Alternatives to VectorRAG.Net

Qdrant: open-source Rust vector search engine built for filtered hybrid search at scale

Moss delivers in-process semantic search with sub-10ms retrieval for voice agents, copilots and on-device AI.

Fully managed vector database on Milvus for RAG, semantic search, and billion-scale AI applications.

Meilisearch delivers typo-tolerant, sub-50ms search-as-you-type plus hybrid vector and multimodal retrieval through one REST API.

Vespa is an open-source AI search platform combining vector, text and tensor retrieval with machine-learned ranking in one engine.

Embeddable RAG for Elixir and Phoenix — vector search, GraphRAG, and agentic retrieval inside your existing Ecto/Postgres repo.

TopK is a unified retrieval engine that runs multi-vector, dense, sparse, regex, and metadata-filtered search in one query, billed per unit of usage.

Embedbase is a single API that combines a semantic search vector store with LLM text generation.

Open-source, in-memory text vector database for .NET apps that adds semantic search and RAG without provisioning a vector server.

Chroma is open-source search infrastructure for AI that keeps vectors, indexes and metadata on cheap object storage.

Fully managed vector database and knowledge platform: search, upsert, and rerank from one index and one API key.

pgvector adds vector similarity search inside Postgres, so embeddings live next to your relational data.

Vector and full-text search on object storage, with sub-10ms warm queries and pricing roughly 10x under compute-based vector databases.

TiDB is a distributed SQL database with native vector search for AI agent memory, state, and RAG at scale.

Weaviate is an open-source AI database that combines vector search, hybrid search, and RAG with agent memory in one stack.

LanceDB is an open-source multimodal lakehouse for vector search, curation, and training on one Lance table.

Lance is an open lakehouse format for multimodal AI, pairing a file format, table format, and catalog spec with hybrid vector and full-text search on object

PostgresML brings LLM inference, embeddings, and vector search to your database with SQL — Postgres with GPUs for RAG and ML.

Elasticsearch Labs: free tutorials, notebooks, and reference apps for AI search, vector search, and RAG on Elasticsearch.

Kronotop is a distributed transactional document database with built-in vector search and millions of isolated agent namespaces per cluster.

Open-source in-process vector database from Alibaba — install one Python package, query millions of vectors in seconds.

NodeDB is a universal database engine fusing vector, graph, document, columnar, key-value, and full-text search in one SQL binary.

Open-source RAG engine with built-in ETL, hybrid search and visual AI agent orchestration

VectorAI DB: run a local vector database for persistent AI agent memory on your own hardware, offline or air-gapped.

Context layer for AI agents: push nested JSON once, then query graph, vector, and analytics through a single API.

Open-source vector database for GenAI apps, scaling from a pip install to tens of billions of vectors

Open-source Python framework that incrementally keeps AI agent context fresh by reprocessing only changed data.

Voyage AI embedding models and rerankers for high-precision RAG retrieval, now part of MongoDB

Open-source, self-hosted AI search and RAG chat you add to your site with one script tag

Open-source Microsoft Research RAG that builds a knowledge graph and community summaries from your documents, then queries them in four modes.