#88th of 8 in Vector databases
Vespa
Serving engine for vectors, tensors, text and ML ranking at scale
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- Stars
- 7.1k
- License
- Apache-2.0
- Last commit
- Oct 2026
- Last release
- Oct 2026
Overview
Vespa is a serving platform that indexes vectors, tensors, text and structured data, selects a subset at query time, evaluates machine-learned ranking models over it and returns results in under 100 ms while the corpus changes, across many nodes. The Java and C++ engine builds from this repo with a release every morning Monday to Thursday. Getting started and self-hosting live in docs.vespa.ai; Vespa Cloud is the hosted option.
Who it is for: search teams needing ML ranking at very large scale
Strengths
- Vectors, tensors, text and structured data queried and ranked together
- Machine-learned ranking models evaluated at serving time
- Runs hundreds of thousands of queries per second on large internet services
- Sample applications repo plus detailed docs
Weaknesses
- README covers building, not running; install details live in docs
- Heavy platform (Java and C++ engine) sized for multi-node clusters
- C++ builds require AlmaLinux 8; Java needs JDK 17 and Maven
- A new release every weekday morning Monday to Thursday; versions churn
What it needs
- no GPU
- Models: machine-learned ranking models evaluated in Vespa
Also in Vector databases
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|---|---|---|
| 1 | MilvusDistributed vector database with dense, sparse and hybrid search at scale | 81 out of 100 |
| 2 | MeilisearchRust search engine API with full-text, vector and hybrid search | 69 out of 100 |
| 3 | ChromaEmbedding database with a four-function API for Python and JavaScript | 64 out of 100 |
| #4 | QdrantRust vector database with payload filtering, REST and gRPC | 63 out of 100 |
| #5 | pgvectorPostgreSQL extension for vector similarity search with HNSW and IVFFlat | 59 out of 100 |