The 5 best vector databases you should try in 2024

The best vector databases are known for their versatility, performance, scalability, consistency and efficient algorithms for storing, indexing and querying vector embeddings for artificial intelligence applications.

A vector database is a specialized type of database designed to store and index vector embeddings for efficient retrieval and similarity. It is used in various applications related to large language models, generative AI and semantic search. Vector embeddings are mathematical representations of data that capture semantic information and provide insights into patterns, relationships, and underlying structures.

Vector databases are gaining importance in the field of artificial intelligence applications because they are excellent at handling high-dimensional data and facilitating complex similarity searches.

In this blog, we'll take a look at the top five vector databases you should try in 2024. These databases were chosen based on their scalability, versatility, and performance when working with vector data.

1. Qdrant

Qdrant is an open source vector similarity search engine and vector database that provides a production-ready service with a user-friendly API. You can store, search and manage vector embeddings. Qdrant supports advanced filtering, making it useful for a wide range of applications involving neural network or semantic matching, faceted search, and more. Since Qdrant is written in the robust and fast Rust programming language, it can efficiently handle a high user load.

Using Qdrant, you can create full-fledged applications with built-in encoders for tasks such as matching, searching, recommending, and more. The solution is also available as Qdrant Cloud, a fully managed version including a free tier, allowing users to easily utilize the vector search capabilities in their projects.

2. Pine cone

Pinecone is a managed vector database that has been specifically designed to address high-dimensional data challenges. With its advanced indexing and search capabilities, Pinecone enables engineers and data scientists to build and deploy large-scale machine learning applications that can efficiently process and analyze high-dimensional data.

Pinecone's key features are a fully managed, highly scalable service that enables real-time data and low-latency search. Pinecone also provides integration with LangChain to run natural language processing applications. With its specialization in high-dimensional data, Pinecone is an optimized platform for deploying efficient machine learning projects.

3. Weaviate

Weaviate is an open source vector database that allows you to store data objects and vector embeddings from your favorite ML models, easily scaling to billions of data objects. Weaviate is fast - it can quickly find the ten nearest neighbors among millions of objects in just a few milliseconds. You can vectorize data during import or load your own vectors using modules integrated with platforms such as OpenAI, Cohere, HuggingFace, and others.

Weaviate emphasizes scalability, replication, and security to ensure production readiness, from prototypes to large-scale deployments. In addition to fast vector search, Weaviate also offers recommendation, summarization, and integration with neural search engines. It is a flexible and scalable vector database for a wide variety of use cases.

4. Milvus

Milvus is a powerful open source vector database for artificial intelligence and similarity search applications. It makes searching over unstructured data more accessible and provides a consistent user experience regardless of the deployment environment.

Milvus 2.0 is a cloud native vector database where storage and computation are separated by design, and static-free components provide increased elasticity and flexibility. Released under the Apache License 2.0, Milvus offers millisecond search on trillion-dollar vector datasets, simplified management of unstructured data through rich APIs and consistent experience across environments, and built-in real-time application search. It is highly scalable and elastic, supporting on-demand component-level scaling.

Milvus combines scalar filtering with vector similarity to create a hybrid search solution. With community support and over 1,000 enterprise users, Milvus is a robust, flexible, and scalable open source vector database for a variety of use cases.

5. Fais

Faiss is an open source library for efficient similarity search and clustering of dense vectors, capable of searching massive sets of vectors larger than RAM. It contains several similarity search methods based on comparing vectors using L2 distances, dot products and cosine similarity. Some methods, such as binary vector quantization, compress vector representations to scale, while others, such as HNSW and NSG, use indexing to speed up the search.

Faiss is mainly written in C++, but fully integrates with Python/NumPy. The key algorithms are available for GPU execution, taking input from CPU or GPU memory. The GPU implementation allows CPU indexes to be replaced for faster results by automatically processing CPU-GPU copies. Developed by the Meta AI fundamental research group, Faiss is an open source toolkit that enables fast search and clustering in large vector datasets on both CPU and GPU.

Conclusion

Vector databases are quickly becoming an important component of modern artificial intelligence applications. As we explored in this article, there are several interesting options to consider when choosing a vector database in 2024. Qdrant offers universal open source capabilities, Pinecone provides a managed service designed to handle high-dimensional data, Weaviate focuses on scalability and flexibility, Milvus provides a consistent experience across environments, and faiss provides efficient similarity search with optimized algorithms.

Each database has its own strengths and advantages depending on the specific use case and infrastructure. As artificial intelligence and semantic search models evolve, the right vector database for storing, indexing, and querying vector embeddings will play a key role.