AI Storage vs. Legacy Storage: A Migration Guide for the AI Era

Why legacy NAS, SAN, and Object storage fail under petabyte-scale GPU workloads — and how to evaluate parallel file systems for seamless migration.

There’s nothing wrong with legacy enterprise storage, as long as you’re using it for legacy enterprise reasons. Problems only arise when you start trying to use it for AI workloads.

While legacy storage architectures like NAS, SAN, and Object Storage all solve important problems related to sharing files and accessing data, none of them are equipped for the unique needs of AI. This isn’t a shortcoming, but a stark reminder of just how fundamentally different AI works. In the same way AI is disrupting how we use memory and design GPUs, it is also changing how we architect storage systems. Understanding this change is an essential step toward optimizing AI at your organization.

Here’s a closer look at how legacy storage differs from AI storage, as well as a technical breakdown of the different approaches to AI storage architectures, so that you can plan your migration in a way that won’t disrupt your existing operations.

The Three Legacy Architectures

The dominance of AI means that legacy storage architecture tends to get left out of the conversation these days. But they’re still a vital aspect of our computing and enterprise systems, not to mention a starting point for our AI stacks. This makes understanding these architectures – including both what they’re built for and what they’re not – an important part of your AI migration strategy.

Network Attached Storage (NAS)

As its name suggests, NAS is a dedicated file server that connects to a computer network. It’s composed of dedicated hardware (storage disks and drives, processors, and memory) and a basic operating system embedded on that hardware that manages data transfer protocols. 

This straightforward system provides a simple, cost-effective way to share and access files across a network. Because of this, it’s ideal for small numbers of users with sequential workloads like office documents and media. However, the cost and complexity of scaling NAS can make it less effective for high-performance or massively parallel demands.

Storage Area Network (SAN)

Compared with NAS, SAN offers faster performance and greater flexibility by connecting multiple storage devices and servers across a specialized network. The data on these devices is pooled using block-level access, which makes it possible to pull information from anywhere as if it existed on local storage. 

The high speed and availability of SAN storage makes it a great fit for workloads requiring high IOPS and low latency, such as databases and virtual machines. That said, its complexity can make the cost of setting up and maintaining SAN infrastructure a significant drawback.

Object Storage

This is a type of storage architecture that stores and organizes data into discrete, unstructured units called objects. Each of these objects contains data, metadata, and a unique identifier that makes it possible to place them across a flat, efficient, and highly scalable system known as a storage pool.

This rich metadata and scalability makes object storage systems particularly well suited for environments that contain large amounts of unstructured data, such as archival systems and media libraries. However, because you’ll need to rewrite an entire object in order to modify it, this storage type is less effective for high-performance applications and dynamic data.

Where Legacy Storage Breaks for AI Workloads

Although each of the three legacy storage types may have their distinct advantages, the demands of AI introduce an entirely new set of requirements. And it’s not just the type of I/O that’s changed; it’s the sheer volume of data behind it. AI datasets are routinely measured in petabytes and growing toward exabytes, a scale that most legacy architectures were never designed to handle. At that size, every architectural limitation compounds: a metadata bottleneck that’s manageable at terabyte scale becomes crippling at petabyte scale, and a workaround that works for one training run falls apart when you’re running dozens in parallel. In many cases, AI workloads break the assumptions each architecture was designed around.

Consider NAS. While its simple architecture may streamline its management, this also means it typically relies on only one controller to direct file traffic. This can work fine when requests are limited to around 10,000 to 100,000 per second (the typical range for NAS), but AI training often needs to make requests in the millions per second. No matter how fast the data transfer speeds are, the rate at which this one controller can read and write metadata will become the bottleneck.

SAN introduces a more fundamental challenge. Because SAN uses block-level access, it doesn’t deal in files. Instead, it delivers storage up in raw blocks that the attached server then organizes into filesystems. Although this process is fast, it doesn’t provide any way for hundreds or thousands of GPU nodes to access the same files and datasets. In order to get around this, organizations typically have to create an additional shared filesystem layer on top of SAN storage.

While this approach enables coordination across nodes, it also introduces performance and scalability challenges. Similar to NAS, the massive amounts of requests and metadata operations AI training requires can push the boundaries of traditional SAN architecture. Even more significantly, the huge bursts of I/O activity that AI training produces are substantially different from the steady, predictable workloads SAN environments are designed for. As this training scales, this can result in bottlenecks that limit performance.

Finally, there’s object storage. Alongside the file-based architecture of NAS and the block-based architecture of SAN, this storage type may offer the most promising foundation for AI due to its ability to handle large amounts of data and its high scalability. However, limitations remain. Object storage systems like S3 typically use a REST-based API, which can add a few milliseconds of latency per operation. Combine that with the millions of operations per second that AI training jobs require and that can produce impactful delays.

And training isn’t the only workload exposing these gaps. AI inference brings an entirely different access pattern that legacy storage cannot handle:

• KV Cache Offload: Requires microsecond-latency random reads to swap context in and out of GPU memory without stalling compute.
• RAG & Vector Retrieval: Hits storage with constant, unpredictable small-read bursts as models fetch context for every query.
• Agentic Workloads: Continuously reads and writes small, hot contexts in real time as autonomous agents reason across multi-step execution paths.

None of these patterns look anything like the sequential, predictable I/O that legacy storage was built for — and unlike training, they are always on.

Possibly a bigger issue is how objects cannot be edited or modified but must be re-uploaded in their entirety. This poses a major obstacle for checkpointing – the ability of a model to save its current state during training so it can resume it later on. Whereas a file-based or block-based storage system could modify an existing dataset to create a checkpoint, object storage must write an entirely new object. This creates additional storage overhead and network traffic, which can become significant when hundreds of nodes or thousands of GPUs are involved.

Quick Comparison Guide: NAS vs. SAN vs. Object Storage

	NAS	SAN	Object Storage
How it works	Dedicated file server on a network; single controller manages file traffic	Specialized network pooling multiple storage devices; block-level access across servers	Flat namespace of discrete objects, each with data, metadata, and a unique identifier
Best for	Small user groups with sequential workloads (documents, media)	High-IOPS, low-latency workloads (databases, VMs)	Large-scale unstructured data (archives, media libraries)
Metadata throughput	10K–100K ops/sec	Depends on added filesystem layer	REST API adds ms-level latency per operation
AI breaking point	Single controller becomes a metadata bottleneck at millions of ops/sec	No native shared file access across GPU nodes; requires an additional filesystem layer that can’t handle bursty I/O	Objects can’t be modified in place — checkpointing requires full rewrites, compounding storage overhead at scale
Root cause	Architecture assumes one traffic director is enough	Architecture assumes steady, predictable workloads	Architecture assumes data is written once and read many times

Although the specifics of how each storage type falls short for AI workloads may be different, the theme is the same: none of them were designed to handle the random small reads, massive sequential writes, and low latency demands that AI requires. Introducing more bandwidth won’t fix this. Only a new architecture will.

What AI Storage Architecture Looks Like

How do you manage huge datasets and extreme I/O patterns while maintaining performance? The answer is by rethinking how storage works and how it needs to scale for AI workloads that require more memory. If traditional architectures are built for predictable and consistent workloads, then the storage architectures for AI must be able to deliver low latency and high throughput while remaining flexible in the face of real-time data needs. Here’s what this looks like.

Parallel File Systems

Instead of directing all file requests through a single node, parallel file systems distribute data across multiple nodes at once. It does this by breaking files up and striping data (splitting it into equal-sized blocks or chunks) across servers, then allowing compute nodes to read and write different parts of that file at the same time. 

The key benefits of this system are its high performance and scalability. By distributing requests, it can handle thousands of concurrent read and write jobs without degradation. Additionally, adding more nodes not only increases capacity, but also increases performance, allowing both to grow linearly as you scale up. Even for datasets in the exabyte range, this approach means you won’t run into bottlenecks.

NVMe-native

Non-Volatile Memory Express (NVMe) is the gold standard when it comes to data transfer storage speeds. Designed specifically for SSD storage, it takes advantage of the PCI Express protocol to deliver speeds far faster than conventional protocols like SAS and SATA. This has made it a sought-after storage type, which is why it’s become common to add in NVMe drives and compatible interfaces on top of SAS/SATA drives. 

But while this may look like NVMe, it won’t perform like NVMe – and AI systems can tell. Requests will still funnel through the controller logic and pathways made for the older SAS/SATA protocols, limiting read/write performance capabilities to the surrounding architecture. Instead, AI storage demands NVMe-native architecture, meaning that the entire system (controller design, internal data paths, queuing, software) is designed for flash and NVMe all the way through so that it can deliver the fastest concurrent request rates possible.

GPUDirect Storage and RDMA

NVIDIA’s GPUDirect Storage, built on Remote Direct Memory Access (RDMA) technology, creates a direct data path between the NVMe flash storage and the GPU memory. This solves an important efficiency problem. Normally, when a GPU needs data from a storage device, that data has to take an indirect path. It first has to transfer over to the server’s regular system memory, then take another step into the GPU memory.

Individually, the cost of this is small – but in aggregate it can be very costly. First off, because the CPU is in charge of moving data from system to GPU memory, that can create a bottleneck as the data adds up. Second, the zig-zagging path from storage to system memory to GPU eats up both time and bandwidth, adding latency with every additional request. At scale, all this can become damaging. But by cutting out the CPU entirely from this process and simplifying the path to the GPU, GPUDirect Storage helps keep storage throughput high.

Distributed Metadata

Metadata is all the information about a file, such as its name, size, permissions, and so forth. In legacy storage architectures like NAS, this information is processed through a single controller called the “master node.” For smaller sequential workloads, this isn’t an issue. But for the huge number of requests that come through an AI system, this configuration will turn into yet another bottleneck.

The solution is to transfer metadata responsibilities from a single physical server to a series of virtual metadata servers. As part of the software layer, these virtual servers can be created, multiplied, and shrunk as needed. And because they lack a physical location, they can be reassigned to wherever they’re needed most. This flexibility makes it possible for an AI storage system to handle billions of files while maintaining sub-millisecond response times.

Multi-protocol

Different tools and different stages of an AI pipeline are built around different protocols. This can include S3 objects for data ingestion, POSIX file calls for loading data into training frameworks, NFS mounts for older infrastructure, and GPUDirect for transferring data from storage to the GPU as quickly as possible. If your architecture only speaks one of these protocols, then you’ll have to copy data into the correct format before it can be read – which can quickly turn costly at scale.

Multi-protocol access changes this. Instead of forcing you to pick one protocol, your storage system can access its underlying data via either POSIX, S3, NFS, or GPUDirect simultaneously. A data engineering tool can read data as S3 objects, while at the very same time a training job can read the same files via POSIX, and the GPU pulls from it via GPUDirect for maximum speed. No duplicates are made and no silos are created. Instead, data moves across the AI storage architecture frictionlessly.

While there may be no such thing as an ideal AI storage architecture, WEKA NeuralMesh™ uses all of the above principles to create a unified, software-defined storage solution designed specifically for the demands of AI training and inference. Read more about NeuralMesh’s design and how it’s engineered for enterprise AI. 

Migration Paths — Meeting Enterprises Where They Are

Migrating to AI-ready storage doesn’t have to be an all-or-nothing process. As in any migration, the goal shouldn’t just be to move data, but to do so with minimal downtime or interruption. Accomplishing this, while also successfully transitioning to an architecture that can meet your AI demands, requires a balance between your current enterprise capabilities and your AI needs.

With that in mind, here are three paths you can take to migrate your legacy workloads to AI storage.

Path 1: Parallel Deployment

This involves building a new AI storage system alongside your NAS/SAN legacy architecture. Any existing workloads stay right where they are, while new AI workloads begin using the new storage from day one. In many ways, this path actually isn’t a migration at all, as nothing on the legacy side actually gets moved. Instead, you’ll end up running two separate environments side by side rather than one unified system.

This path can be a great fit for organizations just starting out with AI, or those with legacy workloads that are business-critical or compliance-bound. The low risk it offers also makes it a great option for organizations that are still testing out their AI initiatives but may not have the budget or buy-in for bigger infrastructure change. Of course, a trade off of this approach is that you won’t get any of the larger benefits of consolidation. You’ll effectively be operating two silos and any data that needs to be moved from legacy to AI storage will have to be copied.

Path 2: Tiered Architecture

Instead of building an entirely independent architecture, this path takes a more incremental approach by organizing data according to how “hot” (active) or “cold” (inactive) it is. Hot data (which includes any datasets currently being used to train AI models) lives on high-performance AI storage, while less active data (such as older datasets and archives) transfers into legacy storage. Rather than someone manually managing this movement, this movement happens automatically via pre-set rules, such as how recently or often data has been accessed. This helps keep the most relevant data on the fastest-tier of storage.

Organizations further along in their AI journey, especially those that have already accumulated a lot of training data, stand to benefit most from this approach. That’s because, by not treating all data as equally important, this path offers a good balance between cost and performance. That said, its success largely depends on the automated rules actually working. If “hot” data is accidentally categorized as “cold,” it could end up causing significant delays.

Path 3: Full Consolidation

This is the path for organizations ready to fully embrace AI storage architecture. Instead of trying to balance two different types of storage, full consolidation uses a single unified platform with multi-protocol access that can read and write data regardless of format. So no matter whether a dataset requires POSIX, S3, NFS, or GPUDirect, you’ll have the architecture in place to quickly and efficiently feed that data into your AI system.

Not only is this best suited for organizations interested in making AI a permanent part of their business model, but it’s also great for those looking to avoid the cost of running multiple disconnected legacy systems. However, in order to make this happen, this path does involve more effort than the other approaches in large part due to the time and planning associated with migrating all of your existing workloads and data off of legacy systems.

Additional Considerations

How and to what extent you migrate your workloads isn’t the only factor to consider. You should also be aware of how you plan on protecting your data and whether you can ensure equal performance regardless of the data’s location.

Data protection

Lost AI training data isn’t just an inconvenience. It also means dealing with the expense of idle compute. Although AI storage typically has protection capabilities like snapshots (point-in-time copies of your data’s state), replication (continuous copies of data), and erasure coding (splitting data into fragments so that it can be reconstructed later on), these often exist as external features that may have certain limitations, such as limits on dataset size. 

While traditional backup processes, such as disaster recovery, may offer an alternative for those organizations retaining their legacy storage architecture, these won’t be feasible as workloads reach petabyte or exabyte ranges. Instead, it may be necessary to consider options like NeuralMesh snapshot that offer native AI-ready data protection.

Hybrid and multi-cloud

Whatever migration path you choose, you’re unlikely to limit available GPUs to just one environment. Instead, you’ll want to take advantage of every GPU you have, whether they’re located on-prem, across clouds, or in hybrid spaces. This means that you’ll need to ensure that, regardless of where those GPUs are located, your storage architecture will be able to deliver the same performance.

Why? Because if you don’t, you’ll end up introducing yet another bottleneck. For example, if your AI workloads only work well with on-prem GPUs, but degrade in quality once you incorporate public cloud or hybrid workloads, then you’ve effectively reintroduced the same problem you were trying to solve when you migrated from legacy to AI storage architecture in the first place. This is why it’s so important to choose AI storage options that are capable of performing well across environments and their underlying infrastructure.

Evaluation Checklist: What to Ask When Evaluating AI Storage

Q1: Can the architecture handle small random reads and large sequential writes simultaneously?

A: AI workloads feature bimodal I/O patterns. Modern AI storage requires parallel file systems to manage thousands of concurrent, mixed read/write jobs without performance degradation.

Q2: Does metadata scale linearly, or does a master node create a bottleneck?

A: Legacy architectures rely on a single master node that bottlenecks at ~100K ops/sec. AI storage requires distributed metadata across virtual metadata servers to sustain millions of requests at sub-millisecond response times.

Q3: Does it support GPUDirect Storage (GDS) and RDMA for zero-copy GPU data paths?

A: Bypassing the host CPU via GPUDirect Storage and RDMA eliminates data-copying overhead, cutting latency and maximizing GPU memory throughput during training and inference.

Q4: Can it serve POSIX and S3 protocols on the same data concurrently without copying?

A: Multi-protocol support prevents data duplication and silos, allowing S3 object ingestion, POSIX training loading, and GPUDirect transfers to access the exact same underlying dataset simultaneously.

Q5: What is the migration path from existing legacy storage?

A: Flexible migration strategies—such as parallel deployment, tiered hot/cold architectures, or full platform consolidation—allow organizations to transition to AI-ready storage with minimal downtime or operational disruption.

Q6: How does data protection work without impacting system performance?

A: Enterprise AI storage requires native, low-overhead data protection mechanisms like snapshots, replication, and erasure coding that safeguard petabyte-to-exabyte scale datasets without degrading GPU throughput.

Get a full evaluation criteria for AI storage, including an evaluation scorecard you can use during your assessments, in our Buyer’s Guide to AI Storage.