Top 5 Open Source Vector Databases in 2024

A Vector Database is a specialized type of database designed to store and manage collections of vector embeddings. These embeddings represent chunks of data—such as text or images—as numerical vectors. A vector database allows you to create, read, update, and delete vector embeddings efficiently. It stores data in vector form and retrieves information based on vector similarity, measuring the "distance" and relationships between vectors in a multi-dimensional vector space.

With the rapid rise of AI and big data, vector databases are gaining significant attention. They are critical for handling high-dimensional vector data, which is commonly found in applications such as machine learning embeddings, recommendation systems, image search, and natural language processing. As AI models continue to grow in complexity, the role of vector databases becomes increasingly important in managing and retrieving vast amounts of vectorized data.

Vector databases play a pivotal role in various AI and machine learning (ML) applications, including:

Retrieval-Augmented Generation (RAG) Systems: Vector databases can be integrated with large language models (LLMs) to build knowledge-based language AI applications. By retrieving relevant information based on vector similarity, they enhance the accuracy of AI-generated responses.

Natural Language Processing (NLP): They improve search engines, chatbots, and language translation by enabling fast and accurate text similarity searches, helping to find the closest matches to a given query.

Computer Vision: Vector databases support image recognition, facial recognition, and object detection by efficiently comparing high-dimensional image embeddings, making it possible to quickly identify visual similarities between images.

Recommendation Systems: By analyzing user behavior and preferences encoded as vectors, vector databases enable personalized recommendations, offering products or content that closely match user interests.

Large Language Models (LLMs): They enhance LLMs by providing long-term memory, enabling semantic search, and supporting retrieval-based text generation, which improves the relevance and coherence of AI-generated outputs.

Content Moderation: Vector databases assist in automated content moderation by comparing new data with a database of known examples, helping to detect and filter inappropriate or harmful content.

The world of artificial intelligence (AI) is evolving rapidly, and at the forefront of this progress is a new type of database: the vector database. However, with so many options available, choosing the right one can be overwhelming. This blog post will take a deep dive into the five major players in the vector database space: Faiss, Milvus, Qdrant, Chroma DB, and Weaviate. We will briefly introduce their functions and features, explore their market share and application scenarios, and help you choose the right vector database.

Official Website: https://faiss.ai/

GitHub Repo: https://github.com/facebookresearch/faiss

GitHub Star: 30.8k

Faiss is an open source project developed by Facebook AI Research (FAIR) and is a library for efficient similarity search and dense vector clustering. It contains algorithms that can search vector sets of arbitrary size, even vector sets that may not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with a full Python wrapper. Although it is mainly written in C++, it fully supports Python/NumPy integration. Some of its key algorithms can also be executed on GPUs.

Note: FAISS is a vector index library, not a complete vector database. It is mainly responsible for efficiently building and querying vector indexes, and provides a variety of search algorithms to achieve fast similarity search.

Features:
• Supports multiple index structures (such as IVF, HNSW), and optimizes CPU and GPU performance.
• Provides excellent similarity search speed.

Market share:
• Faiss is widely used in academia and enterprises due to its powerful GPU acceleration and support in the AI ​​community.

Recommended scenarios:
• High-performance computing, especially GPU-accelerated similarity search.
• Academic research or experimental scenarios that require processing large-scale vector data.

Official Website: https://milvus.io/

GitHub Repo: https://github.com/milvus-io/milvus

GitHub Star: 23.8k

Milvus is an open source vector database, driven by Zilliz, designed for processing large-scale vector data. It supports both NNS and ANNS, and can be well integrated with various ML frameworks.

Features:
• Efficient retrieval: supports fast similarity search of high-dimensional vectors (such as approximate nearest neighbor retrieval - ANN), combined with indexing technologies such as FAISS, HNSW, etc.
• Good scalability: supports distributed architecture and is suitable for processing large-scale data.

Market share:
• high, especially widely used in the open source community. Milvus community is very active and widely used for industrial-level large-scale data retrieval.

Recommended scenarios:
• Support industrial-level AI applications that require scalability and distributed architecture.
• Management of large-scale vectorized data such as text, images, and voice.

Official Website: https://qdrant.tech/

GitHub Repo: https://github.com/qdrant/qdrant

GitHub Star: 20.1k

Qdrant is an advanced vector search engine designed for high-dimensional data processing. It provides a scalable solution for similarity search and machine learning model integration. Main features: real-time updates, precise search capabilities, and efficient vector storage.

Features:
• High-performance approximate nearest neighbor search: Based on HNSW technology, it optimizes similarity search for large-scale vector data.
• Simple cloud hosting solution: provides a hosted version of SaaS service, and users can flexibly choose self-hosting or cloud hosting.

Market share:
• Qdrant is a fast-growing open source vector database that focuses on performance optimization and real-time similarity search, mainly targeting the developer community that needs embedded search.

Recommended scenarios:
• Real-time vector similarity search and AI-driven recommendation system.
• Applications that require high availability and dynamic data updates.

Official Website: https://www.trychroma.com

GitHub Repo: https://github.com/chroma-core/chroma

GitHub Star: 14.8k

Chroma DB is an open source vector database specifically designed for storing and managing vector embeddings, aiming to provide developers with efficient vector retrieval capabilities. It is widely used in building large-scale machine learning and deep learning models, and is particularly suitable for application scenarios that require fast vector retrieval, similarity search, and support for complex embedding operations. Chroma DB emphasizes ease of use, and developers can quickly integrate and use it to build AI-driven search and recommendation systems.

Features:
• Vector embedding storage and retrieval: Chroma is designed for efficient storage and management of vector data, and is suitable for processing embeddings generated by neural networks, such as vectorized data such as text, images, and audio.
• Multiple vector indexes: Chroma supports different indexing strategies, such as HNSW (Hierarchical Navigable Small World), to ensure low-latency retrieval on large-scale datasets.
• Integration and extension: Chroma is easy to integrate with existing machine learning and deep learning frameworks (such as PyTorch and TensorFlow), and users can directly store the embeddings generated by the model into the database.
• Vector and metadata management: In addition to storing the vectors themselves, Chroma also supports attaching metadata to vectors, making it easier for users to manage and retrieve datasets containing complex information.
• Real-time processing: Supports real-time data updates, allowing developers to quickly store dynamically generated embeddings and data into the database and perform real-time similarity searches.

Market share:
• Chroma DB is an open source vector database that is widely used for embedded storage and retrieval in AI and machine learning, and its market share is gradually increasing, especially in the developer community.

Recommended scenarios:
• Recommendation system: suitable for building recommendation engines based on vector similarity, which can be used to process documents, images or user behavior data to recommend items similar to queries or preferences to users.
• Search engine: used to build large-scale similarity search engines, supporting embedding-based search, such as searching for similar products for users in e-commerce platforms.
• AI-assisted applications: suitable for AI-assisted applications that need to quickly query similar content from the embedding space, such as text summarization, image classification, and speech recognition systems.
• Natural language processing (NLP): In NLP, Chroma can be used to process embeddings generated by language models to achieve semantic search and classification of text.

Official Website: https://weaviate.io/

GitHub Repo: https://github.com/weaviate/weaviate

GitHub Star: 11.7k

Weaviate is a GraphQL-based cloud-native vector database designed for large-scale AI applications that provides powerful search and retrieval capabilities for vector data. Weaviate takes a unique approach, acting as a one-stop shop for both your data and its AI-generated representations. It seamlessly stores not only raw data objects but also the vector embeddings derived from various machine learning models. This versatility, coupled with its lightning-fast search capabilities, empowers users to explore complex datasets and uncover hidden patterns at unprecedented speed.

Features:
• Knowledge graph integration: can be combined with knowledge graph data for semantic search, suitable for processing complex text queries.
• Multimodal data support: supports vectorization and retrieval of multiple data types such as text and images.

Market share:
• Weaviate is an open source vector database that is gradually gaining popularity in academia and industry applications, especially applications that require knowledge graphs and semantic search.

Recommended scenarios:
• Semantic search and knowledge graph construction.
• AI applications that need to combine structured and unstructured data.

The choice of vector database depends on your specific use case, scale, and performance needs. Here's a quick guide to help:

For speed and GPU support: Choose Faiss if you need rapid vector search in a high-performance, GPU-accelerated environment.

For large-scale distributed systems: Go with Milvus if you need scalability, persistence, and enterprise-level features for large datasets.

For real-time applications: Qdrant is ideal for real-time updates and applications that need immediate, high-availability vector search.

For simplicity and prototyping: Chroma is a lightweight, easy-to-use option for smaller projects or those focused on rapid prototyping.

For semantic and hybrid search: Weaviate is your best choice if you need to integrate semantic understanding and process both structured and unstructured data.

Each of these databases offers unique strengths and is suited to different tasks. Understanding your project’s scale, data type, and performance requirements will help you make the right decision.