Provides a scalable platform that combines a distributed text search engine with a vector database, enabling real-time querying, ranking, and inference over billions of data items with sub-100ms latency. It supports applications like hybrid search, recommendation systems, and generative AI by integrating machine-learned relevance models and multi-vector representations for improved accuracy and performance.
Funding
$31M raised to dateRaised 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.
BCFounders
Product
Problem
Building applications that require real-time querying, ranking, and inference over large datasets with low latency is challenging. Existing solutions often struggle to efficiently combine text search with vector-based similarity search and machine learning for relevance ranking.
Solution
Vespa.ai provides a scalable platform that unifies a distributed text search engine and a vector database, enabling real-time computation and querying over data. It allows users to organize and make inferences on vectors, tensors, text, and structured data at scale, supporting billions of data items and thousands of queries per second with sub-100ms latency. The platform facilitates the development of applications like hybrid search, recommendation systems, and generative AI by integrating machine-learned relevance models and multi-vector representations. Vespa's architecture supports both indexed and streaming search modes, catering to diverse use cases from large-scale content retrieval to personalized data access.
Target Audience
The primary users are developers and AI teams building large-scale AI applications that require real-time search, recommendation, and personalization capabilities.
Features
- Unified platform for vector, text, and structured search with configurable linguistics integration
- Distributed machine-learned ranking using ONNX or XGBoost models for relevance scoring
- Support for hybrid search combining keyword and vector similarity for improved accuracy
- Real-time querying and inference with sub-100ms latency at scale
- Infinite automated scalability, allowing for dynamic adjustment of cluster sizes without impacting queries or writes
- Continuous deployment and upgrades, ensuring access to the latest platform features and security patches
- Fully managed service with strong security, including encryption of data at rest and in transit
- Support for deploying applications in customer-owned dedicated accounts