VAST Data Explained for Founders: Why AI Storage Is Suddenly Strategic
Ask a founder building an AI product what infrastructure they need, and they will immediately talk about GPUs. How many H100s, how much compute time, which cloud provider has the best pricing. Ask about storage, and you will get a blank stare. Maybe a vague reference to S3 buckets. Maybe "we use Postgres." Storage is the infrastructure category that everyone needs and nobody thinks about — until it becomes the bottleneck that slows everything else down.
In 2026, storage is becoming that bottleneck for a growing number of AI companies. Training runs that cost millions of dollars in GPU time are being throttled because the storage system cannot feed data to the GPUs fast enough. Inference pipelines that need to search across terabytes of documents for retrieval-augmented generation are limited by storage IOPS, not model speed. Model checkpoints that need to be saved every few minutes during training consume petabytes and must be written at speeds that traditional storage systems cannot sustain.
On the Technology Brothers Podcast Network, the infrastructure conversations have increasingly turned to the storage layer. John Coogan and Jordi Hays have highlighted that the AI infrastructure stack is only as fast as its slowest component, and for many workloads, that component is now storage. Understanding why — and what companies like VAST Data are doing about it — is becoming essential knowledge for any founder building on AI.
The Problem: Why Traditional Storage Cannot Keep GPUs Fed
To understand the storage bottleneck, you need to understand how AI training actually uses data. A large language model training run reads a massive dataset — potentially petabytes of text, images, or video — and processes it in batches. Each batch needs to be loaded from storage into GPU memory, processed, and then the next batch needs to be ready immediately. If the storage system cannot deliver data as fast as the GPU can consume it, the GPU sits idle. You are paying for GPU time but not using it.
The GPU Starvation Problem
Modern AI accelerators are incredibly fast. An NVIDIA H100 can process data at rates that would have been unthinkable five years ago. But that processing speed creates an enormous demand for data throughput. When you have a cluster of 256, 1,024, or 4,096 GPUs all requesting data simultaneously, the aggregate throughput requirement can exceed hundreds of gigabytes per second.
Traditional storage systems were not designed for this workload pattern. Network-attached storage (NAS) systems were designed for file sharing among dozens of users, not streaming data to thousands of GPUs. Storage area networks (SANs) were designed for transactional database workloads with random I/O patterns, not sequential reads of massive datasets. Object stores like S3 were designed for durability and cost-efficiency, not raw throughput.
The Multi-Protocol Problem
AI workloads also create a multi-protocol challenge. During training, data is typically read as files (NFS or POSIX file system access). Model checkpoints are written as large objects (object storage). Vector search for RAG applications uses database queries. Analytics on training metrics use SQL. Traditionally, each of these access patterns required a different storage system, meaning data had to be copied between systems, managed separately, and kept in sync — a nightmare of complexity and cost.
The Cost Problem
Storage can represent 20% to 40% of total AI infrastructure spend, and that percentage is growing as models get larger and datasets expand. For a company spending $10 million per year on GPU compute, $2 million to $4 million goes to storage. At this scale, storage efficiency is not a nice-to-have — it is a material cost driver that directly impacts runway and unit economics.
VAST Data's Approach: Unified Storage for AI
VAST Data has emerged as one of the most important companies in AI infrastructure by solving these problems with a fundamentally different architecture. Founded in 2016, VAST has built a storage platform specifically designed for the demands of AI workloads. Its approach centers on three key innovations.
Universal Storage: File, Object, and Database in One System
VAST's core architectural innovation is a universal storage system that provides file, object, and database access to the same underlying data through a single platform. Instead of maintaining separate NFS filers, object stores, and databases, VAST presents one pool of storage that supports multiple protocols simultaneously.
For AI workloads, this means:
- Training data stored once, accessible as files for training and as objects for data pipelines
- Model checkpoints written as objects, queryable as database records for experiment tracking
- Vector embeddings stored in VAST's built-in database, searchable for RAG without a separate vector database
- No data movement between systems — everything lives in one place, accessible through whatever protocol the application needs
Disaggregated Shared-Everything Architecture
Traditional storage systems are either shared-nothing (each node has its own data, limiting flexibility) or shared-everything (all nodes access all data but with bottlenecks). VAST uses a disaggregated shared-everything architecture that separates compute and storage into independent tiers that can scale independently, while maintaining the performance benefits of shared-everything access.
In practice, this means you can add storage capacity without adding compute, or add compute performance without buying more storage. For AI workloads that are bursty — periods of intense data reading during training alternating with quieter periods during evaluation — this flexibility translates directly into cost savings.
NVMe-over-Fabrics and All-Flash Architecture
VAST is built entirely on flash storage (SSDs), not spinning hard drives. This is a fundamental architectural choice that enables the throughput and latency characteristics AI workloads demand. Combined with NVMe-over-Fabrics (NVMe-oF) networking, VAST can deliver data to GPUs at near-local-SSD speeds over the network, eliminating the traditional tradeoff between local storage performance and shared storage flexibility.
Why This Matters for AI Workloads
Training Data Management
AI training datasets are large, growing, and need to be versioned, filtered, and served at high throughput. A unified storage system simplifies training data management by providing a single location where data scientists can store, query, version, and serve training data without managing multiple systems. VAST's database capabilities mean you can filter and query training data by metadata (date, source, quality score, content type) without maintaining a separate metadata database.
Model Checkpointing
Model checkpointing — saving the model's state at regular intervals during training — is essential for recovering from failures in long-running training jobs. A training run that costs $1 million in GPU time and fails without a recent checkpoint can waste hundreds of thousands of dollars. Checkpointing requires writing hundreds of gigabytes (for large models, terabytes) of data at regular intervals — often every 5-15 minutes — without slowing down the training process.
VAST's write performance is specifically optimized for this pattern: large sequential writes that need to happen quickly and reliably. The platform can sustain checkpoint writes at speeds that keep the GPU pipeline moving without pause, while also maintaining the historical checkpoints needed for experiment tracking and rollback.
Retrieval-Augmented Generation (RAG)
RAG — the technique of searching a knowledge base to provide context for language model responses — requires fast vector search across potentially massive datasets. Traditional approaches use a separate vector database (Pinecone, Weaviate, Qdrant) that must be loaded, indexed, and maintained independently from the source documents. VAST's built-in database capabilities with vector search support mean you can store the source documents, their embeddings, and their metadata in the same system, eliminating the complexity of synchronizing data between a document store and a vector database.
Inference Caching
For inference workloads, caching frequently accessed data (model weights, common embeddings, popular query results) is critical for latency and cost. VAST's all-flash architecture provides the consistent low-latency access needed for effective caching, while its scale-out design supports the aggregate throughput needed when serving multiple inference endpoints simultaneously.
How Founders Should Think About Storage
If you are building an AI product, storage decisions that seem boring today will become strategic tomorrow. Here is a framework for thinking about storage at different stages of your company.
Pre-Seed to Seed: Use Managed Services, but Plan Ahead
At the earliest stage, use S3 or GCS for data storage and managed databases for structured data. Do not over-engineer your storage stack. But make two important decisions now:
- Use standard protocols (S3-compatible APIs, standard SQL) so you can migrate later without rewriting your entire data layer
- Track your storage costs from day one — many startups are surprised when storage becomes a top-3 expense line item
Series A: Evaluate Your Storage Architecture
At Series A, you likely have enough data and enough GPU usage that storage efficiency starts to matter. This is the time to evaluate whether your storage architecture can scale with your growth plan. Key questions:
- How much time are your GPUs spending waiting for data versus processing data?
- How many separate storage systems are you managing (object store, file system, database, vector store)?
- What percentage of your infrastructure budget goes to storage?
- Can your current architecture support 10x data growth without fundamental changes?
Series B and Beyond: Storage as Competitive Advantage
At scale, storage architecture becomes a competitive advantage. Companies with efficient, high-throughput storage systems can train models faster, serve inference at lower latency, and manage larger datasets at lower cost than competitors with legacy storage architectures. This is where purpose-built AI storage solutions like VAST Data, Weka, and others provide genuine strategic value.
Alternative Solutions: The AI Storage Landscape
VAST Data is not the only company building storage for AI workloads. Understanding the alternatives helps frame where VAST fits and what tradeoffs each option involves.
WekaIO (Weka)
Weka provides a high-performance parallel file system designed for AI and HPC workloads. Its strength is raw throughput performance — it consistently benchmarks among the fastest storage systems for large sequential reads. Weka is popular in GPU cloud environments and with companies that need maximum training data throughput. The tradeoff versus VAST is that Weka focuses on file storage and does not provide the unified file/object/database approach that VAST offers.
DDN
DDN (DataDirect Networks) has decades of experience in high-performance computing storage. Its AI-focused products (AI400X, EXAScaler) are deployed at some of the world's largest supercomputing facilities. DDN's strengths are proven scale, deep HPC expertise, and strong relationships with research institutions. It is often chosen by organizations with existing HPC infrastructure that are adding AI workloads.
NetApp
NetApp has evolved its traditional enterprise storage platform to support AI workloads, with AFF (All Flash FAS) arrays and ONTAP software providing file and object access with enterprise features (snapshots, replication, data protection). NetApp's advantage is enterprise integration — organizations with existing NetApp deployments can extend their infrastructure to support AI without introducing new storage vendors.
MinIO
MinIO provides an open-source, S3-compatible object store that many AI teams use for training data and model artifacts. MinIO's strengths are simplicity, S3 compatibility, and cost-efficiency for large datasets that do not require extreme throughput. It is often used alongside a high-performance file system — MinIO for data lake storage, Weka or VAST for the active training data tier.
Cloud-Native Options
AWS FSx for Lustre, Google Cloud Filestore, and Azure Managed Lustre provide managed high-performance file systems in the cloud. These are convenient for cloud-native AI workflows but typically cost more per GB than dedicated solutions and may not provide the same peak throughput.
When Storage Becomes a Competitive Advantage
For most startups, storage is a commodity — you use S3, maybe add a managed database, and do not think about it much. But there is a threshold where storage architecture becomes a genuine competitive advantage. That threshold is defined by several factors:
- Dataset size: When your training data exceeds 100TB, storage choices start to significantly impact training time and cost
- Training frequency: If you retrain models weekly or daily, the time savings from faster storage compound rapidly
- Multi-modal data: If you work with images, video, audio, and text, unified storage that handles all data types reduces complexity
- RAG at scale: If your inference pipeline searches across millions of documents, storage latency directly impacts user experience
- Compliance requirements: Regulated industries (healthcare, finance, government) have data residency and audit requirements that purpose-built storage simplifies
If none of these apply to you today, you do not need to worry about storage architecture yet. But if several of them apply, or if you can see your growth trajectory heading in this direction, investing in the right storage foundation now will save significant pain and cost later.
The TBPN Perspective on Infrastructure
The Technology Brothers Podcast Network has consistently argued that the most important AI infrastructure decisions are not the most visible ones. Everyone talks about GPU selection. Few people talk about the storage, networking, and data management decisions that determine whether those GPUs actually deliver value. VAST Data, Weka, and the broader AI storage category represent the kind of infrastructure investment that does not make headlines but determines which companies can train faster, serve cheaper, and scale more efficiently.
For the TBPN community — builders, founders, and investors navigating the AI infrastructure landscape — understanding storage is no longer optional. It is the difference between infrastructure that scales and infrastructure that breaks.
Stay sharp on infrastructure trends with the TBPN daily show, 11 AM to 2 PM PT on YouTube and X. Grab a TBPN hoodie for those data center visits, a TBPN tumbler for the long architecture review sessions, and TBPN stickers for your server rack (or laptop). Check out the full t-shirt collection and hats at the TBPN merch store.
Frequently Asked Questions
At what scale should a startup start thinking about purpose-built AI storage?
The rough threshold is when your training data exceeds 50-100TB and you are running GPU training jobs regularly (weekly or more frequently). Below this scale, S3 or GCS with standard caching strategies is usually sufficient. Above this scale, the gap between commodity storage performance and purpose-built AI storage starts to translate into meaningful differences in training time, cost, and operational complexity. Some companies hit this threshold within months of starting serious model training; others operate for years without reaching it. Track your GPU utilization — if your GPUs are spending more than 10-15% of their time waiting for data, you have a storage bottleneck worth addressing.
How does VAST Data pricing compare to S3 or standard cloud storage?
VAST Data is more expensive per GB than S3 on a pure storage-cost basis. S3 Standard costs approximately $0.023 per GB per month, while VAST's effective cost is higher (pricing varies by deployment and is not publicly listed). However, the comparison is misleading because VAST provides dramatically higher performance. The right comparison is total infrastructure cost: if VAST's throughput reduces your training time by 30%, the GPU cost savings likely exceed the storage premium. Think of it as paying more for storage to pay less for compute — the total cost of ownership often favors high-performance storage at scale.
Can I use VAST Data in the cloud, or is it on-premises only?
VAST Data supports both on-premises and cloud deployments. Their cloud offering runs on major cloud providers, and they also support hybrid configurations where on-premises VAST storage is connected to cloud-based GPU compute. For startups, the cloud deployment option removes the need for upfront capital expenditure on storage hardware. For larger organizations with existing data center infrastructure, on-premises deployment provides maximum performance and cost control.
What is the difference between VAST Data and just using a really fast parallel file system like Lustre?
Lustre (and its managed cloud variants like FSx for Lustre) is a high-performance parallel file system that excels at large sequential reads — exactly what AI training needs. VAST Data provides similar file system performance but adds unified object storage, database capabilities, and vector search in the same platform. If your only need is fast file reads for training, Lustre may be sufficient and simpler. If you also need object storage for data pipelines, database queries for metadata, and vector search for RAG — all on the same data — VAST's unified approach eliminates the need to manage and synchronize multiple storage systems.