Wrike
Asana
Basecamp
Trello
monday.com
Smartsheet
Redmine
Teamwork
Qdrant
Milvus
Weaviate
pgvecto.rs
Vespa.ai
txtai
Zilliz
ElasticSearch
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.
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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 seems to be a lot more popular than Wrike. While we know about 39 links to Qdrant, we've tracked only 1 mention of Wrike. 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.
I just realized I can use this to see who is ignoring GDPR as well. Scanning a few sites I know I see a lot of tracking scripts load up right away. When I scan something like wrike.com I see good implementation of GDPR because the only thing loading is GTM / GA and trust arc as far as third-party libraries. Source: over 2 years ago
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
Asana - Asana project management is an effort to re-imagine how we work together, through modern productivity software. Fast and versatile, Asana helps individuals and groups get more done.
Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
Basecamp - A simple and elegant project management system.
Weaviate - Welcome to Weaviate
Trello - Infinitely flexible. Incredibly easy to use. Great mobile apps. It's free. Trello keeps track of everything, from the big picture to the minute details.
pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs