Liran Zvibel
CEO
WEKA
WEKA provides a cloud-native, software-defined data platform that enables organizations to efficiently store, process, and manage large volumes of data across on-premises and cloud environments. By transforming stagnant data silos into streaming data pipelines, WEKA enhances performance for AI and high-performance computing workloads while reducing energy consumption and carbon emissions.
- Artificial Intelligence
- Data & Analytics
- Enterprise Software
- Software Only
Updated 22 months ago
Funding
Raised to date
$140MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
+2Funding rounds are not available yet.
Founders
Product
Problem
Organizations struggle to efficiently manage and process the increasing volumes of data required for AI, high-performance computing, and other demanding workloads. Traditional data infrastructure often creates stagnant data silos, leading to complexity, performance bottlenecks, and increased energy consumption.
Solution
WEKA provides a software-defined data platform designed for the cloud and AI era, enabling organizations to seamlessly and sustainably store, process, and manage data across on-premises and cloud environments. The platform transforms data silos into streaming data pipelines, accelerating next-generation workloads while optimizing resource utilization and reducing carbon emissions. By offering a unified and easy-to-use solution, WEKA eliminates the complexities associated with traditional data infrastructure, providing mind-bending speed, seductive simplicity, infinite scale and effortless sustainability.
Target Audience
The primary target audience includes enterprises and research organizations in industries such as financial services, media and entertainment, life sciences, and the public sector that require high-performance data infrastructure for AI, machine learning, and high-performance computing workloads.
Features
- Software-defined architecture that abstracts the data layer from the underlying hardware
- Support for both file and object storage protocols
- Independent and linear scaling of compute and storage resources
- High I/O, low latency performance optimized for small files and mixed workloads
- Integration with cloud services for hybrid cloud deployments
- AI RAG Reference Platform (WARRP) architecture to accelerate the development of scalable RAG-based inferencing environments
- Data reduction techniques to minimize storage footprint and energy consumption