Introduction
In recent times, Vector Databases have gained quite a popularity, especially after the arrival of the RAG architecture to work efficiently with LLMs. The concept of vector databases is not new, however, as they were already used in recommendation engines, personalization, Ad targeting, etc. Vector databases are used to save, index, and retrieve complex data like text, images, or other unstructured formats in vectors. The vectors are mathematical representations of data in a high-dimensional space enabling high-quality similarity and semantic searches on complex data types.
Now there are many types of vector databases in the market that are both open source and proprietary with different characteristics. In this post, we will go through different vector database options that are available along with their features to help you make the right choice for your needs.
1. Pinecone
Pinecone is a fully managed Vector DB that provides seamless integration with various machine learning frameworks, real-time querying capabilities, and efficient handling of high-dimensional vector data. Its key features are –
- Serverless Architecture
- Provides third-party integration with various frameworks & platforms
- Supported languages are Python, JavaScript/TypeScript, Java, Go
- Real-time Updates, Search and Analysis
- Highly Scalable with billions of vectors and low latency
- Easy to use with API integrations.
- Supports hybrid search with both vectors and keywords.
- Allows filter of vector search results with metadata for better accuracy.
License: Proprietary, but offers a free starter plan with limited features.
2. Milvus
Milvus is a highly scalable distributed Vector database, suitable for large-scale applications and offers both cloud-native and standalone deployments. Its key features are –
- Optimized to scale with large-scale vector data
- Distributed architecture to provide scalability and reliability
- Offers tunable consistency to balance between query performance and data freshness
- Supports both standalone and distributed deployments
- Supports integration with popular machine learning framework
- Integration with cloud-native architecture
- Supports multiple languages Python, Java, Go, C#, and Node. js
- Offers over 10 types of index types to optimize searches as per requirement
- Multiple search capabilities like top-K Approximate Nearest Neighbor (ANN), Range ANN, etc. with metadata filtering
- Supports hardware acceleration with GPU
License: Opensource under Apache 2.0 License but also offers a fully managed cloud version with pricing.
3. FAISS (Facebook AI Similarity Search)
FAISS is a vector DB open-sourced by Facebook designed for efficient similarity search and clustering of dense vectors. It can be used for quick prototyping or small or medium scale applications but may not be suitable for large-scale enterprise applications. The key features of FAISS are –
- Written in C++ but offers Python wrapper
- Support hardware acceleration with GPU
- Highly optimized for both CPU and GPU
- Scalable for billions of vector datasets
- Offers multiple indexing methods
License: Opensource
4. Weaviate
Weviate is a highly performant vector database that uses semantic properties to store and retrieve data. It saves data both as an object and as a vector allowing for hybrid search. The key features of Weaviate are –
- Fast queries with sub-millisecond retrieval for millions of data
- Supports hybrid search with both vector and keyword search
- Real-time updation of records
- Supports horizontal scaling
- Supports Python, Go, Javascript/Typescript
- Provides GraphQL endpoints to access data
- Provides various 3rd party integrations
- Offers managed cloud as part of paid plans
License: Opensource, but also has managed/cloud offerings with pricing.
5. Vespa
Vespa is a versatile search engine and vector database suitable for designing large-scale production-grade applications. The key features of Vespa vector DB are –
- Capable of low latency searches with high load data
- Real-time indexing and analysis of records
- Out-of-the-box integration with machine learning models
- Hybrid search using vectors and lexical properties
- Can search structured data
- Highly scalable and available
- Provides managed platform in paid plans
License: Opensource under Apache 2.0 License but also offers managed service with pricing.
6. Qdrant
Qdrant is a vector search database that provides production-ready services as API to store and search data as vectors. It also allows to attach extra payload to vectorized data that can be used as metadata to refine search qualities. Other key features of Qdrant vector DB are –
- Optimized for speed and memory efficiency
- Advanced data compression for faster search queries
- Provides various REST APIs to interact with the database
- Supported languages are Python, Typescript, Java, C#, Rust
- Various 3rd party integrations with frameworks & platforms
- Offers managed service in paid plan with hybrid & private cloud options.
License: Opensource under Apache 2.0 License, but also has managed/cloud offerings with pricing.
7. Vald
Vald is a distributed vector database based on cloud-native architecture. The key features of Vald are –
- Designed to run on Kubernetes
- Highly scalable with billions of data using horizontal scaling
- Distributed indexing with replication
- Automatic indexing and backup
- Supports multiple languages like Go, Java, Clojure, Node.js, and Python.
- Supports REST API and gRPC API
License: Opensource under Apache 2.0 License
8. Chroma DB
Chroma DB is a simple yet powerful vector DB that can be used for quick prototyping and production applications. The key features of Chroma DB are –
- Supports multiple languages including Python, Javascript, Java, Go, PHP, C#, and more.
- Support for various embedding models
- Can store and query both embeddings and metadata
- Supports both running in-memory or persisting on disk
- Easy integration with popular ML frameworks and LLM applications
License: Opensource under Apache 2.0 License
9. Elasticsearch
Elasticsearch is a popular search engine that has extended its platform for vector search capabilities. Its key features are –
- Supports hybrid query with text and vector search
- Supports generation of embeddings
- It is highly scalable due to its distributed design
- Easy integration with the existing Elasticsearch ecosystem
License: Opensource under Apache 2.0 License, but also has managed/cloud offerings with pricing.
