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.
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.
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.
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.
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.
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.
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