Vald
FundedHighly scalable distributed vector search engine for large-scale data.
About Vald
Vald is a cloud-native, distributed vector search engine designed to perform fast approximate nearest neighbor searches on dense vector data. It is engineered for enterprises that require efficient querying and indexing of billions of feature vectors, supporting horizontal scaling on memory and CPU resources to meet varying demands. Vald’s architecture supports asynchronous auto indexing, distributed indexing, and index replication, ensuring continuous operation and high availability even during indexing or agent failures.
Vald is suitable for organizations with large-scale data warehousing and DevOps needs, particularly those leveraging machine learning or AI models that generate high-dimensional vector data. Its customizable ingress and egress filtering, multi-language SDK support (Golang, Java, Node.js, Python), and automated backup capabilities using object storage or persistent volumes provide robust disaster recovery and integration flexibility. The platform is designed to be easy to deploy and configure, enabling enterprises to optimize vector search performance while maintaining operational resilience.
Key Capabilities
- ✓Asynchronous auto indexing without downtime
- ✓Distributed and replicated vector indexing
- ✓Horizontal scaling on memory and CPU
- ✓Customizable ingress and egress filtering
- ✓Automated index backup and disaster recovery
Integrations
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