multiple versions too, which are perfect for testing different model iterations without touching the originals. It’s like having isolated sandboxes for experimentation, which keeps production stable and speeds up development. In my view, this reduces complexity a ton and helps maintain data integrity, making AI projects feel less like wrestling with spaghetti code.

Metadata Query Engine: Precision for Researchers

Unstructured data is a beast to query. I’ve spent hours trying to dig out specific files, like “CT scans with certain markers” or “documents in a specific language.” The old way meant endless manual curation or clunky ETL processes. Now, with AI-enriched metadata and a query engine that acts like a SQL table, it’s a breeze. If you need “all receipts with Spanish text”, it’s there in seconds. This precision makes curating datasets for training so much faster, and since metadata lives with the data, there’s no pipeline shuffle to slow me down.

For instance, if you’re tweaking a translation model, you could query: “Show me all receipts with Spanish text in the metadata.” That pulls together a dataset for evaluation or fine-tuning. Or, as a medical researcher, “Find all CT scans annotated with tuberculosis indicators.” It’s a game-changer because it uses the outputs from your AI processing (like detected languages or diagnoses) as search criteria.

This is all about embedding metadata smartly so queries are fast and precise. For data scientists, it means quicker curation of high-quality training sets, which directly impacts model performance. No more manual sifting through folders; it’s approachable querying that feels intuitive.

Secure Access Control: Protecting Sensitive Data with “Chat with Your Data”

Security in AI is tricky because tools like large language models (LLMs) don’t inherently understand permissions or have Access Control Lists. For example, if you fed sensitive HR files or top-secret engineering docs into a shared model, anyone querying it might have access to information they shouldn’t.

Compare that to Microsoft Copilot approach with SharePoint. It’s handy, but by default, it indexes everything, and if your permissions aren’t airtight, leaks can easily happen. I had one customer who, after turning this feature on, spent $10M on a data labelling clean-up exercise.

Vast Data’s approach is more deliberate. Vast’s InsightEngine (a.k.a “Chat with Your Data”) adds built-in retrieval-augmented generation (RAG) as a core file system function, so you can converse with your data securely without the need for extra tools. You get the same CoPilot chat type capability with your data, but you choose which folders to index, and it preserves your access control lists (ACLs).

To give credit where due, Cohesity got the ball rolling a few years back with Gaia, letting you chat with backup data. But Vast takes it further with real-time handling of structured and unstructured data at exabyte scale, powered by NVIDIA for RAG. For example, HR folks could query employee records via a chatbot, but only see what’s authorized for them. It demystifies secure AI querying, so you’re not exposing everything; you’re controlling access at the source.

Secure Multi-Tenancy: Virtual Air Gaps for Data Isolation

In environments like telcos or medical labs, data from different customers or projects can’t mix. So, something like a sysadmin accidentally granting access could be nightmare fuel for compliance.

Vast Data’s multi-tenancy creates virtual air gaps – logical separations that ensure partitions stay isolated. Think of it as digital silos: even admins can’t peek across without explicit setup. This builds trust & auditability, especially when guaranteeing clients their data won’t leak. It’s not overkill; it’s practical for regulated industries and makes AI work feasible without constant worry.

Automating AI Tasks Feels Like a Superpower
Manual data processing is a grind – waiting on batch jobs or external tools to transcribe, summarise, or enrich information. Now, you have a system that runs itself. If a new file arrives, it’s instantly transcribed or summarised, transformed, and pushed to APIs or search indexes. There’s no waiting or handoffs, just real-time pipelines doing the heavy lifting.

Cisco Partnership: A Turnkey AI Solution

Cisco has added Vast solutions to their price list, both as a turnkey storage appliance, or as Cloud Managed AI clusters “Nexus Hyperfabric AI” – which pairs Cisco UCS servers with a pre-validated stack for AI infrastructure.

For IT teams, this means easier procurement and faster rollout, without having to piece together hardware from scratch. It’s essentially approachable scaling – you buy it, plug it in, and drive toward your AI goals.

(Note: Cisco also works with other storage partners, including Netapp, Pure & Nutanix)

Reference Customers: Powering Leading AI Labs and Hyperscalers

Vast isn’t a theory; it’s in use by some heavy hitters:

CoreWeave, a GPU cloud provider for AI, built its global infrastructure on Vast for high-performance training.

xAI, Elon Musk’s venture, uses it for data processing in its Colossus cluster.

Lambda leverages Vast for AI workloads across their Nvidia GPU clouds and data centres.

Even Pixar taps into it for handling petabytes of rendered assets as AI training data.

Core42 (from G42) standardizes on Vast too.

These aren’t small players; these companies are pushing AI boundaries at scale, and seeing Vast enable that reinforces its value for real-world innovation.

Limitations of Vast Data: Not a Universal Solution

But Vast isn’t for everything. It’s stellar for unstructured data in AI and supports a swiss army knife of protocols, including: SMB, RDMA, NFS, S3, Kafka. While it’s great at unstructured data, from a block storage perspective, VAST only recently added NVME over Fabric, but it skips legacy block interfaces like iSCSI or Fibre Channel. If you’re running traditional databases, like Oracle or SAP, and need two or three-way synchronous metro clustering for zero-downtime high availability, Vast might not be the right fit for those types of enterprise workloads; alternatives could fit better.

My point of view is that Vast is purpose-built for next-gen AI, so evaluate your mix. This focus is a strength, but know your needs to avoid mismatches.

Unmatched Storage Efficiency

Storage costs can be a significant part of Cloud budgets. Unlike CPU, you can’t ‘power off’ your storage when you aren’t using it – storage needs to be running 24/7. Traditional data protection setups like RAID 6 use striping with parity, often at 20% overhead (e.g., 8 data + 2 parity drives). Vast’s Disaggregated Shared Everything (DASE) architecture does better. A 14D+2P erasure code hits about 12.5% overhead on a 12-server / 1 PB cluster, giving you 8.5% more usable capacity, without factoring in VAST’s deduplication & compression algorithms

What does that mean? More usable space for your money, which is great for hoarding medical images or receipt archives without extra hardware. It’s efficient without being flashy, lowering costs for scaling AI datasets.

| Storage Method | Overhead % | Min Servers | Usable Capacity Gain Example |

|—————|————|————-|——————————|

| Traditional RAID 6 (8D+2P) | ~20% | N/A | Base (e.g., 80% usable) |

| Vast DASE (14D+2P) | ~12.5% | 12 | Up to 37.5% more usable space |

This table simplifies it: Less overhead = more room for AI work.

Why Vast Data Wins for AI (Despite Limitations)

Pulling it together, Vast shines in streamlined workflows (with those handy snapshots), precise metadata searches, secure chatting via InsightEngine, multi-tenancy isolation, Cisco’s plug-and-play, and efficient storage. For IT pros, it’s ops simplicity; business folks get compliance and quicker insights; data scientists love the query precision.

Sure, limitations mean it’s not universal, but for AI-focused orgs, it delivers. And customers like CoreWeave and xAI prove it scales where it counts.

If you’re building AI infrastructure, I recommend checking out Vast Data, especially with Cisco’s integration. It’s a solid foundation for secure, efficient innovation. But be sure to match it to your workloads. AI storage doesn’t have to be intimidating; tools like this make it incredibly accessible.

That’s all for now. But I’m interested – have you tried Vast yet? Let me know how it’s worked for you.