ActiveTigger vs Label Sleuth

ActiveTigger9.0Visit Label Sleuth6.8Visit

Higher score ActiveTigger #1 in Active Learning Tools 9.0/10 Open source Visit ActiveTigger

vs

Label Sleuth #7 in Active Learning Tools 6.8/10 Free plan Free plan Visit Label Sleuth

• ActiveTigger fits best

  Collaborative corpus annotation and text modeling

• Label Sleuth fits best

  Free, self-hosted active text classification

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Side by side

Feature	ActiveTigger 9.0/10 Visit ↗	Label Sleuth 6.8/10 Visit ↗
At a glance
Editor score	9.0	6.8
Ranking	#1 in Active Learning Tools	#7 in Active Learning Tools
Best for	Collaborative corpus annotation and text modeling	Free, self-hosted active text classification
Pricing model	Free	Free
Starting price	Not published	Not published
Free plan	Not published	✓ (best)
Free trial	—	—
Deployment	Cloud, Self-hosted	Self-hosted
Platforms	Web	Web, Windows, macOS, Linux
Support	Email, Community, Docs	Community, Docs
Built for	Solo, Small business, Mid-market	Solo, Small business, Mid-market
Specs
Query strategies	Random; Fixed; Max prob LABEL; Active; Active LABEL	RANDOM; HARD_MINING (uncertainty sampling/least confidence); RETROSPECTIVE (top model-scored instances)
Human annotation workflow	Built_in	Built_in
Supported data types	text; images	text
Model frameworks	scikit-learn; Hugging Face; BERT	scikit-learn; PyTorch; Hugging Face Transformers
Deployment options	Both	Self_hosted
Our review
Pros	Shared codebooks support annotation across collaborators. Active learning helps prioritize examples for annotation. Combines scikit-learn, BERT training, topic models, and export.	Recommends examples through random, uncertainty-based, and retrospective strategies Trains classifiers in the background and shows predictions for review Exports the latest model with a Python usage snippet
Cons	Its primary audience is computational social scientists. The hosted CREST instance requires an account request. Self-hosting requires deployment on an organisation’s infrastructure.	Supports text data only, not other data types Requires local self-hosted installation and operation Maintenance status is unclear; support is through docs and community
Our verdict	ActiveTigger is an open-source web application for collaborative corpus annotation and classification, aimed primarily at computational social scientists. Teams upload tabular datasets, define shared codebooks, and annotate text together,… Read the review →	Label Sleuth is an open-source tool for annotating text and building text classifiers, aimed at domain experts such as physicians, lawyers, and researchers. Its browser interface brings labeling, model predictions, and active-learning… Read the review →

Strengths and trade-offs

• ActiveTigger — where it wins

  • Shared codebooks support annotation across collaborators.
  • Active learning helps prioritize examples for annotation.
  • Combines scikit-learn, BERT training, topic models, and export.

  Where it doesn't

  • Its primary audience is computational social scientists.
  • The hosted CREST instance requires an account request.
  • Self-hosting requires deployment on an organisation’s infrastructure.

• Label Sleuth — where it wins

  • Recommends examples through random, uncertainty-based, and retrospective strategies
  • Trains classifiers in the background and shows predictions for review
  • Exports the latest model with a Python usage snippet

  Where it doesn't

  • Supports text data only, not other data types
  • Requires local self-hosted installation and operation
  • Maintenance status is unclear; support is through docs and community

• ActiveTigger9.0/10 · Open source

  A broad annotation and modeling workflow for teams working with text corpora.

  Visit ActiveTiggerFull verdict →
• Label Sleuth6.8/10 · Free plan

  A self-hosted browser workflow for labeling text and iteratively training classifiers.

  Visit Label SleuthFull verdict →