Software Alternatives, Accelerators & Startups

Qdrant VS Milvus

Compare Qdrant VS Milvus and see what are their differences

Qdrant logo Qdrant

Qdrant is a high-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

Milvus logo Milvus

Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
  • Qdrant Landing page
    Landing page //
    2023-12-20

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.

  • Milvus Landing page
    Landing page //
    2022-12-01

Milvus is a highly flexible, reliable, and blazing-fast cloud-native, open-source vector database. It powers embedding similarity search and AI applications and strives to make vector databases accessible to every organization. Milvus can store, index, and manage a billion+ embedding vectors generated by deep neural networks and other machine learning (ML) models. This level of scale is vital to handling the volumes of unstructured data generated to help organizations to analyze and act on it to provide better service, reduce fraud, avoid downtime, and make decisions faster.

Milvus is a graduated-stage project of the LF AI & Data Foundation.

Qdrant

$ Details
freemium
Platforms
Linux Windows Kubernetes Docker
Release Date
2021 May

Milvus

Website
github.com
Pricing URL
-
$ Details
free
Platforms
-
Release Date
2019 October

Qdrant features and specs

  • Advanced Filtering: Yes
  • On-disc Storage: Yes
  • Scalar Quantization: Yes
  • Product Quantization: Yes
  • Binary Quantization: Yes
  • Sparse Vectors: Yes
  • Hybrid Search: Yes
  • Discovery API: Yes
  • Recommendation API: Yes

Milvus features and specs

No features have been listed yet.

Qdrant videos

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Milvus videos

End to End Tutorial on Milvus Lite

More videos:

  • Demo - An Introduction To the Milvus Open Source Vector Database

Category Popularity

0-100% (relative to Qdrant and Milvus)
Search Engine
53 53%
47% 47
Databases
63 63%
37% 37
Custom Search Engine
41 41%
59% 59
Vector Databases
0 0%
100% 100

Questions and Answers

As answered by people managing Qdrant and Milvus.

Why should a person choose your product over its competitors?

Qdrant's answer

Advanced Features, Performance, Scalability, Developer Experience, and Resources Saving.

What makes your product unique?

Qdrant's answer

Highest performance https://qdrant.tech/benchmarks/, scalability and ease of use.

Which are the primary technologies used for building your product?

Qdrant's answer

Qdrant is written completely in Rust. SDKs available for all popular languages Python, Go, Rust, Java, .NET, etc.

User comments

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Social recommendations and mentions

Qdrant might be a bit more popular than Milvus. We know about 39 links to it since March 2021 and only 34 links to Milvus. 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.

Qdrant mentions (39)

  • How to Build a Chat App with Your Postgres Data using Agent Cloud
    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 / 1 day ago
  • Hindi-Language AI Chatbot for Enterprises Using Qdrant, MLFlow, and LangChain
    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 / 12 days ago
  • Boost Your Code's Efficiency: Introducing Semantic Cache with Qdrant
    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 / 19 days ago
  • Ask HN: Has Anyone Trained a personal LLM using their personal notes?
    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 1 month ago
  • Open-source Rust-based RAG
    There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb. - Source: Hacker News / 2 months ago
View more

Milvus mentions (34)

  • Ask HN: Who is hiring? (April 2024)
    Zilliz (zilliz.com) | Hybrid/ONSITE (SF, NYC) | Full-time I am part of the hiring team for DevRel NYC - https://boards.greenhouse.io/zilliz/jobs/4307910005 SF - https://boards.greenhouse.io/zilliz/jobs/4317590005 Zilliz is the company behind Milvus (https://github.com/milvus-io/milvus), the most starred vector database on GitHub. Milvus is a distributed vector... - Source: Hacker News / about 1 month ago
  • Ask HN: Who is hiring? (February 2024)
    Zilliz is hiring! We're looking for REMOTE and/or HYBRID roles in SF Zilliz is the company behind Milvus (https://github.com/milvus-io/milvus. - Source: Hacker News / 3 months ago
  • Qdrant, the Vector Search Database, raised $28M in a Series A round
    Congrats to Qdrant's team, $28M for a Series is really nice. There are a lot of OSS vector search databases out there, we could probably list the main ones: - Qdrant https://github.com/qdrant/qdrant - Milvus https://github.com/milvus-io/milvus What else? - Source: Hacker News / 4 months ago
  • Open Source Advent Fun Wraps Up!
    But before we do, I do want to say that 🤩 all these lovely Open-Source projects would love a little 🎉💕 love by getting a GitHub star ⭐ for their efforts. Including Open Source Milvus 🥰. - Source: dev.to / 4 months ago
  • Milvus Adventures Dec 15, 2023
    We are celebrating 25 different open source projects during the Open Source Advent this month! You can earn points all month long for a chance to win an exclusive swag pack from Zilliz and the participating projects! It’s a great chance to learn new skills and have some winter fun. Today is the first day and we are featuring Milvus! You can join us in our Discord channel or check us out on GitHub! We'd love a ⭐... - Source: dev.to / 5 months ago
View more

What are some alternatives?

When comparing Qdrant and Milvus, you can also consider the following products

Weaviate - Welcome to Weaviate

pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs

Vespa.ai - Store, search, rank and organize big data

txtai - AI-powered search engine

ElasticSearch - Elasticsearch is an open source, distributed, RESTful search engine.

Zilliz - Data Infrastructure for AI Made Easy