Top 10 open source vector databases

What is a vector database?

A vector database is a data storage system used to manage, index, and query high-dimensional vector data. Vectors, in this context, represent data points in multi-dimensional space, often used in machine learning, data mining, and other advanced analytical applications.

Vector databases are useful in tasks involving the computation of distances or similarities between vectors, such as recommendation systems, image and video recognition, and natural language processing.

Unlike traditional databases that manage structured rows and columns, vector databases handle more complex, high-dimensional data representations, which are essential for applications requiring efficient similarity searches and pattern recognition. Some general purpose databases, such as PostgreSQL and Cassandra, now support both traditional data formats and vector data.

Further in this article, we’ll provide more details on 5 dedicated open source vector databases and 5 popular general purpose databases that provide vector database functionality.

Key features of open source vector databases

Open-source vector databases typically include the following features:

Efficient indexing: Indexing mechanisms such as Approximate Nearest Neighbor (ANN) searches reduce the time required to find similar vector representations, useful for applications involving real-time data analysis.
Similarity search: This feature finds vectors that are close to a given query vector in high-dimensional space, based on measures like Euclidean distance and cosine similarity. It is essential for applications like recommendation engines, where the system needs to identify items similar to the user’s preferences. Open-source vector databases often used algorithms to perform these searches accurately.
Scalability: As organizations collect more high-dimensional data, the database must efficiently manage this increase without compromising performance. Open-source solutions often offer distributed architectures that help in scaling out, ensuring consistent response times even as data volumes expand.
Integration with machine learning libraries: Open-source vector databases often work with popular machine learning frameworks, allowing for simple deployment of machine learning models directly on the database. This enables the direct application of learned models to the stored data for real-time analysis and predictions.
Community and support: An open-source community can provide assistance through forums, documentation, or contributions to the codebase. These databases often benefit from active communities that help in troubleshooting, feature enhancements, and providing comprehensive usage guides.

Tips from the expert

Merlin Walter

Solution Engineer

With over 10 years in the IT industry, Merlin Walter stands out as a strategic and empathetic leader, integrating open source data solutions with innovations and exhibiting an unwavering focus on AI's transformative potential.

In my experience, here are tips that can help you better utilize open-source vector databases:

Monitor memory usage: Ensure your vector indexes fit within available memory. If you use PostgreSQL with the pgvector extension you can ensure this by setting the appropriate maintenance_work_mem. Vector data can grow large, and exceeding available memory during indexing can drastically increase build times.
Understand your indexing algorithms: Use specialized vector indexes like HNSW (Hierarchical Navigable Small Worlds) or IVFFlat (Inverted File with Flat Compression) for fast approximate nearest neighbor (ANN) search. HNSW is ideal for most use cases. It features high query performance and its indexing structure adapts to dataset evolution because it is based on graphs, while IVFFlat is better for memory efficiency and lower build times.
Incorporate vector quantization: Utilize scalar quantization to reduce 4-byte floats to 2-byte floats, and binary quantization to reduce the dimensions to a single bit. This dramatically cuts storage costs, especially for large datasets with high-dimensional vectors.
Monitor vector database performance: Implement monitoring and logging tools to track the performance of your vector database, particularly during high-load periods. This can help in identifying bottlenecks and optimizing query strategies in real-time.

Open source vector databases

Here are some of the most popular open source vector databases.

1. Facebook AI Similarity Search (Faiss)

Faiss is a library for similarity search and clustering of dense vectors. Developed primarily at Meta’s Fundamental AI Research group, Faiss supports searching in sets of vectors of any size, including those too large to fit into RAM. It also offers tools for evaluation and parameter tuning. Written in C++, Faiss includes complete wrappers for Python/numpy.

Repo: https://github.com/facebookresearch/faiss
License: MIT license
GitHub stars: ~30K
Contributors: 100+

Key features of Faiss:

Similarity search methods: Supports several methods for similarity search, assuming instances are represented as vectors identified by integers. Allows comparison of vectors using L2 (Euclidean) distances or dot products, and supports cosine similarity for normalized vectors.
Compressed vector representations: Some methods in Faiss use binary vectors and compact quantization codes, allowing for a compressed representation of vectors without retaining the originals.
Indexing structures: Improves search efficiency by adding indexing structures on top of raw vectors, such as HNSW and NSG. These structures help in managing large datasets and speeding up the search process.
GPU implementation: Supports GPU implementations that can take input from either CPU or GPU memory. GPU indexes can replace CPU indexes (e.g., replacing IndexFlatL2 with GpuIndexFlatL2), with automatic handling of memory transfers.

2. Chroma

Chroma is an AI-native open-source vector database used to simplify the development of LLM (Large Language Model) applications. It supports building these applications by making knowledge, facts, and skills easily pluggable for LLMs.

Repo: https://github.com/chroma-core/chroma
License: Apache-2.0 license
GitHub stars: 14K
Contributors: 100+

Key features of Chroma:

Storage of embeddings and metadata: Allows efficient storage and management of embeddings and their associated metadata, ensuring easy retrieval and organization of high-dimensional data.
Document and query embedding: Provides tools for embedding documents and queries, enabling similarity searches and ensuring relevant results.
Search embeddings: Supports searching within embeddings to find relevant data points quickly, enhancing the performance of applications that rely on rapid data retrieval.
Speed: Offers high performance with quick data processing capabilities, ensuring that applications can handle large volumes of data without latency issues.

3. Milvus

Milvus is an open-source vector database for embedding similarity search and AI applications. It aims to make unstructured data search more accessible and provides a consistent user experience across different deployment environments, including laptops, local clusters, and the cloud.

Repo: https://github.com/milvus-io/milvus
License: Apache-2.0 license
GitHub stars: 28K
Contributors: 250+

Key features of Milvus:

Millisecond search on trillion vector datasets: Capable of performing searches with average latency measured in milliseconds, even on trillion-vector datasets.
Simplified unstructured data management: Offers rich APIs for data science workflows, simplifying the management and querying of unstructured data.
Consistent user experience: Provides a seamless user experience across various deployment environments.
Always-on database: Features built-in replication and failover/failback mechanisms to maintain business continuity. These features ensure that data and applications remain available and reliable even in the event of disruptions.

Source: Milvus

4. Qdrant

Qdrant (pronounced: quadrant) is a vector similarity search engine and vector database offering a production-ready service with an easy-to-use API for storing, searching, and managing vectors along with additional payload data. It provides extended filtering support, making it suitable for neural-network or semantic-based matching, faceted search, and other applications.

Repo: https://github.com/qdrant/qdrant
License: Apache-2.0 license
GitHub stars: ~20K
Contributors: 100+

Key features of Qdrant:

Filtering and payload: Allows attaching any JSON payloads to vectors, supporting various data types and query conditions. Enables storage and filtering based on values in these payloads, including keyword matching, full-text filtering, numerical ranges, and geo-locations.
Hybrid search with sparse vectors: To enhance the capabilities of vector embeddings, supports sparse vectors alongside regular dense ones. Sparse vectors extend the functionality of traditional BM25 or TF-IDF ranking methods, allowing for effective token weighting using transformer-based neural networks.
Vector quantization and on-disk storage: Offers multiple options for making vector searches more cost-effective and resource-efficient. Built-in vector quantization reduces RAM usage by up to 97%, dynamically balancing search speed and precision.
Distributed deployment: Supports horizontal scaling through sharding and replication, enabling size expansion and throughput enhancement. Provides zero-downtime rolling updates and dynamic scaling of collection.

Source: Qdrant

5. Weaviate

Weaviate is a cloud-native, open-source vector database that emphasizes speed and scalability. Using machine learning models, it transforms various types of data—text, images, and more—into a highly searchable vector database.

Repo: https://github.com/weaviate/weaviate
License: BSD-3-Clause license
GitHub stars: 10K+
Contributors: 100+

Key features of Weaviate:

Speed: Has a core engine capable of performing a 10-NN nearest neighbor search on millions of objects in milliseconds.
Flexibility: Can vectorize data during the import process or allow users to upload pre-vectorized data. The system’s modular architecture provides more than two dozen modules that connect to popular services and model hubs, including OpenAI, Cohere, VoyageAI, and HuggingFace.
Production-readiness: Built with a focus on scaling, replication, and security. Smoothly transitions from rapid prototyping to full-scale production. This ensures that applications can grow without compromising performance or reliability.
Beyond search: Its capabilities extend to recommendations, summarization, and integration with neural search frameworks.

Source: Weaviate

General purpose databases supporting vector data

6. PostgreSQL

PostgreSQL is an open-source relational database that supports vector data through extensions like pgvector. This extension enables efficient similarity search on vector data, integrating with PostgreSQL’s ecosystem.

Repo: https://github.com/postgres/postgres
License: PostgreSQL License
GitHub stars: 15K+
Contributors: 50+

Key features of PostgreSQL for Vector Data:

pgvector extension: Allows storage and querying of vector embeddings, facilitating similarity searches within the PostgreSQL environment.
Indexing: Supports various indexing methods, such as ivfflat, to optimize vector search performance.
Scalability: Offers scalability and support for large datasets, making it suitable for vector data applications.
Flexibility: Enables complex queries combining vector searches with traditional SQL operations, providing a unified platform for diverse data types.

Source: PostgreSQL

7. Cassandra

Cassandra is a scalable NoSQL database for handling large amounts of data across many commodity servers, providing high availability with no single point of failure. With the introduction of vector search capabilities, Cassandra can efficiently manage vector data.

Repo: https://github.com/apache/cassandra
License: Apache-2.0 license
GitHub stars: 8K+
Contributors: ~450

Key features of Cassandra for Vector Data:

Scalability: Capable of handling petabytes of data, making it suitable for applications requiring large-scale vector storage and retrieval.
High availability: Ensures data availability through its distributed architecture, even during node failures.
Vector search: Supports vector search functionalities, enabling similarity search within its distributed database framework.
Integration: Can integrate with various machine learning frameworks, leveraging its data storage capabilities for AI applications.

Source: Apache

8. Redis

Redis is an in-memory data structure store known for its speed and flexibility. With the addition of the RedisAI module, it extends its capabilities to support vector data and AI model serving.

Repo: https://github.com/redis/redis
License: Redis Source Available License 2.0
GitHub stars: ~66K
Contributors: 700+

Key features of Redis for Vector Data:

In-memory speed: Provides fast data retrieval and processing due to its in-memory nature.
Vector similarity search: RedisAI allows storing and querying vector data, supporting rapid similarity searches.
AI integration: Enables model serving and vector operations within the same environment, streamlining AI workflows.
Scalability: Redis Cluster enables scaling out across multiple nodes, maintaining performance and reliability.

Source: Redis

9. Valkey

Valkey is a specialized vector database designed to manage and search high-dimensional vector data efficiently. It offers a suite of tools and APIs tailored for vector data management and retrieval.

Repo: https://github.com/valkey-io/valkey
License: BSD 3-Clause license
GitHub stars: 15K
Contributors: 70+

Key features of Valkey:

Optimized storage: Uses advanced data structures and indexing methods to store and retrieve vector data efficiently.
High performance: Designed for rapid vector similarity searches, ensuring low latency even with large datasets.
Rich API: Provides APIs for managing vector data, supporting a range of use cases from AI to search engines.
Scalability: Supports distributed deployments, allowing scaling as data and query loads increase.

10. CockroachDB

CockroachDB is a cloud-native, distributed SQL database designed for ultra-resilient, global applications. It has recently added support for vector data, making it a versatile choice for managing both traditional and vector-based data.

Repo: https://github.com/cockroachdb/cockroach
License: BSL 1.1, MIT license, CockroachDB Community License (CCL)
GitHub stars: ~30K
Contributors: 750+

Key features of CockroachDB for Vector Data:

Distributed architecture: Ensures data resilience and availability across multiple geographic locations.
Vector search support: Enables efficient storage and retrieval of vector data, supporting similarity search operations.
SQL compatibility: Combines vector data handling with traditional SQL queries, providing a unified data management solution.
Scalability and resilience: Built to scale out horizontally with strong consistency guarantees, ensuring reliable performance for vector data applications.

Source: CockroachDB

Streamlining performance and reliability: Instaclustr's managed approach to vector database management

Instaclustr is a leading provider of managed solutions for open-source technologies offering comprehensive management services for vector databases. These databases are commonly used in applications such as machine learning, data analytics, and recommendation systems.

Instaclustr’s management of vector databases encompasses various aspects to ensure optimal performance, scalability, and reliability. Instaclustr provides a fully managed service, handling the deployment, configuration, and ongoing maintenance of the vector database infrastructure. This relieves organizations from the complexities of managing and operating the database themselves, allowing them to focus on their core business objectives.

One key aspect of Instaclustr’s management approach is expertise in tuning and optimizing vector databases. Instaclustr has a deep knowledge of the underlying technologies and understands the intricacies of configuring the database for specific use cases. This expertise enables fine-tuning of database parameters, indexing strategies, and query optimization techniques to maximize performance and minimize query latency.

Scalability is another critical aspect of Instaclustr’s management of vector databases. As datasets grow in size or the workload demands increase, Instaclustr can seamlessly scale the vector database infrastructure to handle the additional load. It employs horizontal scaling techniques, such as sharding and replication, to distribute the data and workload across multiple nodes, ensuring high availability and efficient utilization of resources.

Instaclustr also places a strong emphasis on security and data protection. It implements robust security measures to safeguard the vector database infrastructure, including encryption at rest and in transit, access controls, and regular security audits. Additionally, it provides automated backups and disaster recovery solutions to ensure data integrity and availability in the event of any unforeseen incidents.

For more information on Instaclustr and vector databases, see: