Hugging Face
데이터셋 · 27.4M 월 방문
Hugging Face는 선도적인 오픈소스 머신러닝 플랫폼이자 커뮤니티입니다. 개발자와 연구자가 최첨단 모델을 구축, 훈련 및 배포할 수 있는 도구를 제공하며, 방대한 사전 훈련된 모델, 데이터셋 및 데모 애플리케이션 허브를 제공합니다.
VS
Microsoft Open Source
플랫폼 · 210K 월 방문
Microsoft의 방대한 오픈 소스 프로젝트 포트폴리오를 발견, 사용 및 기여하기 위한 중앙 허브입니다. 개발자에게 강력한 도구, 프레임워크, AI/ML 라이브러리에 대한 액세스를 제공하여 글로벌 커뮤니티 내에서 협업과 혁신을 촉진합니다.
제품 개요
Hugging Face 제품 개요
Hugging Face는 선도적인 오픈소스 머신러닝 플랫폼이자 커뮤니티입니다. 개발자와 연구자가 최첨단 모델을 구축, 훈련 및 배포할 수 있는 도구를 제공하며, 방대한 사전 훈련된 모델, 데이터셋 및 데모 애플리케이션 허브를 제공합니다.
Preview
Microsoft Open Source 제품 개요
Microsoft의 방대한 오픈 소스 프로젝트 포트폴리오를 발견, 사용 및 기여하기 위한 중앙 허브입니다. 개발자에게 강력한 도구, 프레임워크, AI/ML 라이브러리에 대한 액세스를 제공하여 글로벌 커뮤니티 내에서 협업과 혁신을 촉진합니다.
Preview
Detailed feature comparison
| Feature | Hugging Face | Microsoft Open Source |
|---|---|---|
| 주요 카테고리 | 데이터셋 | 플랫폼 |
| 등록일 | 2025-08-17 | 2025-08-01 |
| 가격 | 프리미엄 | 무료 |
| 공식 사이트 | huggingface.co | opensource.microsoft.com |
| 제품 유형 | 웹사이트 | 웹사이트 |
| Performance data | ||
| 사용자 평점 | 확인되지 않음 | 확인되지 않음 |
| 댓글 | 0 | 0 |
| 월 방문 | 27.4M | 210K |
| 월 성장률 | -9.6% | 50.5% |
| 즐겨찾기 | 117 | 106 |
| Details | 상세 보기 | 상세 보기 |
Hugging Face vs Microsoft Open Source monthly traffic
Compare Hugging Face and Microsoft Open Source by monthly reach, traffic trend, visit depth, top regions, and acquisition sources.
How to interpret the traffic data
In the Hugging Face vs Microsoft Open Source monthly traffic comparison, Hugging Face currently shows 27.4M visits and Microsoft Open Source shows 210K; Hugging Face has about 130.3 times the visible traffic of Microsoft Open Source, an absolute difference of about 27.2M 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.
Hugging Face monthly traffic:
Latest traffic
월 방문
27.4M
평균 방문 시간
5:18
방문당 페이지
6.47
이탈률
41.95%
Data updated 2026-06-15
Monthly traffic trend
- 2025/9: 22.9M 월 방문
- 2026/1: 24.9M 월 방문
- 2026/2: 23.3M 월 방문
- 2026/3: 26.4M 월 방문
- 2026/4: 30.3M 월 방문
- 2026/5: 27.4M 월 방문
주요 지역
Top 5 countries/regions
| Country/region | Percentage | Traffic |
|---|---|---|
| 🇺🇸United States | 38.11% | 10.4M |
| 🇨🇳China | 25.84% | 7.1M |
| 🇮🇳India | 17.44% | 4.8M |
| 🇷🇺Russia | 9.32% | 2.6M |
| 🇩🇪Germany | 9.29% | 2.5M |
트래픽 소스
| Source type | Percentage | Traffic |
|---|---|---|
| 직접 | 79.44% | 21.7M |
| 리퍼럴 | 19.3% | 5.3M |
| 이메일 | 1.26% | 344.8K |
검색 키워드
deepseekdeepseek v4deepseek v4 prohugging facehuggingface
Microsoft Open Source monthly traffic:
Latest traffic
월 방문
210K
평균 방문 시간
1:01
방문당 페이지
1.94
이탈률
63.86%
Data updated 2026-06-15
Monthly traffic trend
- 2025/9: 137.2K 월 방문
- 2026/1: 72.4K 월 방문
- 2026/2: 73.7K 월 방문
- 2026/3: 117.3K 월 방문
- 2026/4: 139.5K 월 방문
- 2026/5: 210K 월 방문
주요 지역
Top 5 countries/regions
| Country/region | Percentage | Traffic |
|---|---|---|
| 🇺🇸United States | 54.77% | 115K |
| 🇮🇳India | 16.45% | 34.5K |
| 🇯🇵Japan | 9.88% | 20.7K |
| 🇨🇦Canada | 9.6% | 20.2K |
| 🇬🇧United Kingdom | 9.3% | 19.5K |
트래픽 소스
| Source type | Percentage | Traffic |
|---|---|---|
| 리퍼럴 | 50.87% | 106.8K |
| 직접 | 48.22% | 101.3K |
| 이메일 | 0.91% | 1.9K |
검색 키워드
agent governance toolkitagent os kernelazure linux 4.0microsoft agent governance toolkitversions of basic still available
Traffic-based selection guidance: If public market visibility is an important first-pass criterion, investigate Hugging Face first. The final choice should still follow taxonomy, use case, and a real trial because higher traffic does not prove broader capabilities or better workflow fit.
Usage comparison
Compare the core capabilities of Hugging Face and Microsoft Open Source
Hugging Face Core features
협업
데이터셋
머신러닝
Microsoft Open Source Core features
협업
플랫폼
머신러닝
코드 저장소
Use cases
Hugging Face Use cases
기계 학습
오픈 소스
AI 커뮤니티
컴퓨터 비전
데이터셋 호스팅
개발자 플랫폼
확산 모델
대규모 언어 모델
모델 허브
NLP
Microsoft Open Source Use cases
기계 학습
오픈 소스
AI
애저
코딩
협업
개발자 도구
프레임워크
GitHub
라이브러리
마이크로소프트
.NET
vscode
Hugging Face vs Microsoft Open Source：In-depth comparison and selection guidance
First decide whether the products solve the same kind of need
This in-depth Hugging Face vs Microsoft Open Source comparison uses only the product records, taxonomy, audience, traffic, and community signals available on this page. Hugging Face is primarily listed under “데이터셋”, while Microsoft Open Source is primarily listed under “플랫폼”, so the first decision is whether your actual task matches their recorded scope.
The structured fields currently show these decision-relevant differences: Primary category (Hugging Face: 데이터셋; Microsoft Open Source: 플랫폼); Pricing (Hugging Face: Freemium; Microsoft Open Source: Free); Monthly visits (Hugging Face: 27.4M; Microsoft Open Source: 210K); Monthly growth (Hugging Face: -9.6%; Microsoft Open Source: 50.5%); Favorites (Hugging Face: 117; Microsoft Open Source: 106). These facts are more useful for selection than brand visibility alone.
What market visibility and monthly traffic mean
In the Hugging Face vs Microsoft Open Source monthly traffic comparison, Hugging Face currently shows 27.4M visits and Microsoft Open Source shows 210K; Hugging Face has about 130.3 times the visible traffic of Microsoft Open Source, an absolute difference of about 27.2M 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.
If public market visibility is an important first-pass criterion, investigate Hugging Face first. The final choice should still follow taxonomy, use case, and a real trial because higher traffic does not prove broader capabilities or better workflow fit.
Product positioning, use cases, and roles
Hugging Face and Microsoft Open Source currently overlap in shared categories: 협업; shared tags: 기계 학습 및 오픈 소스. This can place both on the same shortlist, but it does not prove equal implementation, depth, or cost.
Hugging Face's unique categories/tags are 데이터셋, 머신러닝, AI 커뮤니티, 컴퓨터 비전, 데이터셋 호스팅, 개발자 플랫폼, 확산 모델 및 대규모 언어 모델; Microsoft Open Source's are 플랫폼, 머신러닝, 코드 저장소, AI, 애저, 코딩, 협업 및 개발자 도구. 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
Hugging Face has no verified rating, 0 comments, 117 favorites, and 126 likes；Microsoft Open Source has no verified rating, 0 comments, 106 favorites, and 102 likes。
Neither product has enough rating or comment samples for a credible reputation ranking.
Selection guidance by actual need
When to evaluate Hugging Face first
Put Hugging Face on the priority trial list when the task aligns with “데이터셋” and especially 데이터셋, 머신러닝, AI 커뮤니티, 컴퓨터 비전, 데이터셋 호스팅 및 개발자 플랫폼. This follows recorded positioning and does not imply unlisted capabilities are absent.
Hugging Face also currently records: pricing is freemium, product type is website, 27.4M 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.
When to evaluate Microsoft Open Source first
Put Microsoft Open Source on the priority trial list when the task aligns with “플랫폼” and especially 플랫폼, 머신러닝, 코드 저장소, AI, 애저 및 코딩. This follows recorded positioning and does not imply unlisted capabilities are absent.
Microsoft Open Source also currently records: pricing is free, product type is website, 210K 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 Hugging Face and Microsoft Open Source, 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.
비교 FAQ
How should I choose between Hugging Face and Microsoft Open Source?
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.