WEKA vs VAST Data: AI Storage Platform Comparison

WEKA and VAST Data can both supply shared storage for demanding AI and HPC environments, but they reach that goal through different system designs. WEKA is a software-defined, distributed parallel file system built around NVMe storage and high-performance clients. VAST Data separates stateless service nodes from a shared pool of persistent media through its Disaggregated Shared Everything, or DASE, architecture, then presents file, object and block services as part of a broader data platform.

That distinction should guide the first shortlist. WEKA is a natural candidate when the project centres on a high-performance POSIX data path, direct GPU access and deployment flexibility across qualified server or cloud infrastructure. VAST deserves close examination when the requirement includes a large shared namespace, multi-protocol consolidation and independent scaling of storage service processing and capacity.

Neither description proves that one platform will be faster, cheaper or easier for a particular cluster. The decision requires a proof of concept using the buyer's datasets, client count, checkpoint pattern, protocols, network and failure tests.

WEKA vs VAST Data at a glance

| Decision area | WEKA | VAST Data | What to verify | | --- | --- | --- | --- | | Core design | Distributed parallel file system deployed as software across NVMe-based infrastructure | DASE architecture with stateless CNodes accessing shared persistent media over NVMe-oF | Which design matches the team's hardware, expansion and support model | | Primary AI data path | Native client options, POSIX access, RDMA and NVIDIA GPUDirect Storage in supported environments | Scale-out file and object access through service nodes, with a shared all-flash back end | Sustained throughput and tail latency from the intended GPU hosts | | Access methods | POSIX, NFS, SMB, S3 and Kubernetes CSI are documented by WEKA | NFS, SMB, S3 and NVMe/TCP block services are documented by VAST | The exact protocol versions, client OS support and feature behaviour required | | Deployment approach | Software-defined deployment on qualified bare-metal, virtual or cloud resources; converged and dedicated patterns are possible | Software-defined platform commonly deployed with CNodes and shared storage enclosures or supported server designs | Failure domains, minimum starting configuration and upgrade path | | Data services | File systems, snapshots, clones, quotas, tiering and object integration | DataStore plus optional database, data-engine and global data-space services | Which features are licensed, mature and needed on day one | | Scaling question | How performance, capacity and client services change as WEKA processes and NVMe resources are added | How CNode service capacity and persistent media capacity are expanded independently | Non-disruptive expansion, mixed hardware generations and operational limits |

This table describes architecture, not a benchmark result. Product releases, qualified hardware and licensed features change, so the final design must use current vendor documentation and a written acceptance plan.

How WEKA is built

WEKA describes its platform as a software-defined, distributed parallel file system. Its back-end processes handle data placement, protection, metadata and tiering across NVMe resources. Client access can use a high-performance WEKA client as well as standard protocols for systems that cannot run that client.

The architecture is designed to parallelise both data and metadata work rather than route every request through a fixed pair of storage controllers. WEKA documents kernel-bypass client modes, RDMA support and NVIDIA GPUDirect Storage. In a supported GDS path, data can move between storage and GPU memory without an extra copy through host memory. That can reduce CPU involvement, but it only helps when the GPU, NIC, PCIe topology, driver, client mode and application are all compatible.

WEKA can be deployed on dedicated storage servers, in cloud instances, or in a converged pattern that uses resources in application hosts. Those options are not interchangeable. A converged design shares CPU, memory, NVMe and network resources with the workload. A dedicated design consumes extra rack space and power but gives the storage layer clearer resource ownership. The proof of concept should reproduce the intended deployment model rather than testing a laboratory configuration that will not be purchased.

WEKA also supports tiering to object storage. This can extend the accessible namespace beyond the flash tier, but the active working set, recall behaviour and object-store performance still need measurement. Tiering does not turn a slower capacity layer into NVMe.

How VAST Data is built

VAST's DASE architecture separates the system's service processing from its persistent media. Stateless CNodes run the protocol and data-service logic. Shared storage enclosures, commonly described as DBoxes in VAST material, hold NVMe media and persistent system state. CNodes access that shared state through an NVMe-over-Fabrics data path.

The shared-everything model is important. VAST states that each CNode can access the system's persistent data and metadata rather than owning a private shard that must be coordinated with peer nodes. Service capacity can therefore be expanded by adding CNode resources, while media capacity can follow a different growth schedule. Buyers should ask for the supported scaling increments, network port counts and failure-domain rules for the proposed generation of hardware.

VAST DataStore presents file, object and block services. Current VAST material also places DataStore inside a wider platform that can include DataBase, DataEngine and DataSpace functions. Those services may be relevant to organisations that want storage, tabular metadata, event processing or multi-site data access under one platform. They should not be assumed to be part of every storage quote. Establish which components are included, how they are licensed and who will operate them.

VAST's design uses a persistent write buffer and data-reduction structures to combine low-latency writes with high-capacity flash. That is an architectural claim worth testing against the buyer's real checkpoint sizes, overwrite rate, retention policy and recovery load. A reduction ratio from one dataset should not be applied to another without measurement.

The practical architectural difference

The clearest comparison is not "parallel file system versus storage array". Both platforms distribute work and serve large client populations. The more useful distinction is where services run, how clients reach the data and how the system grows.

With WEKA, the high-performance file client and its allocated host resources are part of the design. Client mode, reserved CPU cores, networking and GDS compatibility can influence results. With VAST, the number and placement of CNode services, their front-end network capacity and the shared NVMe fabric are central sizing variables.

This affects operations as well as speed. A team comfortable managing software-defined storage on its chosen server hardware may value WEKA's deployment flexibility. A team seeking a more appliance-like shared data service may prefer a VAST design supplied and supported as an integrated configuration. Actual support boundaries depend on the quote, OEM and deployment method, so capture them in writing.

GPU data paths and network design

AI storage performance is a networked-system property. A fast storage cluster cannot feed GPU hosts through an oversubscribed switch, an unsuitable NIC topology or a client that is limited by host CPU copies.

For WEKA, confirm whether the proposed GPU hosts will use the native client, RDMA and GPUDirect Storage. WEKA's documentation says supported environments can use RDMA and GDS automatically for suitable I/O. The design still needs validated NICs, a compatible software stack and enough PCIe bandwidth between the GPUs, network adapters and CPUs.

For VAST, size the front-end file or object network independently from the internal NVMe fabric. Confirm how many client-facing ports each service node supplies, how traffic is balanced and what happens when a node or link is lost. If the application requires GPUDirect Storage, ask VAST and the server vendor to identify the supported client path and release combination rather than assuming that an RDMA-capable network is sufficient.

The network choice should follow the workload:

• Large sequential reads matter for training jobs that stream large samples or pre-packed datasets.
• Small-file and metadata work matters for unsharded image collections, source trees and pipelines that repeatedly scan directories.
• Checkpoint writes create bursts and can collide when many workers save at the same interval.
• RAG and inference services may combine object reads, model loading, vector or database calls and KV-cache movement.
• Ingest and transformation can be write-heavy even when the final training stage is read-heavy.

Use separate traffic classes or fabrics when a shared network cannot meet storage, compute and management requirements at the same time. The GPUMachines Ethernet cluster designs and InfiniBand cluster designs provide starting points, but switch and optic quantities must be calculated from the selected servers and oversubscription target.

Which workloads favour WEKA?

WEKA should be on the shortlist when several of these statements are true:

• The main requirement is a high-performance shared POSIX namespace for training, simulation, rendering or HPC.
• GPU hosts can use a qualified native client and the team wants to evaluate RDMA or GPUDirect Storage.
• The organisation wants software that can run on qualified server infrastructure or in supported public-cloud environments.
• Active data sits on NVMe while colder data can move to an object tier under a defined policy.
• The operations team is prepared to plan client resources, storage processes, failure domains and software upgrades.

These conditions are not proof of fit. For example, a high headline throughput figure does not answer how the system handles millions of small files or a simultaneous checkpoint storm. Test those patterns explicitly.

Which workloads favour VAST Data?

VAST should be on the shortlist when several of these statements are true:

• A large flash-backed namespace must serve NFS, SMB, S3 or block consumers as well as AI clients.
• The buyer wants to scale client-service processing separately from persistent capacity.
• Consolidating several data-access patterns is more important than optimising only one parallel file workflow.
• Data reduction, snapshots, multi-tenancy and long-lived enterprise data are part of the same storage requirement.
• The wider VAST data-platform services have a defined owner and a concrete application in the project.

Again, validate the exact requirement. A broad protocol list is valuable only if the required semantics, identity mapping, locking and performance remain correct when the same data is accessed in more than one way.

Questions that expose the real requirement

Before comparing products, collect workload evidence:

1. How much data is active, warm and retained, and how quickly does each tier grow? 2. What are the common and worst-case file sizes? 3. How many files, directories and objects are created per day? 4. How many GPU and CPU clients run concurrently? 5. What throughput must one host and the whole cluster sustain? 6. What are the acceptable median and 99th-percentile read and write latencies? 7. How large are checkpoints, and how many jobs may write them at once? 8. Which protocols and operating systems are mandatory? 9. Does the application have verified GPUDirect Storage support? 10. What happens to running jobs when a drive, service node, switch or site fails? 11. Which team owns storage updates, monitoring, capacity and incident response? 12. What rack power, cooling, port count and floor-space limits apply?

A vendor sizing exercise without these inputs can produce a technically valid system that solves the wrong bottleneck.

A fair proof-of-concept plan

Run both candidates with the same client servers, NICs, switches, dataset and application release where possible. Record configuration details so a result can be reproduced.

1. Establish the baseline

Measure the current system using real jobs. Capture GPU utilisation, storage throughput, IOPS, metadata operations, CPU use, network counters and job completion time. A new platform cannot be judged against a vague complaint that the old storage is slow.

2. Test the data shapes

Include large sequential reads, mixed read/write activity, small-file traversal, directory creation, object access and checkpoint bursts in the proportions seen in production. Synthetic tools are useful for isolation, but they do not replace a full training or inference run.

3. Increase concurrency

Test one client first, then the planned rack and cluster concurrency. Record whether throughput scales and whether tail latency rises sharply. A platform that saturates one host may still struggle when hundreds of workers reach the same directory or object prefix.

4. Exercise failures

Remove a client link, service process or permitted hardware component under vendor supervision. Measure I/O interruption, application behaviour, rebuild load and time to restore protection. Repeat the performance test during recovery. Normal-state speed is only part of the service level.

5. Test operations

Create snapshots, restore data, expand capacity, apply quotas and run the monitoring workflow. Verify identity integration and audit requirements. Ask the people who will operate the system to perform these tasks rather than leaving the proof of concept entirely to the vendor team.

6. Measure cost against useful work

Compare the complete supported design: storage nodes, media, network ports, switches, optics, licences, support, rack power and the staff time needed to operate it. Relate that cost to completed training steps, served tokens, recovered checkpoints or another workload measure. Raw capacity price alone omits the reason the storage exists.

Common mistakes in a WEKA vs VAST evaluation

• Comparing a tuned vendor benchmark with an untuned production workload.
• Testing only large sequential I/O when production contains many small files.
• Treating usable capacity and data-reduction estimates as guaranteed outcomes.
• Ignoring the CPU and memory reserved by storage clients or service processes.
• Counting RDMA-capable hardware as proof that GPUDirect Storage will work.
• Omitting failure and rebuild traffic from the acceptance test.
• Buying a multi-protocol platform without testing permissions and locking across those protocols.
• Sizing the storage network independently from the GPU cluster topology.
• Assuming a cloud deployment and an on-premises deployment have the same performance or operating model.

How GPUMachines approaches the design

GPUMachines starts with the GPU workload and works back through the data path. The storage platform is assessed alongside GPU nodes, CPU and memory balance, local NVMe, the east-west compute fabric, north-south storage traffic, management access and rack power.

For a new cluster, begin with the GPU cluster configurator. For storage hardware, review the storage server catalogue and the scale-out storage solution. The final bill of materials should follow a measured workload and a vendor-supported architecture, not a generic node ratio.

FAQ

Is WEKA faster than VAST Data?

There is no defensible universal answer. Results depend on client type, protocol, network, file size, concurrency, data protection state and software configuration. Test both platforms with the intended application and record tail latency as well as aggregate throughput.

Does WEKA require GPUDirect Storage?

No. WEKA supports several access methods. GPUDirect Storage is an option for compatible GPU workloads and environments, not a requirement for every deployment. Its benefit must be measured with the selected framework and server topology.

Is VAST only a file-storage platform?

No. VAST DataStore supports file and object access and documents NVMe/TCP block services. VAST also offers wider data-platform components. Confirm which services are included in the proposed configuration and whether the project needs them.

Can either platform replace local NVMe in GPU servers?

Shared storage and local NVMe solve different problems. Local drives can hold temporary data, caches and job scratch space, while shared storage provides common datasets, model repositories and checkpoints. The right split depends on data reuse, failure handling and workflow design.

Should AI storage use Ethernet or InfiniBand?

Either can be appropriate. The answer depends on the storage client, RDMA support, existing compute fabric, required bandwidth and operations standard. A design must include switch ports, optics, cabling, routing or subnet management and a tested failure plan.

What should be fixed before requesting a quote?

Define active capacity, growth, file and object profile, client count, target throughput, protocols, rack location and support expectations. If these are unknown, start with a discovery and proof-of-concept plan rather than a final hardware order.

Technical sources

• WEKA Data Platform introduction
• WEKA networking, RDMA and GPUDirect Storage
• WEKA and Slurm integration guide
• VAST AI Operating System white paper
• VAST DataStore white paper

Vendor documentation describes supported architecture and features. It does not replace application testing, current compatibility matrices or a supplier-approved design.