# vespa-engine/vespa

The AI search platform

Repository: https://github.com/vespa-engine/vespa
Canonical: https://ross.abutalabs.com/products/vespa
Homepage: https://vespa.ai
Language: Java
License: Apache-2.0
License Family: permissive
Topics: vespa, search-engine, big-data, ai, serving-recommendation, machine-learning, server, java, vector-search, rag, search, vector, tensor, vector-database
Last push: 2026-08-26T20:31:13+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 87, longevity 100
- inputs: {"age_days": 3743, "days_push": 7, "days_rel": 9, "gap_med": 4, "n_releases_24m": 140}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 7069, forks 735 (observed 2026-08-28T04:09:55.676450+00:00)

## What it is
Vespa is an open-source, distributed AI search platform and serving engine that combines full-text search, vector/tensor search, and machine-learned ranking with real-time inference over large, continuously changing datasets. It scales to billions of documents and hundreds of thousands of queries per second with sub-100ms latencies, and can be self-hosted or run as a managed cloud service.

## Use cases
- build a hybrid search engine combining BM25 text search with vector similarity
- serve RAG applications with retrieval and embedding inference at scale
- run a vector database for nearest-neighbor search over billions of embeddings
- build personalized recommendation and ranking systems with ML model inference at query time
- deploy low-latency search over constantly updating large corpora
- evaluate and rank results with ONNX, TensorFlow, XGBoost, or LightGBM models at serving time

## When to choose
- you need production-scale search, recommendation, or RAG with sub-100ms latency over billions of documents
- you want unified full-text, vector, and structured data search with integrated ML ranking in one platform
- you need real-time indexing and querying of continuously changing data
- you want to self-host a battle-tested engine proven on large internet services like Perplexity

## When to avoid
- you need a simple embedded search library for a small application without distributed serving
- your team cannot operate a complex multi-node Java-based platform and you don't want a managed cloud
- you only need lightweight keyword search where Elasticsearch or a simpler tool suffices
- you want a pure vector store without text search or ranking features

## Facets
- artifact type: service
- maturity: stable
- function: search-engine, vector-database, machine-learning, rag, llm-inference, streaming
- domain: databases, large-language-models, big-data, machine-learning, analytics
- platform: cloud, self-hosted, jvm, python
- tags: hybrid-search, tensor-computation, ranking, recommendation, distributed-serving, approximate-nearest-neighbor, bm25, real-time-indexing, pyvespa, search, retrieval-augmented-generation, linux, macos, docker, kubernetes, web-server

## Member repositories
- vespa-engine/vespa (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:55.676450+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:40:27.920572+00:00, confidence not recorded.
  - readme: https://github.com/vespa-engine/vespa (fetched 2026-08-28T04:09:55.676450+00:00, sha 5e778a33e203)
  - homepage: https://vespa.ai (fetched 2026-08-29T08:36:14.460473+00:00, sha 61b7ec27f4d2)
  - site_page: https://blog.vespa.ai/vespa-quickstart-how-to-set-up-an-application-with-vespa (fetched 2026-08-29T08:36:14.480905+00:00, sha 19ff5914a268)
  - site_page: https://docs.vespa.ai/ (fetched 2026-08-29T08:36:14.469755+00:00, sha 886b51ce462e)
  - site_page: https://vespa.ai/features (fetched 2026-08-29T08:36:14.472421+00:00, sha 8aa0dc7c5e75)
  - site_page: https://vespa.ai/company (fetched 2026-08-29T08:36:14.474778+00:00, sha 226853ab6f47)
  - site_page: https://vespa.ai/press-releases (fetched 2026-08-29T08:36:14.476841+00:00, sha 9542c0a82c4d)
  - site_page: https://vespa.ai/pricing (fetched 2026-08-29T08:36:14.478995+00:00, sha b4bcd4fea534)
  - site_page: https://blog.vespa.ai/re-autoresearching-msmarco-bm25-on-vespa (fetched 2026-08-29T08:36:14.482784+00:00, sha 5de7969c6385)
- Data as of 2026-08-30T08:39:29.467469+00:00.