Qdrant
Milvus
Weaviate
pgvecto.rs
Vespa.ai
txtai
ElasticSearch
Pinecone
Zilliz
Weaviate
Milvus
txtai
EVA DB
Vector Vault
Vespa.ai
Pinecone
Qdrant is a leading open-source high-performance Vector Database written in Rust with extended metadata filtering support and advanced features. It deploys as an API service providing a search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications. Powering vector similarity search solutions of any scale due to a flexible architecture and low-level optimization. Qdrant is trusted and high-rated by Machine Learning and Data Science teams of top-tier companies worldwide.
Zilliz Cloud is a fully managed vector database based on the popular open-source Milvus. Zilliz Cloud helps to unlock high-performance similarity searches with no previous experience or extra effort needed for infrastructure management. It is ultra-fast and enables 10x faster vector retrieval, a feat unparalleled by any other vector database management system. Zilliz includes support for multiple vector search indexes, built-in filtering, and complete data encryption in transit, a requirement for enterprise-grade applications. Zilliz is a cost-effective way to build similarity search, recommender systems, and anomaly detection into applications to keep that competitive edge.
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Qdrant's answer
Advanced Features, Performance, Scalability, Developer Experience, and Resources Saving.
Qdrant's answer
Highest performance https://qdrant.tech/benchmarks/, scalability and ease of use.
Qdrant's answer
Qdrant is written completely in Rust. SDKs available for all popular languages Python, Go, Rust, Java, .NET, etc.
Based on our record, Qdrant should be more popular than Zilliz. It has been mentiond 39 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
AgentCloud uses Qdrant as the vector store to efficiently store and manage large sets of vector embeddings. For a given user query the RAG application fetches relevant documents from vector store by analyzing how similar their vector representation is compared to the query vector. - Source: dev.to / 9 days ago
Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker. - Source: dev.to / 20 days ago
I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours. - Source: dev.to / 27 days ago
I'm currently looking to implement locally, using QDrant [1] for instance. I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2]. [1]. https://qdrant.tech/. - Source: Hacker News / about 2 months ago
There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb. - Source: Hacker News / 2 months ago
If you find yourself unsure about the optimization process, leverage the power of benchmarking tools like VectorDBBench. This tool, developed and open-sourced by Zilliz, can evaluate all mainstream vector databases. It allows you to conduct comprehensive experiments and fine-tune your system for optimal performance. - Source: dev.to / 23 days ago
Last week I celebrated my first year at Zilliz 🎉, the startup behind the open source vector database Milvus, in the heart of the AI boom. Somehow, the year has been both the shortest and longest year of my 17 years in the software industry. It seems like a prudent time to stop, catch my breath, and reflect on what I’ve learned. - Source: dev.to / 3 months ago
Zilliz is a powerful vector database designed to empower developers and data scientists in building the next generation of AI and search applications. It offers a robust platform for scalable, efficient, and accurate vector search and analytics, supporting a wide array of AI-driven applications. - Source: dev.to / 3 months ago
As an open-source and self-hosted solution, developers can deploy their own version of the plugin and register it with ChatGPT. The plugin leverages OpenAI embeddings and allows developers to choose a vector database (Milvus, Pinecone, Qdrant, Redis, Weaviate or Zilliz) for indexing and searching documents. Information sources can be synchronized with the database using webhooks. Source: 11 months ago
Zilliz | Onsite/Hybrid (Redwood City, CA, USA) - https://zilliz.com/ Zilliz is the company behind Milvus, the world's most popular open-source vector database. We're hiring engineers, developer advocates, product marketing, and product managers. You can view and apply for open roles at https://zilliz.com/careers, or feel free to reach out to me directly. - Source: Hacker News / about 1 year ago
Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
Weaviate - Welcome to Weaviate
pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs
txtai - AI-powered search engine
Vespa.ai - Store, search, rank and organize big data
EVA DB - EVA AI-Relational Database System | SQL meets Deep Learning
