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Vespa is a type of open source vector database. It is a data-serving engine that is particularly designed for organizing, searching, and storing large amounts of data with machine-learned judgments.

Data is stored and queried locally and can be synchronized with a Qdrant server. It offers the same powerful vector search capabilities as the client-server version but with a smaller footprint, making it ideal for applications that require low latency and offline functionality.

Qdrant is an Open-Source Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.

This comparison breaks down the leading open source vector databases for production AI workloads. Each database in this comparison optimizes for different tradeoffs, and the right choice depends on your scale, deployment constraints, and infrastructure preferences.

We’ve identified the top 7 open-source vector databases that excel in scalability, performance, and real-world AI deployment.

Milvus is designed to handle vector search at scale. It stores vectors, which are learned representations of unstructured data, together with other scalar data types such as integers, strings, and JSON objects. Users can conduct efficient vector search with metadata filtering or hybrid search.

Walk through the most popular open-source vector databases available today, compare their strengths and limitations, and provide practical insights to help you make an informed decision.

Compare the best open source vector databases in 2026 for RAG, semantic search, AI apps, Postgres stacks, scale, and production readiness.

In this guide, we’ll walk through the most popular open-source vector search engines available today, compare their strengths and limitations, and provide practical insights to help you make...

Unlike existing relational databases which mainly deal with structured data following a pre-defined pattern, Milvus is designed from the bottom-up to handle embedding vectors converted from ...