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Label Studio
Training Data · 261K monthly visits

Label Studio is a versatile open-source data labeling platform designed for a wide range of data types. It enables users to annotate images, text, audio, video, and time-series data to fine-tune LLMs, prepare training data for machine learning, and validate AI models with human-in-the-loop feedback.

VS
ModelScope
Model Hub · 2.9M monthly visits

ModelScope is an open-source AI model community and platform, offering a vast library of models and datasets. It provides a "Model-as-a-Service" (MaaS) ecosystem with tools for easy model training, inference, and application development, supported by free computing resources.

Label Studio vs ModelScope: pricing, features, traffic, and use cases

Compare Label Studio and ModelScope across positioning, pricing, traffic, and user feedback using structured factual data.

Updated Aug 5, 2026

Product overview

Label Studio Product overview

Label Studio is a versatile open-source data labeling platform designed for a wide range of data types. It enables users to annotate images, text, audio, video, and time-series data to fine-tune LLMs, prepare training data for machine learning, and validate AI models with human-in-the-loop feedback.

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ModelScope Product overview

ModelScope is an open-source AI model community and platform, offering a vast library of models and datasets. It provides a "Model-as-a-Service" (MaaS) ecosystem with tools for easy model training, inference, and application development, supported by free computing resources.

Preview

Detailed feature comparison

FeatureLabel StudioModelScope
Primary categoryTraining DataModel Hub
Added2025-08-132025-08-03
PricingFreemiumFreemium
Official websitelabelstud.iomodelscope.cn
Product typeWebsiteWebsite
Performance data
User ratingNot verifiedNot verified
Comments00
Monthly visits261K2.9M
Monthly growth9%-26.3%
Favorites137114
DetailsView detailsView details

Label Studio vs ModelScope monthly traffic

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

How to interpret the traffic data

In the Label Studio vs ModelScope monthly traffic comparison, Label Studio currently shows 261K visits and ModelScope shows 2.9M; ModelScope has about 11.2 times the visible traffic of Label Studio, an absolute difference of about 2.7M 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.

Label Studio monthly traffic:

Latest traffic

Monthly visits
261K
Avg. visit duration
1:15
Pages per visit
1.98
Bounce rate
45.34%
Data updated 2026-06-11

Monthly traffic trend

  • 2025/9: 217.4K Monthly visits
  • 2026/1: 229.7K Monthly visits
  • 2026/2: 192.9K Monthly visits
  • 2026/3: 220.2K Monthly visits
  • 2026/4: 239.5K Monthly visits
  • 2026/5: 261K Monthly visits

Top regions

Top 5 countries/regions
Country/regionPercentageTraffic
🇩🇪Germany42.84%111.8K
🇺🇸United States16.26%42.4K
🇨🇳China14.08%36.7K
🇮🇳India13.59%35.5K
🇻🇳Vietnam13.23%34.5K

Traffic sources

Source typePercentageTraffic
Direct80.15%209.2K
Referral18.19%47.5K
Email1.66%4.3K

Search keywords

image labelleinglabel studiolabel-studiolabelstudiolabel studio interface

ModelScope monthly traffic:

Latest traffic

Monthly visits
2.9M
Avg. visit duration
4:55
Pages per visit
6.81
Bounce rate
35.01%
Data updated 2026-06-15

Monthly traffic trend

  • 2026/1: 2.8M Monthly visits
  • 2026/2: 2.6M Monthly visits
  • 2026/3: 3.5M Monthly visits
  • 2026/4: 4M Monthly visits
  • 2026/5: 2.9M Monthly visits

Top regions

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 sources

Source typePercentageTraffic
Direct83.92%2.5M
Referral16%468.1K
Email0.08%2.3K

Search keywords

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 Label Studio and ModelScope

Label Studio Core features

Training Data
Data Labeling
Data Management

ModelScope Core features

Model Hub
Research
Low Code No Code

Use cases

Label Studio Use cases

computer vision
fine-tuning
NLP
open source
AI training
annotation tool
data annotation
data labeling
llm
machine learning
RLHF

ModelScope Use cases

computer vision
fine-tuning
NLP
open source
AI community
AI development
alibaba
large language models
MaaS
model library

Label Studio vs ModelScope:In-depth comparison and selection guidance

First decide whether the products solve the same kind of need

This in-depth Label Studio vs ModelScope comparison uses only the product records, taxonomy, audience, traffic, and community signals available on this page. Label Studio is primarily listed under “Training Data”, while ModelScope is primarily listed under “Model 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 (Label Studio: Training Data; ModelScope: Model Hub); Monthly visits (Label Studio: 261K; ModelScope: 2.9M); Monthly growth (Label Studio: 9%; ModelScope: -26.3%); Favorites (Label Studio: 137; ModelScope: 114); Website (Label Studio: labelstud.io; ModelScope: modelscope.cn). These facts are more useful for selection than brand visibility alone.

What market visibility and monthly traffic mean

In the Label Studio vs ModelScope monthly traffic comparison, Label Studio currently shows 261K visits and ModelScope shows 2.9M; ModelScope has about 11.2 times the visible traffic of Label Studio, an absolute difference of about 2.7M 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.

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

Label Studio and ModelScope currently overlap in shared tags: computer vision, fine-tuning, NLP, and open source. This can place both on the same shortlist, but it does not prove equal implementation, depth, or cost.

Label Studio's unique categories/tags are Training Data, Data Labeling, Data Management, AI training, annotation tool, data annotation, data labeling, and llm; ModelScope's are Model Hub, Research, Low Code No Code, AI community, AI development, alibaba, large language models, and 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

Label Studio has no verified rating, 0 comments, 137 favorites, and 143 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 Label Studio first

Put Label Studio on the priority trial list when the task aligns with “Training Data” and especially Training Data, Data Labeling, Data Management, AI training, annotation tool, and data annotation. This follows recorded positioning and does not imply unlisted capabilities are absent.

Label Studio also currently records: pricing is freemium, product type is website, 261K 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 “Model Hub” and especially Model Hub, Research, Low Code No Code, AI community, AI development, and alibaba. 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 Label Studio 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.

Comparison FAQ

How should I choose between Label Studio 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.