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Hugging Face
Datensatz · 27.4M monatliche besuche

Hugging Face ist die führende Open-Source-Plattform und Community für maschinelles Lernen. Sie bietet Entwicklern und Forschern Werkzeuge zum Erstellen, Trainieren und Bereitstellen modernster Modelle sowie einen riesigen Hub mit vortrainierten Modellen, Datensätzen und Demo-Anwendungen.

VS
ModelScope
Modell-Hub · 2.9M monatliche besuche

ModelScope ist eine Open-Source-KI-Modell-Community und -Plattform, die eine riesige Bibliothek von Modellen und Datensätzen bietet. Es stellt ein "Model-as-a-Service" (MaaS)-Ökosystem mit Werkzeugen für einfaches Modelltraining, Inferenz und Anwendungsentwicklung bereit, unterstützt durch kostenlose Rechenressourcen.

Hugging Face vs ModelScope: Preise, Funktionen und Traffic

Vergleiche Hugging Face und ModelScope nach Positionierung, Preis, Funktionen, Traffic und Nutzerfeedback.

Aktualisiert 05.08.2026

Produktübersicht

Hugging Face Produktübersicht

Hugging Face ist die führende Open-Source-Plattform und Community für maschinelles Lernen. Sie bietet Entwicklern und Forschern Werkzeuge zum Erstellen, Trainieren und Bereitstellen modernster Modelle sowie einen riesigen Hub mit vortrainierten Modellen, Datensätzen und Demo-Anwendungen.

Preview

ModelScope Produktübersicht

ModelScope ist eine Open-Source-KI-Modell-Community und -Plattform, die eine riesige Bibliothek von Modellen und Datensätzen bietet. Es stellt ein "Model-as-a-Service" (MaaS)-Ökosystem mit Werkzeugen für einfaches Modelltraining, Inferenz und Anwendungsentwicklung bereit, unterstützt durch kostenlose Rechenressourcen.

Preview

Detailed feature comparison

FeatureHugging FaceModelScope
HauptkategorieDatensatzModell-Hub
Hinzugefügt2025-08-172025-08-03
PreismodellFreemiumFreemium
Offizielle Websitehuggingface.comodelscope.cn
ProdukttypWebsiteWebsite
Performance data
NutzerbewertungNicht verifiziertNicht verifiziert
Kommentare00
Monatliche Besuche27.4M2.9M
Monatliches Wachstum-9.6%-26.3%
Favoriten117114
DetailsDetails ansehenDetails ansehen

Hugging Face vs ModelScope monthly traffic

Compare Hugging Face and ModelScope by monthly reach, traffic trend, visit depth, top regions, and acquisition sources.

How to interpret the traffic data

In the Hugging Face vs ModelScope monthly traffic comparison, Hugging Face currently shows 27.4M visits and ModelScope shows 2.9M; Hugging Face has about 9.4 times the visible traffic of ModelScope, an absolute difference of about 24.4M 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.

ModelScope is registered at the modelscope.cn/home 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.

Hugging Face monthly traffic:

Latest traffic

Monatliche Besuche
27.4M
Ø Besuchsdauer
5:18
Seiten pro Besuch
6.47
Absprungrate
41.95%
Data updated 2026-06-15

Monthly traffic trend

  • 2025/9: 22.9M Monatliche Besuche
  • 2026/1: 24.9M Monatliche Besuche
  • 2026/2: 23.3M Monatliche Besuche
  • 2026/3: 26.4M Monatliche Besuche
  • 2026/4: 30.3M Monatliche Besuche
  • 2026/5: 27.4M Monatliche Besuche

Top-Regionen

Top 5 countries/regions
Country/regionPercentageTraffic
🇺🇸United States38.11%10.4M
🇨🇳China25.84%7.1M
🇮🇳India17.44%4.8M
🇷🇺Russia9.32%2.6M
🇩🇪Germany9.29%2.5M

Traffic-Quellen

Source typePercentageTraffic
Direkt79.44%21.7M
Verweis19.3%5.3M
E-Mail1.26%344.8K

Suchbegriffe

deepseekdeepseek v4deepseek v4 prohugging facehuggingface

ModelScope monthly traffic:

Latest traffic

Monatliche Besuche
2.9M
Ø Besuchsdauer
4:55
Seiten pro Besuch
6.81
Absprungrate
35.01%
Data updated 2026-06-15

Monthly traffic trend

  • 2026/1: 2.8M Monatliche Besuche
  • 2026/2: 2.6M Monatliche Besuche
  • 2026/3: 3.5M Monatliche Besuche
  • 2026/4: 4M Monatliche Besuche
  • 2026/5: 2.9M Monatliche Besuche

Top-Regionen

Top 5 countries/regions
Country/regionPercentageTraffic
🇨🇳China88.58%2.6M
🇺🇸United States4.98%145.7K
🇭🇰Hong Kong4.52%132.2K
🇹🇼Taiwan1.19%34.8K
🇸🇬Singapore0.73%21.4K

Traffic-Quellen

Source typePercentageTraffic
Direkt83.92%2.5M
Verweis16%468.1K
E-Mail0.08%2.3K

Suchbegriffe

modelscope魔塔魔塔社区魔搭魔搭社区
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 ModelScope

Hugging Face Core features

Datensatz
Maschinelles Lernen
Zusammenarbeit

ModelScope Core features

Modell-Hub
Forschung
Low-Code No-Code

Use cases

Hugging Face Use cases

KI-Community
Computer Vision
Große Sprachmodelle
NLP
Open Source
Dataset-Hosting
Entwicklerplattform
Diffusionsmodelle
maschinelles Lernen
Modell-Hub

ModelScope Use cases

KI-Community
Computer Vision
Große Sprachmodelle
NLP
Open Source
KI-Entwicklung
Alibaba
Feinabstimmung
MaaS
Modellbibliothek

Hugging Face vs ModelScope:In-depth comparison and selection guidance

First decide whether the products solve the same kind of need

This in-depth Hugging Face vs ModelScope comparison uses only the product records, taxonomy, audience, traffic, and community signals available on this page. Hugging Face is primarily listed under “Datensatz”, while ModelScope is primarily listed under “Modell-Hub”, 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: Datensatz; ModelScope: Modell-Hub); Monthly visits (Hugging Face: 27.4M; ModelScope: 2.9M); Monthly growth (Hugging Face: -9.6%; ModelScope: -26.3%); Favorites (Hugging Face: 117; ModelScope: 114); Website (Hugging Face: huggingface.co; ModelScope: modelscope.cn). These facts are more useful for selection than brand visibility alone.

What market visibility and monthly traffic mean

In the Hugging Face vs ModelScope monthly traffic comparison, Hugging Face currently shows 27.4M visits and ModelScope shows 2.9M; Hugging Face has about 9.4 times the visible traffic of ModelScope, an absolute difference of about 24.4M 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.

ModelScope is registered at the modelscope.cn/home 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.

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 ModelScope currently overlap in shared tags: KI-Community, Computer Vision, Große Sprachmodelle, NLP und Open Source. 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 Datensatz, Maschinelles Lernen, Zusammenarbeit, Dataset-Hosting, Entwicklerplattform, Diffusionsmodelle, maschinelles Lernen und Modell-Hub; ModelScope's are Modell-Hub, Forschung, Low-Code No-Code, KI-Entwicklung, Alibaba, Feinabstimmung, MaaS und Modellbibliothek. 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;ModelScope has no verified rating, 0 comments, 114 favorites, and 118 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 “Datensatz” and especially Datensatz, Maschinelles Lernen, Zusammenarbeit, Dataset-Hosting, Entwicklerplattform und Diffusionsmodelle. 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 ModelScope first

Put ModelScope on the priority trial list when the task aligns with “Modell-Hub” and especially Modell-Hub, Forschung, Low-Code No-Code, KI-Entwicklung, Alibaba und Feinabstimmung. This follows recorded positioning and does not imply unlisted capabilities are absent.

ModelScope also currently records: pricing is freemium, product type is website, 2.9M 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.

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 ModelScope, 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.

Vergleichs-FAQ

How should I choose between Hugging Face and ModelScope?
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.