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Ludwig
Modelltraining · 6.6K monatliche besuche

Ludwig ist ein Low-Code, Open-Source Deep-Learning-Framework, das die Erstellung und das Training von benutzerdefinierten KI-Modellen vereinfacht. Mithilfe deklarativer YAML-Konfigurationen können Benutzer problemlos komplexe Modelle, einschließlich LLMs, für multimodales und Multi-Task-Lernen erstellen, ohne umfangreichen Boilerplate-Code schreiben zu müssen. Es ist auf Skalierbarkeit und Produktionsreife ausgelegt und integriert sich in beliebte Tools wie HuggingFace und MLFlow.

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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.

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Ludwig vs ModelScope: Preise, Funktionen und Traffic

Vergleiche Ludwig und ModelScope nach Positionierung, Preis, Funktionen, Traffic und Nutzerfeedback.

Aktualisiert 05.08.2026

Produktübersicht

Ludwig Produktübersicht

Ludwig ist ein Low-Code, Open-Source Deep-Learning-Framework, das die Erstellung und das Training von benutzerdefinierten KI-Modellen vereinfacht. Mithilfe deklarativer YAML-Konfigurationen können Benutzer problemlos komplexe Modelle, einschließlich LLMs, für multimodales und Multi-Task-Lernen erstellen, ohne umfangreichen Boilerplate-Code schreiben zu müssen. Es ist auf Skalierbarkeit und Produktionsreife ausgelegt und integriert sich in beliebte Tools wie HuggingFace und MLFlow.

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

FeatureLudwigModelScope
HauptkategorieModelltrainingModell-Hub
Hinzugefügt2025-08-072025-08-03
PreismodellKostenlosFreemium
Offizielle Websiteludwig.aimodelscope.cn
ProdukttypWebsiteWebsite
Performance data
NutzerbewertungNicht verifiziertNicht verifiziert
Kommentare00
Monatliche Besuche6.6K2.9M
Monatliches Wachstum3.4%-26.3%
Favoriten83114
DetailsDetails ansehenDetails ansehen

Ludwig vs ModelScope monthly traffic

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

How to interpret the traffic data

In the Ludwig vs ModelScope monthly traffic comparison, Ludwig currently shows 6.6K visits and ModelScope shows 2.9M; ModelScope has about 446.3 times the visible traffic of Ludwig, an absolute difference of about 2.9M 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.

Ludwig is registered at the ludwig.ai/latest subpage; 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.

Ludwig monthly traffic:

Latest traffic

Monatliche Besuche
6.6K
Ø Besuchsdauer
0:14
Seiten pro Besuch
1.66
Absprungrate
41.22%
Data updated 2026-06-11

Monthly traffic trend

  • 2026/1: 7.2K Monatliche Besuche
  • 2026/2: 5.3K Monatliche Besuche
  • 2026/3: 6.5K Monatliche Besuche
  • 2026/4: 6.3K Monatliche Besuche
  • 2026/5: 6.6K Monatliche Besuche

Top-Regionen

Top 5 countries/regions
Country/regionPercentageTraffic
🇺🇸United States62.41%4.1K
🇮🇳India25.98%1.7K
🇨🇦Canada7.38%484
🇻🇳Vietnam4.23%277

Suchbegriffe

in context learningin-context learningludwiludwigludwig guru

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

Usage comparison

Compare the core capabilities of Ludwig and ModelScope

Ludwig Core features

Low-Code No-Code
Modelltraining
Maschinelles Lernen

ModelScope Core features

Low-Code No-Code
Modell-Hub
Forschung

Use cases

Ludwig Use cases

Feinabstimmung
Open Source
AutoML
Datenwissenschaft
Deklaratives ML
Deep Learning
Rahmen
Großes Sprachmodell
Low-Code
maschinelles Lernen
Multimodal
Python

ModelScope Use cases

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

Ludwig vs ModelScope：In-depth comparison and selection guidance

First decide whether the products solve the same kind of need

This in-depth Ludwig vs ModelScope comparison uses only the product records, taxonomy, audience, traffic, and community signals available on this page. Ludwig is primarily listed under “Modelltraining”, 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 (Ludwig: Modelltraining; ModelScope: Modell-Hub); Pricing (Ludwig: Free; ModelScope: Freemium); Monthly visits (Ludwig: 6.6K; ModelScope: 2.9M); Monthly growth (Ludwig: 3.4%; ModelScope: -26.3%); Favorites (Ludwig: 83; ModelScope: 114). These facts are more useful for selection than brand visibility alone.

What market visibility and monthly traffic mean

In the Ludwig vs ModelScope monthly traffic comparison, Ludwig currently shows 6.6K visits and ModelScope shows 2.9M; ModelScope has about 446.3 times the visible traffic of Ludwig, an absolute difference of about 2.9M 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.

Ludwig is registered at the ludwig.ai/latest subpage; 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.

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

Product positioning, use cases, and roles

Ludwig and ModelScope currently overlap in shared categories: Low-Code No-Code; shared tags: Feinabstimmung und Open Source. This can place both on the same shortlist, but it does not prove equal implementation, depth, or cost.

Ludwig's unique categories/tags are Modelltraining, Maschinelles Lernen, AutoML, Datenwissenschaft, Deklaratives ML, Deep Learning, Rahmen und Großes Sprachmodell; ModelScope's are Modell-Hub, Forschung, KI-Community, KI-Entwicklung, Alibaba, Computer Vision, Große Sprachmodelle und MaaS. 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

Ludwig has no verified rating, 0 comments, 83 favorites, and 87 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 Ludwig first

Put Ludwig on the priority trial list when the task aligns with “Modelltraining” and especially Modelltraining, Maschinelles Lernen, AutoML, Datenwissenschaft, Deklaratives ML und Deep Learning. This follows recorded positioning and does not imply unlisted capabilities are absent.

Ludwig also currently records: pricing is free, product type is website, 6.6K 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 ModelScope first

Put ModelScope on the priority trial list when the task aligns with “Modell-Hub” and especially Modell-Hub, Forschung, KI-Community, KI-Entwicklung, Alibaba und Computer Vision. 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 Ludwig 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 Ludwig 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.