Software Alternatives, Accelerators & Startups

Qdrant VS Meilisearch

Compare Qdrant VS Meilisearch 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/

Meilisearch logo Meilisearch

Ultra relevant, instant, and typo-tolerant full-text search API
  • 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.

  • Meilisearch Landing page
    Landing page //
    2023-12-16

Meilisearch is a powerful, fast, open-source, easy to use, and deploy search engine. The search and indexation are fully customizable and handles features like typo-tolerance, filters, and synonyms.

Qdrant

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

Meilisearch

Pricing URL
-
$ Details
Platforms
-
Release Date
-

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

Meilisearch features and specs

No features have been listed yet.

Category Popularity

0-100% (relative to Qdrant and Meilisearch)
Search Engine
41 41%
59% 59
Custom Search Engine
18 18%
82% 82
Databases
100 100%
0% 0
Custom Search
0 0%
100% 100

Questions and Answers

As answered by people managing Qdrant and Meilisearch.

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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Reviews

These are some of the external sources and on-site user reviews we've used to compare Qdrant and Meilisearch

Qdrant Reviews

We have no reviews of Qdrant yet.
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Meilisearch Reviews

5 Open-Source Search Engines For your Website
MeiliSearch is an open-source, blazingly fast and hyper-relevant search engine that will improve your search experience. It provides an extensive toolset for customization. It works out-of-the-box with a preset that easily answers the needs of most applications. Communication is done with a RESTful API because most developers are already familiar with its norms.
Source: vishnuch.tech
MeiliSearch: Zero-config alternative to Elasticsearch, made in Rust | Hacker News
"We send events to our Amplitude instance to be aware of the number of people who use MeiliSearch. We only send the platform on which the server runs once by day. No other information is sent. If you do not want us to send events, you can disable these analytics by using the MEILI_NO_ANALYTICS env variable."
Recommendations for Poor Man's ElasticSearch on AWS?
I'd second these two. I've been following them for quite some time. I even did an extensive research on which one I'd use, and I ended up with Typesense. I don't remember the specific reasoning though. Both seem quite good. MeiliSearch is written in Rust, which makes it more "hipsterish" ;)

Social recommendations and mentions

Based on our record, Qdrant seems to be a lot more popular than Meilisearch. While we know about 39 links to Qdrant, we've tracked only 3 mentions of Meilisearch. 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 / 3 days 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 / 14 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 / 21 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

Meilisearch mentions (3)

  • Show HN: Hyvor Blogs – Multi-language blogging platform
    Meilisearch [https://meilisearch.com] for the search index. - Source: Hacker News / about 1 year ago
  • Meilisearch, the Rust search engine, just raised $5M
    Meilisearch is an open-source, lightning-fast, and hyper-relevant search engine that fits effortlessly into your apps, websites, and workflow. You can find more info on our website https://meilisearch.com. Source: over 2 years ago
  • Search engines for website
    Algolia.com - new plans are very affordable Meilisearch.com - open source. Source: about 3 years ago

What are some alternatives?

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

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

Algolia - Algolia's Search API makes it easy to deliver a great search experience in your apps & websites. Algolia Search provides hosted full-text, numerical, faceted and geolocalized search.

Weaviate - Welcome to Weaviate

Typesense - Typo tolerant, delightfully simple, open source search 🔍

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

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