Vespa
Hybrid search and ML serving engine for billion-scale applications
Our Take
Vespa is the most underappreciated tool in the vector database landscape. Ranked #1 Leader in GigaOm's 2025 Vector Database Radar, it combines vector search, BM25 text search, tensor operations, and real-time ML model inference in a single Java/C++ distributed platform. It powers Spotify search and Yahoo at billions of vectors. The learning curve is steep — Vespa's configuration model and query language are more complex than competitors — but for teams needing hybrid search with custom ML ranking at massive scale, nothing else comes close.
Pros
- + Ranked #1 in GigaOm 2025 Vector Database Radar
- + Proven at billions of vectors (Spotify, Yahoo)
- + Combines vector search, BM25, and ML inference
- + Open source (Apache 2.0)
- + Managed cloud option available
Cons
- - Steep learning curve
- - Complex configuration model
- - Heavier operational footprint than simpler alternatives
- - Smaller developer community relative to capabilities
Details
- Pricing Model
- open-source
- Starting Price
- $0
- Self-Hosted
- Yes
- Cloud Hosted
- Yes
- Founded
- 2017
- Repository
- GitHub →
Best For
- • Hybrid search + custom ML ranking at scale
- • Billion-vector production deployments
- • Real-time ML model inference alongside search
- • Teams needing BM25 + vector + tensor operations
Integrations
LangChain LlamaIndex Haystack Python SDK Java SDK
Articles featuring Vespa
Last updated: