Top 7 Open-Source Vector Databases in 2025

At AIMultiple, we develop and evaluate Generative AI technologies, including custom GPTs, AI agents, and cloud GPUs. A key enabler of these advancements is vector databases, which optimize high-dimensional data storage and retrieval for AI applications.

Based on our experience and benchmarking, we’ve identified the top 7 open-source vector databases excelling in scalability, performance, and real-world AI deployment:

Selection criteria

To ensure a focused selection process while aligning with key vector database use cases, we applied the following publicly verifiable criteria:

• 10k+ Github stars
• 100+ Contributors
• All vector databases should indicate their license

Top 7 open-source vector databases analyzed

Redis (Redis-Search and Redis-VSS)

Redis’ wide adoption and in-memory design mean it is focused at rapid, large-scale vector queries while handling hybrid queries (vectors plus filters). It aims to deliver immediate results at scale, making it ideal for high-throughput AI services (e.g. real-time recommendations or chatbots) that require fast similarity lookups with minimal latency.

Key Features:

• In-Memory Vector Search: Optimized for high-speed lookup of embeddings.
• Hybrid Queries: Combines key-value lookups with vector search.

Performance/Unique Points:

• Ideal for recommendation systems and low-latency AI applications.

Figure 1. Redis VB Diagram

Source: Redis

Facebook AI Similarity Search (Faiss)

Faiss (by Facebook/Meta) is a library (not a standalone DB) optimized for performance – it can handle billions of vectors and leverage GPUs for search, achieving state-of-the-art query speeds​

It’s widely used in academia and industry for embedding indexing and nearest-neighbor search at scale, making it optimal for projects that need a highly efficient engine embedded into ML/AI pipelines (e.g. large-scale image or text similarity searches)​

Note: Faiss lacks database features like persistence or clustering, so it’s best when you need raw speed and can manage data storage separately.

Key Features:

• Versatile Similarity Search: Supports multiple methods for high-dimensional similarity search (L2 Euclidean, inner product, and cosine for normalized vectors).
• Compressed Indexes: Offers binary vector and quantization techniques to compress vectors, enabling efficient storage without significant loss of accuracy.
• Advanced Index Structures: Implements various indexing structures (e.g., HNSW, NSG) on top of raw vectors to speed up nearest neighbor queries on large datasets.
• GPU Acceleration: Provides GPU implementations that seamlessly replace CPU indexes (e.g., IndexFlatL2 → GpuIndexFlatL2), with automatic memory transfer handling.

Performance/Unique Points:

• Scalability: Capable of searching through very large collections of vectors, including datasets too big to fit in RAM, by supporting on-disk indexes.
• Production Usage: Developed by Meta AI Research, Faiss is used in production for large-scale similarity search and clustering tasks, demonstrating its reliability at scale.
• Tuning Tools: Comes with evaluation and parameter-tuning tools out of the box, making it suitable for both research experimentation and production deployments.

Milvus

Milvus is an open-source platform with industrial AI applications and an active community. Milvus is focused on production environments – e.g. large recommendation systems, video/image search, or any AI workload handling huge vector corpora – where a user would need indexing and fault-tolerance. It integrates with popular AI tools and provides enterprise features (like replication and backups), setting it apart for big data use cases.

Key Features:

• Scalability: Engineered for millisecond-level search on billion- and even trillion-scale vector datasets, enabling similarity search on massive data stores.
• Rich APIs for Unstructured Data: Offers a broad set of APIs and SDKs to manage and query unstructured data (embeddings) easily.
• Cloud-Native & Portable: Provides a consistent experience across environments – runs on a laptop, local cluster, or cloud, thanks to its cloud-native architecture.
• High Availability: Includes replication and failover/failback, ensuring reliability for production use cases.

Performance/Unique Points:

• Benchmark Speed: Milvus reports millisecond-level search latencies even for trillion-vector collections.
• Distributed for Scale-Out: Scales horizontally with sharding and distributed indexing, maintaining performance as data volume grows.
• Active Ecosystem: A graduate project under the LF AI & Data Foundation, indicating an active community and governance structure.

Figure 2. Milvus Architecture Diagram

Source: Milvus

Qdrant

Qdrant is a fast-growing, open-source vector DB written in Rust, designed for performance and real-time updates​. It’s ideal when your application requires instant similarity matching on evolving data such as live recommendation engines or continually updated AI services.

Qdrant also excels at filtering and geo-search; it can store payload metadata with vectors and filter results by conditions, which is great for scenarios like personalized recommendations or location-based searches. In short, choose Qdrant when you need speed at scale with up-to-the-second data updates in your ML applications​.

Key Features:

• Flexible Filtering: Supports attaching JSON metadata (“payload”) to vectors and filtering search results based on those fields (e.g., keyword matches, numeric ranges, geo-location filters).
• Hybrid Vector Search: Allows mixing dense vector search with sparse vector methods, incorporating keyword scoring alongside vector embedding similarity.
• Vector Quantization: Offers built-in quantization options to compress vectors in memory, cutting RAM usage by up to 97%.
• Distributed and Resilient: Supports sharding and replication for horizontal scaling, plus features like zero-downtime rolling updates.

Performance/Unique Points:

• Memory Efficiency: The quantization feature reduces RAM usage significantly, allowing larger datasets to be served from memory efficiently.
• Easy Integration: Provides an easy-to-use API (REST and gRPC) for managing and querying the vector store.
• Optimized for Neural Search: Well-suited for semantic search applications where metadata and vector similarity must be combined.

Figure 3. High-level overview of Qdrant’s Architecture

Source: Qdrant

PostgreSQL (pgvector Extension)

The pgvector extension brings vector similarity search into PostgreSQL, allowing teams to reuse the familiar Postgres ecosystem​.

This is optimal when you want to avoid deploying a separate vector database – for instance, augmenting an app’s SQL database with vector capabilities for a few million embeddings. It supports efficient approximate nearest neighbor search, but its real advantage is convenience: you get basic vector search combined with SQL querying and Postgres reliability/security in one system​.

In practice, pgvector is best for scenarios where data volume is moderate and integration simplicity outweighs the absolute top-end performance of specialized vector-only systems.

Key Features:

• Extension-Based Vector Search: Uses pgvector to enable vector similarity search within PostgreSQL.
• Indexing for Speed: Supports IVF-based approximate nearest neighbor search.
• Flexible Querying: Enables hybrid queries mixing vector similarity with SQL filters.
• Common Distance Metrics: Supports Euclidean, inner product, and cosine distance.

Performance/Unique Points:

• Relational Integration: Allows seamless storage of vectors alongside relational data.
• Easy Adoption: Compatible with existing PostgreSQL setups and client libraries.
• Exact vs Approximate Search: Provides both precise and high-performance search options.

Chroma

Chroma is an open-source embedding database that is lightweight and developer-friendly, making it easy to go from a notebook demo to a production-scale deployment on the same API​

It’s optimized for simplicity and fast setup, so it excels in use cases like building conversational AI memory, semantic search for documents, or recommendation prototypes where quick iteration is key. Chroma’s focus on language embeddings and integration with ML frameworks (e.g. it plugs into LangChain or PyTorch pipelines) lets you stand up an embedding store and perform similarity queries with minimal effort​.

In summary, Chroma is best for quickly deploying an AI-powered search or QA system and scaling it over time, rather than handling billions of vectors immediately.

Key Features:

• Embedding Storage & Metadata: Designed to efficiently store embedding vectors along with their metadata, allowing easy organization and retrieval of high-dimensional data. 
• Built-in Vector Generation: Supports embedding documents and queries (with integration to models), enabling semantic search and retrieval-augmented generation use cases.
• Fast Similarity Search: Provides optimized search over embeddings to quickly find relevant vectors, and includes support for high throughput with minimal latency.
• LLM Integration: AI-native design focused on Large Language Model applications – making knowledge and facts easily pluggable into LLM workflows.

Performance/Unique Points:

• AI-Native Design: Chroma’s architecture is tailored for AI applications, simplifying development of LLM-powered apps by offering straightforward APIs and integration hooks.
• High Performance: Emphasizes low-latency operations over large volumes of embeddings, as noted by its “speed” focus in design.
• Ease of Use: Prioritizes developer experience with simple setup and usage, which has contributed to its rapid adoption.

Weaviate

Weaviate is an open-source, cloud-native vector database that integrates a knowledge graph and modular ML models, enabling contextual semantic queries on vectors​.

It’s ideal for scenarios like enterprise search, question-answering, or any application needing AI-driven insight over complex data – where you might vectorize text or images and link them with symbolic knowledge (attributes, concepts). Weaviate offers GraphQL APIs, real-time querying, and can handle multi-modal data (text, images, etc.), making it a top choice for building rich semantic search engines or recommendation systems that require understanding relationships and meaning​.

Its combination of semantic vector search with traditional filters and knowledge graph features sets it apart, and it’s used in industry for things like genomic search, FAQ bots, and content recommendation, where accuracy and context matter as much as speed.

Key Features:

• High-Speed Vector Search: Executes k-NN searches on millions of objects within a few milliseconds.
• Modular Architecture: Extensible via modules that integrate with ML model services (e.g., OpenAI, Cohere, HuggingFace).
• Hybrid Search Capabilities: Allows combining vector search with keyword filtering in the same query.
• Production-Ready Features: Includes clustering, replication, authentication, and security features for scalability.

Performance/Unique Points:

• Dual Search (Semantic + Lexical): Supports both vector similarity and symbolic (lexical) search in one engine.
• Plug-and-Play ML Integration: Enables on-the-fly text vectorization or use of pre-vectorized data.
• Scalability: Handles large-scale deployments efficiently.

What is a Vector Database?

A vector database is a specialized type of database designed to store, index, and retrieve high-dimensional vector embeddings efficiently. Instead of traditional structured data like tables and rows, vector databases handle numerical representations of data points. Vector databases are essential for machine learning, AI, and similarity search applications. With a vector database, you can:

• Find similar images or videos otherwise known as reverse research (e.g Google Lens)
• Store face embeddings and match them against a query for authentication or search (e.g Apple Face ID)
• Identify objects in images/videos and find relevant matches (e.g) 

There are numerous examples and use cases of vector databases, which we cover here.

What Are Vector Search Extensions?

Vector search extensions add vector search capabilities to existing databases, such as relational (SQL) or key-value stores, without requiring a dedicated vector database. These extensions allow users to perform similarity searches alongside traditional queries within the same database environment.

Key Characteristics of Vector Search Extensions:

• Embedded in existing databases → No need to introduce a separate vector database.
• Supports structured + vector queries → Enables combining vector-based similarity search with structured filters, SQL joins, and metadata-based lookups.
• Leverages existing indexing techniques → Uses approximate nearest neighbor (ANN) indexing within relational database storage.
• Best for hybrid applications → Ideal for adding AI-powered search to existing enterprise databases.

FAQ: Open-Source Vector Databases

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