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 Scratchpad. While we know about 40 links to Qdrant, we've tracked only 3 mentions of Scratchpad. 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.
Scratchpad (https://scratchpad.com/) | REMOTE (US/Canada) | Full-time | Senior Backend Engineer, Senior Software Engineer - Backend Apply here: https://www.scratchpad.com/careers?ashby_jid=7b986939-6b0d-4365-98f8-9df4c6d83002 About Scratchpad At Scratchpad, our mission is to make salespeople happy. We believe we can do that by reducing complexity and creating delightful experiences, which has led us to pioneer the... - Source: Hacker News / over 1 year ago
You should definitely check out: https://scratchpad.com/. Source: over 2 years ago
Congrats on the new launch Iba and Syed, I still remember meeting you guys years ago in LA, you've come a long way! I'll echo some of the other comments here that most of the bigger companies I've worked with want a better JIRA but are weary of alternatives, data security, and the switching costs involved. Is there a way to offer this on top of the existing Atlassian tools? A similar model would be... - Source: Hacker News / almost 3 years ago
Vector Databases: Qdrant for efficient data storage and retrieval. - Source: dev.to / 7 days 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 / about 1 month 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 / about 2 months 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 / about 2 months 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 / 3 months ago
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