Vespa
FreeWeb-scale hybrid serving
FreeFree tier
Inputs: text
About Vespa
Vespa is an open-source AI search platform that enables search, inference, and organization of vectors, tensors, text, and structured data at serving time and any scale. It provides hybrid serving capabilities, combining traditional text search with modern vector-based approaches, and is designed for web-scale performance. Vespa can be self-hosted and is licensed under Apache 2.0.
Key Features
Hybrid search combining vectors, tensors, text, and structured data
Real-time inference at serving time
Scalable to any scale (web-scale)
Open source under Apache 2.0 license
Full control over code and deployment through self-hosting
Pros & Cons
Pros
- Open source with Apache 2.0 license, no vendor lock-in
- Handles multiple data types (vectors, tensors, text, structured) in a single system
- Scalable to web-scale with low latency
- Supports real-time inference during search
- Active community and rich documentation
Cons
- Requires significant infrastructure and DevOps effort to self-host
- Steep learning curve for setup, configuration, and tuning
- Potential complexity when integrating with existing systems
Best For
Building AI-powered search engines with embeddingsReal-time recommendation systemsLarge-scale semantic and hybrid searchAI applications requiring combined retrieval and inference
FAQ
What is Vespa?
Vespa is an AI search platform that allows you to search, make inferences, and organize vectors, tensors, text, and structured data at serving time and any scale.
Is Vespa open source?
Yes, Vespa is open source under the Apache 2.0 license. The entire codebase is available on GitHub.
What data types does Vespa support?
Vespa supports vectors, tensors, text, and structured data, enabling hybrid search across all these modalities.
Can Vespa be used for real-time inference?
Yes, Vespa supports real-time inference at serving time, allowing you to compute machine learning model outputs as part of search queries.