Machine Learning · 636.1M monthly visits

Papers with Code is a free, open resource for machine learning researchers and developers. It connects scientific papers to their corresponding open-source code, making research more accessible and reproducible. The platform features state-of-the-art leaderboards, browsable datasets, and a comprehensive collection of AI research, helping users track progress, find implementations, and accelerate their work. It is an essential tool for anyone in the AI/ML community.

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Qdrant

Vector Search · 300.2K monthly visits

Qdrant is a high-performance, open-source vector database and similarity search engine built in Rust. It's designed to power next-generation AI applications by efficiently managing and searching billions of high-dimensional vectors. With advanced features like rich filtering, payload storage, and various quantization methods, Qdrant enables developers to build scalable and cost-effective solutions for semantic search, recommendation systems, and Retrieval Augmented Generation (RAG).

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Papers with Code vs Qdrant: pricing, features, traffic, and use cases

Compare Papers with Code and Qdrant across positioning, pricing, traffic, and user feedback using structured factual data.

Updated Aug 5, 2026

Product overview

Papers with Code Product overview

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Qdrant Product overview

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Detailed feature comparison

Feature	Papers with Code	Qdrant
Primary category	Machine Learning	Vector Search
Added	2025-08-07	2025-08-15
Pricing	Free	Freemium
Official website	github.com	qdrant.tech
Product type	Website	Website
Performance data
User rating	Not verified	Not verified
Comments	0	0
Monthly visits	636.1M	300.2K
Monthly growth	0.8%	-4.9%
Favorites	99	132
Details	View details	View details

Papers with Code vs Qdrant monthly traffic

Compare Papers with Code and Qdrant by monthly reach, traffic trend, visit depth, top regions, and acquisition sources.

How to interpret the traffic data

In the Papers with Code vs Qdrant monthly traffic comparison, Papers with Code currently shows 636.1M visits and Qdrant shows 300.2K; Papers with Code has about 2,118.6 times the visible traffic of Qdrant, an absolute difference of about 635.8M visits. This reflects visible reach, not feature quality or paid users.

Both tools provide verified traffic details, so monthly trends, visit depth, regions, and acquisition sources can be compared on the same basis.

Papers with Code is registered at the github.com/paperswithcode subpage. Because the current data does not state whether other host pages are excluded, treat this as domain-level context rather than independent traffic for one product or project.

Papers with Code monthly traffic:

Latest traffic

Monthly visits

636.1M

Avg. visit duration

6:23

Pages per visit

5.92

Bounce rate

36.46%

Data updated 2026-06-15

Monthly traffic trend

Top regions

Top 5 countries/regions

Country/region	Percentage	Traffic
🇺🇸United States	36.14%	229.9M
🇨🇳China	22.96%	146M
🇮🇳India	17.41%	110.7M
🇷🇺Russia	15.84%	100.8M
🇩🇪Germany	7.65%	48.7M

Traffic sources

Source type	Percentage	Traffic
Direct	82.14%	522.5M
Referral	16.14%	102.7M
Email	1.72%	10.9M

Search keywords

githubgithub copilothermes agentzapretзапрет

Qdrant monthly traffic:

Latest traffic

Monthly visits

300.2K

Avg. visit duration

0:59

Pages per visit

1.93

Bounce rate

50.32%

Data updated 2026-06-15

Monthly traffic trend

Top regions

Top 5 countries/regions

Country/region	Percentage	Traffic
🇮🇳India	40.64%	122K
🇺🇸United States	22.09%	66.3K
🇨🇳China	18.45%	55.4K
🇬🇧United Kingdom	9.42%	28.3K
🇩🇪Germany	9.4%	28.2K

Traffic sources

Source type	Percentage	Traffic
Direct	77.33%	232.2K
Referral	20.35%	61.1K
Email	2.32%	7K

Search keywords

aiqdrantqdrant cloudqdrant vector databasequadrant

Traffic-based selection guidance: Papers with Code is registered under a github.com subpath, so its large visible total may include the host platform. The current data does not justify choosing Papers with Code for traffic alone; shortlist by unique taxonomy and use case, then validate with the same tasks.

Usage comparison

Compare the core capabilities of Papers with Code and Qdrant

Papers with Code Core features

Machine Learning

Code Repository

Learning Platform

Academic

Qdrant Core features

Machine Learning

Vector Search

Databases

Use cases

Papers with Code Use cases

machine learning

open source

AI research

benchmarks

code implementation

computer science

datasets

deep learning

research papers

SOTA

state-of-the-art

Qdrant Use cases

machine learning

open source

AI infrastructure

developer tools

RAG

recommendation engine

rust

semantic search

similarity search

vector database

Papers with Code vs Qdrant：In-depth comparison and selection guidance

First decide whether the products solve the same kind of need

This in-depth Papers with Code vs Qdrant comparison uses only the product records, taxonomy, audience, traffic, and community signals available on this page. Papers with Code is primarily listed under “Machine Learning”, while Qdrant is primarily listed under “Vector Search”, so the first decision is whether your actual task matches their recorded scope.

The structured fields currently show these decision-relevant differences: Primary category (Papers with Code: Machine Learning; Qdrant: Vector Search); Pricing (Papers with Code: Free; Qdrant: Freemium); Monthly visits (Papers with Code: 636.1M; Qdrant: 300.2K); Monthly growth (Papers with Code: 0.8%; Qdrant: -4.9%); Favorites (Papers with Code: 99; Qdrant: 132). These facts are more useful for selection than brand visibility alone.

What market visibility and monthly traffic mean

Both tools provide verified traffic details, so monthly trends, visit depth, regions, and acquisition sources can be compared on the same basis.

Papers with Code is registered under a github.com subpath, so its large visible total may include the host platform. The current data does not justify choosing Papers with Code for traffic alone; shortlist by unique taxonomy and use case, then validate with the same tasks.

Product positioning, use cases, and roles

Papers with Code and Qdrant currently overlap in shared categories: Machine Learning; shared tags: machine learning and open source. This can place both on the same shortlist, but it does not prove equal implementation, depth, or cost.

Papers with Code's unique categories/tags are Code Repository, Learning Platform, Academic, AI research, benchmarks, code implementation, computer science, and datasets; Qdrant's are Vector Search, Databases, AI infrastructure, developer tools, RAG, recommendation engine, rust, and semantic search. These unique fields are the strongest differentiators: validate the product whose recorded scope matches the task instead of following traffic alone.

What ratings, comments, and favorites can tell you

Papers with Code has no verified rating, 0 comments, 99 favorites, and 92 likes；Qdrant has no verified rating, 0 comments, 132 favorites, and 117 likes。

Neither product has enough rating or comment samples for a credible reputation ranking.

Selection guidance by actual need

When to evaluate Papers with Code first

Put Papers with Code on the priority trial list when the task aligns with “Machine Learning” and especially Code Repository, Learning Platform, Academic, AI research, benchmarks, and code implementation. This follows recorded positioning and does not imply unlisted capabilities are absent.

Papers with Code also currently records: pricing is free, product type is website, 636.1M monthly visits shown for the registered host (subpage scope unknown), no verified user rating. Verify any hard requirement around price, platform, or reach before trial, and do not let sparse review data substitute for testing.

When to evaluate Qdrant first

Put Qdrant on the priority trial list when the task aligns with “Vector Search” and especially Vector Search, Databases, AI infrastructure, developer tools, RAG, and recommendation engine. This follows recorded positioning and does not imply unlisted capabilities are absent.

Qdrant also currently records: pricing is freemium, product type is website, 300.2K verified monthly visits, no verified user rating. Verify any hard requirement around price, platform, or reach before trial, and do not let sparse review data substitute for testing.

How to validate the recommendation before deciding

The available data describes positioning, public visibility, and community signals, but it cannot prove output quality, speed, integration effort, privacy, or long-term cost in your workflow. Before deciding, run the same representative tasks in Papers with Code and Qdrant, then record completion time, accuracy, manual corrections, and the real paid threshold. A like-for-like trial turns this comparison into a defensible adoption decision.

Comparison FAQ

How should I choose between Papers with Code and Qdrant?

Compare positioning, pricing, taxonomy, and traffic maturity, then verify the latest details on each official website.

Where does this comparison data come from?

The factual baseline is derived from product, taxonomy, traffic, and community data. Reviewed editorial conclusions show their source and verification date.

What do unknown fields mean?

Unknown means there is not enough reliable evidence; the page does not fill gaps with assumptions.