W
data

Weka

WEKA is a high-performance data platform for AI and HPC that unifies NVMe flash, cloud object storage, and parallel file access to feed GPUs at scale with enterprise controls.
storage gpu hpc
Beginner Level
By quote
Starting Price
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Category
data
Setup Time
< 2 minutes
data
Category
Beginner
Difficulty
Active
Status
Web App
Type

What is Weka?

Discover how Weka can enhance your workflow

WEKA delivers a software-defined data platform tuned for GPU-accelerated workloads. Its parallel file system sits on NVMe and scales across on-prem and cloud to provide low-latency, high-throughput access for training and inference. Snap-to-object tiering pushes colder data to cloud buckets while metadata keeps hot sets close to compute. Enterprises integrate WEKA with Kubernetes and common schedulers, enforce quotas, and monitor via dashboards and APIs. Security features include encryption, access controls, audit logs, and options for regional residency. Customers adopt WEKA to remove I/O bottlenecks, increase GPU utilization, and simplify data ops for labs and production AI. Licensing is typically subscription-based by usable capacity with private offers through cloud marketplaces and direct enterprise agreements.

Key Capabilities

What makes Weka powerful

Parallel IO

Deliver low-latency, high-throughput access across nodes so training and inference avoid storage stalls.

Implementation Level Professional

Object Integration

Move colder data to object buckets while metadata keeps hot sets near compute under policy control.

Implementation Level Intermediate

K8s & Schedulers

Integrate with Kubernetes and job schedulers for predictable performance in shared clusters.

Implementation Level Intermediate

Governance & Audit

Apply encryption, access controls, and logs with marketplace procurement and regional residency options.

Implementation Level Professional

Professional Integration

These capabilities work together to provide a comprehensive AI solution that integrates seamlessly into professional workflows. Each feature is designed with enterprise-grade reliability and performance.

Key Features

What makes Weka stand out

  • Parallel file system on NVMe for low-latency IO
  • Hybrid tiering to object storage with policy control
  • Kubernetes integration and scheduler friendliness
  • High throughput to keep GPUs saturated
  • Quotas snapshots and multi-tenant controls
  • Encryption audit logs and SSO options
  • APIs and dashboards for automation and insight
  • Marketplace availability with private offers

Use Cases

How Weka can help you

  • Feed multi-node training jobs with consistent throughput
  • Consolidate research and production data under one namespace
  • Tier datasets to object storage while keeping hot shards local
  • Support MLOps pipelines that read and write at scale
  • Accelerate EDA and simulation with parallel IO
  • Serve inference features with predictable latency
  • Mirror data across regions for resilience
  • Standardize storage for labs and enterprise teams

Perfect For

infra architects, platform engineers, and research leads who need to maximize GPU utilization and simplify AI data operations with enterprise controls

Pricing

Start using Weka today

By quote

Starting price

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Quick Information

Category data
Pricing Model Paid
Last Updated 1/16/2026

Tags

storage gpu hpc parallel-file cloud performance data analytics analysis

Compare Weka with Alternatives

See how Weka stacks up against similar tools

Weka VS Akkio Weka VS Algolia Weka VS Alteryx

Frequently Asked Questions

How is WEKA priced?
Licensing is subscription-based and sold by quote, often by usable TB with private offers via cloud marketplaces.
Is there a free tier?
No, evaluations are typically handled through trials or POCs coordinated with sales or a cloud marketplace.
Does it run in the cloud?
Yes, WEKA supports major clouds and hybrid deployments with data tiering to object storage.
Can it improve GPU utilization?
Yes, by removing IO bottlenecks and delivering consistent throughput to training jobs.
Is Kubernetes supported?
Yes, integrations and drivers support K8s and common schedulers used in AI clusters.
How is data secured?
Encryption, RBAC, audit logs, and region controls help meet enterprise requirements.
Does it replace my object store?
No, it complements object storage and tiers data based on policies.
Is procurement flexible?
Yes, customers can purchase via marketplaces or direct enterprise agreements

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