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Milvus VS Quickwit

Compare Milvus VS Quickwit and see what are their differences

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LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Milvus logo Milvus

Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.

Quickwit logo Quickwit

Open-source & cloud-native log management & analytics
  • 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.

  • Quickwit Landing page
    Landing page //
    2022-11-02

Milvus features and specs

  • High Performance
    Milvus is designed to manage and process large-scale vector data extremely fast, making it suitable for handling real-time processing of massive datasets.
  • Scalability
    Milvus supports horizontal scaling, ensuring that as the data grows, the system can scale out by adding more nodes to maintain performance.
  • Flexible Deployment
    Milvus can be deployed on-premises, on cloud services, or in hybrid environments, providing flexibility for different infrastructure needs.
  • Community and Support
    As an open-source project, Milvus has a strong community and support network, including comprehensive documentation and active community forums.
  • Rich Ecosystem
    Milvus integrates well with various machine learning and data processing tools, such as TensorFlow, PyTorch, and other AI frameworks, facilitating seamless workflows.
  • Built-in Indexing
    Milvus provides built-in indexing capabilities like IVF, HNSW, and ANNOY, which enhance the speed and efficiency of similarity searches on vector data.

Possible disadvantages of Milvus

  • Steep Learning Curve
    The complexity of vector databases and the need for understanding high-dimensional indexing techniques may pose a challenging learning curve for new users.
  • Resource Intensive
    Milvus can be resource-intensive in terms of CPU and memory, especially for large-scale deployments, which may lead to higher operational costs.
  • Evolving Project
    As a relatively new project, Milvus is rapidly evolving, and users might encounter changing APIs or features that could disrupt ongoing projects.
  • Dependency Management
    Deploying Milvus with its dependencies (such as certain hardware requirements for optimal performance) can be complex, necessitating careful planning and management.
  • Limited Use Cases
    Given its specialization in vector similarity searches, Milvus might not be the best choice for applications needing comprehensive relational database capabilities.

Quickwit features and specs

  • Scalability
    Quickwit is designed to handle large-scale data and can efficiently manage data distribution across multiple nodes.
  • Fast Ingestion
    It supports quick data ingestion, which makes it suitable for applications requiring real-time or near-real-time data processing.
  • Efficient Querying
    Optimized for fast search operations which can significantly reduce the time required to query large datasets.
  • Open Source
    Being open source, Quickwit allows users to contribute to the code base, customize it according to their needs, and avoid vendor lock-in.
  • Lower Resource Usage
    Designed to be memory-efficient, Quickwit minimizes resource consumption compared to other search tools.

Possible disadvantages of Quickwit

  • Maturity
    As a relatively new project, Quickwit may not be as mature as other well-established search platforms, which can affect stability and feature set.
  • Community Support
    It may have a smaller community compared to other open-source search engines, which can limit resources for troubleshooting and community engagement.
  • Limited Ecosystem
    The ecosystem of plugins and integrations might be limited compared to more established platforms like Elasticsearch.
  • Learning Curve
    New users or those accustomed to other technologies might face a learning curve in understanding and implementing Quickwitโ€™s functionalities.

Analysis of Milvus

Overall verdict

  • Milvus is generally regarded as a good option, especially for businesses and developers working in the field of AI and data science. Its open-source nature allows for flexibility and community support, and it is backed by a solid architecture designed for scalability and efficiency.

Why this product is good

  • Milvus is considered a strong choice for handling large-scale vector data due to its high-performance capabilities and ability to manage similarity search effectively. It is particularly well-suited for applications involving AI, machine learning, and deep learning where vector operations are common.

Recommended for

    Milvus is ideal for data scientists, AI researchers, and engineers who require efficient and scalable vector search solutions. It is also recommended for companies and projects dealing with recommendation systems, image and video search, natural language processing, and more.

Milvus videos

+ Add

End to End Tutorial on Milvus Lite

More videos:

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

Quickwit videos

No Quickwit videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to Milvus and Quickwit)

Milvus

Quickwit

Search Engine
60 60%
Search Engine
40% 40
Vector Databases
100 100%
Vector Databases
0% 0
Open Source
0 0%
Open Source
100% 100
Databases
100 100%
Databases
0% 0

User comments

Share your experience with using Milvus and Quickwit. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, Milvus should be more popular than Quickwit. It has been mentiond 40 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.

Milvus mentions (40)

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Quickwit mentions (14)

  • HorizonDB, a geocoding engine in Rust that replaces Elasticsearch
    Nice... it's cool to see how different companies are putting together best fit solutions. I'm also glad that they at least started out with off the shelf apps instead of jumping to something like a bespoke solution early on. Quickwit[1] looks interesting, found via Tantivity reference. Kind of like ES w/ Lucene. 1. https://github.com/quickwit-oss/quickwit. - Source: Hacker News / about 1 year ago
  • Tantivy โ€“ full-text search engine library inspired by Apache Lucene
    Https://github.com/quickwit-oss/quickwit to_tsvector in PG never worked well for my use cases SELECT * FROM dump WHERE to_tsvector('english'::regconfig, hh_fullname) @@ to_tsquery('english'::regconfig, 'query'); Wish them to succeed. Will automatically upvote any post Tantivy as keyword. - Source: Hacker News / about 2 years ago
  • S3 Express Is All You Need
    We tested S3 Express for our search engine quickwit[0] a couple of weeks ago. While this was really satisfying on the performance side, we were a bit disappointed by the price, and I mostly agree with the article on this matter. I can see some very specific use cases where the pricing should be OK but currently, I would say most of our users should just stay on the classic S3 and add some local SSD caching if they... - Source: Hacker News / over 2 years ago
  • Ask HN: Who is hiring? (September 2023)
    Quickwit (https://quickwit.io/) | Paris, France | Onsite and remote (based in Europe) | Full-time The company is fully remote but we also have a small office in Paris. We prefer candidates based in Europe but can make exceptions for the right profiles. - Senior Software Engineer 80-110kโ‚ฌ + 0.25-1% equity based on experience. 
        Weโ€™re looking for a senior software engineer to contribute to...
    - Source: Hacker News / almost 3 years ago
  • Show HN: Quickwit โ€“ Cost-efficient Elasticsearch alternative on object storage
    - Another nice comment seen on HN ยซ it seems to be very easy to run, not very IO intensive, and running fine on a single node with modest hardware with >2 billion log rows. It has a really cool dynamic schema feature too.ยป [9] Fun fact: at least 4 users are using Garage[10] as the object storage, this OSS project looks really promising and made the HN front page a few months ago[11], we really cherish the OSS for... - Source: Hacker News / about 3 years ago
View more

What are some alternatives?

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

Pinecone - Search through billions of items for similar matches to any object, in milliseconds. Itโ€™s the next generation of search, an API call away.

Tantivy - ๐ŸŽ On average 2x faster than Lucene ๐Ÿ”Ž Full-text search โš™๏ธ Configurable tokenizer (stemming available for 17 languages) ๐Ÿš€ Tiny startup time (<10ms) โŒจ๏ธ Natural and Phrase Queries ไทด Range Queries ๐Ÿ›  Incremental Indexing ๐Ÿ’จ Multi-threaded Indexing ๐Ÿ”ฉ JSON Fโ€ฆ

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/

Typesense - Typo tolerant, delightfully simple, open source search ๐Ÿ”

Weaviate - Welcome to Weaviate

OpenSearch - OpenSearch is a community-driven, open source search and analytics suite derived from Apache 2.0 licensed Elasticsearch 7.10.2 & Kibana 7.10.2. It consists of a search engine daemon, and a visualization and user interface, OpenSearch Dashboards.

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