The three differ in how data is presented to the computers that use it. A NAS shares folders and files over the network, so many machines can open the same data at once: it is the natural starting point for a small AI team. A SAN gives a server what looks like its own raw disk, delivered over a dedicated storage network: fast and predictable, and suited to databases and virtual machines, but not built for sharing files between workstations. Object storage keeps each item, with its descriptive tags, in a flat pool reached through a web-style interface: it scales to billions of items and is the standard format for large dataset collections and cloud-style AI tooling. Most small and mid-sized AI projects need a NAS, sometimes with one of the others added for a specific job.
How do the three compare?
| NAS | SAN | Object storage | |
|---|---|---|---|
| Presents data as | Files and folders | Blocks: a raw disk | Objects with metadata, in buckets |
| Accessed by | NFS, SMB | Fibre Channel, iSCSI, NVMe over fabrics | HTTP-based API, commonly S3-compatible |
| Shared by many machines at once | Yes | Not directly; one server per volume unless clustered | Yes |
| Who manages the file system | The NAS | The server using the volume | None; objects are addressed by key |
| Performance profile | Good general purpose; depends on drives and network | Low, consistent latency | High throughput at scale; higher latency per request |
| Scale | Tens to hundreds of TB per system | Hundreds of TB and up | Petabytes; billions of objects |
| Editing a file in place | Yes | Yes | No; objects are replaced whole |
| Complexity | Low | High: dedicated network and skills | Moderate to high on premises |
| Entry cost | Low | High | Low in cloud; high on premises |
| Typical use | Shared files, home for datasets and models, backup | Databases, virtual machines, clustered servers | Data lakes, archives, cloud-native applications |
Where does a NAS fit in AI?
It is the default for teams up to a few dozen people and a handful of AI machines. Workstations mount shared folders, read datasets and models, write results, and everything is protected with redundancy and snapshots in one place. AI frameworks read ordinary files, so nothing needs adapting. Its limits appear with scale: very large numbers of small files, many GPU nodes reading at once, or links faster than a single NAS can serve. See why your AI server needs a NAS and building a small AI lab.
Where does a SAN fit?
Where a server needs fast, dedicated disk that behaves like a local drive: the database behind an application, the storage pool under a virtualisation cluster, or a file system that a high-performance cluster builds for itself on top of shared block storage. In an AI context that usually means the infrastructure around the AI, such as the virtual machines and databases, not the datasets. A SAN is not the tool for letting five workstations share a folder; that needs a file system on top, which is what a NAS already is.
Where does object storage fit?
- Very large datasets. Billions of images, documents or sensor records, where a folder tree becomes unmanageable.
- Data lakes. Raw data from many sources kept in one pool for analytics and training.
- Cloud-style tooling. Many data and machine-learning tools read and write S3-compatible storage natively, on premises or in the cloud.
- Archives and backup targets, often with built-in versioning and write-once protection.
- Model and artefact registries.
Its trade-offs: objects are read and written whole, so it suits data that is written once and read many times; each request has overhead, so it is poor for a working directory full of small files being edited; and training usually streams data from it or copies it to local flash first.
Which should an AI project choose?
| Situation | Start with |
|---|---|
| One to five AI workstations, datasets up to tens of TB | NAS |
| A private AI assistant over company documents | NAS as the document store |
| Team sharing datasets, models and results | NAS with 10GbE |
| Virtualised servers and databases supporting the AI application | SAN, or the virtualisation platform's own storage |
| Hundreds of TB of training data, growing | Object storage for the data, flash near the GPUs |
| Pipelines built on cloud-native data tools | Object storage |
| GPU cluster with many nodes training in parallel | Parallel file system or all-flash scale-out storage |
| Long-term archive and backup | NAS, object storage or tape |
CALC. A rough way to see where the folder model strains: 50 million files at an average of 200KB is 50,000,000 x 200 / 1,000,000 = 10,000GB, only 10TB of data, well within a NAS for capacity. The difficulty is the count, not the size: listing, backing up and scanning 50 million small files is slow on any file system. That is the point at which packing files into larger archives, or moving to object storage, starts to pay off. The figures are illustrative.
NAS answers who needs to share this. SAN answers which server needs its own fast disk. Object storage answers how do we keep a billion things. Most AI teams are still asking the first question.
Are the lines really that sharp?
No. Many NAS systems can also offer block volumes over iSCSI, and some can present an S3-compatible interface, which is a practical way to try those access methods without new hardware. Many enterprise storage systems serve file, block and object from one platform. And at the high end, AI clusters use parallel file systems and scale-out flash that combine traits of all three. Treat the categories as access methods to choose between, not as boxes you must buy separately. Check what a specific product supports before relying on it.
What matters whichever you choose?
- Fast storage near the GPU. Whatever sits behind it, active data should reach the GPU from local or all-flash storage. See HDD vs SSD for AI datasets.
- A network to match. See 10GbE vs 2.5GbE and, for larger systems, connecting a DGX Station to enterprise storage.
- Backup. None of the three is a backup of itself. See the 3-2-1 backup rule.
- Skills. Choose what your team can run. An unmanaged SAN is worse than a well-run NAS.
Buying in Singapore
NAS: the Synology DS1825+ (S$2,148), the Synology RackStation RS2423+ (S$4,002) or the QNAP TVS-h874T (S$5,405). SAN: the HPE MSA 2060 16Gb Fibre Channel SFF Storage (S$14,436). Capacity drives: Seagate Exos X24 from S$835 (listed 12 Nov 2026; prices change; ask for a current quotation). Dedicated object storage and scale-out systems are not listed in our store; tell us the capacity, access method and number of GPU systems and we will source and quote. To start, use Request for Quotation on the product page, call (65) 6978 3502, email Sales@sourceit.com.sg or use our contact page.
Frequently asked questions
What is the difference between NAS, SAN and object storage? A NAS shares files and folders over the network. A SAN provides block-level volumes that appear to a server as its own disk. Object storage keeps items with metadata in a flat pool accessed through an API.
Which storage is best for AI? For small and mid-sized teams, a NAS, with fast local flash in the AI machines. Object storage suits very large datasets and cloud-native pipelines, and SAN suits the databases and virtual machines around the AI application.
Can AI training read directly from object storage? Yes, many tools support it, usually by streaming data or caching it on local flash first. It suits large write-once datasets better than working directories of small files.
Is a SAN faster than a NAS? A SAN typically offers lower and more consistent latency for a single server's workload. It is not designed for sharing files between many machines, which is what a NAS does.
