ActiveTigger vs Prodigy

ActiveTigger9.0Visit Prodigy7.7Visit

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

vs

Prodigy #3 in Active Learning Tools 7.7/10 Paid Visit Prodigy

• ActiveTigger fits best

  Collaborative corpus annotation and text modeling

• Prodigy fits best

  NLP teams wanting polished, model-assisted workflows

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Side by side

Feature	ActiveTigger 9.0/10 Visit ↗	Prodigy 7.7/10 Visit ↗
At a glance
Editor score	9.0	7.7
Ranking	#1 in Active Learning Tools	#3 in Active Learning Tools
Best for	Collaborative corpus annotation and text modeling	NLP teams wanting polished, model-assisted workflows
Pricing model	Free	Paid
Starting price	Not published	Not published
Free plan	Not published	—
Free trial	—	—
Deployment	Cloud, Self-hosted	Cloud, Self-hosted
Platforms	Web	Web, Windows, macOS, Linux
Support	Email, Community, Docs	Email, Community, Docs
Built for	Solo, Small business, Mid-market	Solo, Small business, Mid-market, Enterprise
Specs
Query strategies	Random; Fixed; Max prob LABEL; Active; Active LABEL	Not published
Human annotation workflow	Built_in	Not published
Supported data types	text; images	Not published
Model frameworks	scikit-learn; Hugging Face; BERT	Not published
Deployment options	Both	Both
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.	Active-learning and model-assisted labeling for iterative data creation Text, image, audio, and video annotation in one tool Python, REST APIs, review workflows, and broad ML integrations
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.	No free plan; Prodigy is a paid product Python recipes and local deployment favor technical teams Company seats are sold in packs of five
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 →	Prodigy is a downloadable annotation tool and Python library for creating training and evaluation data for machine-learning systems. It is aimed at developers, researchers, data-science teams, startups, and enterprises that need… 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.

• Prodigy — where it wins

  • Active-learning and model-assisted labeling for iterative data creation
  • Text, image, audio, and video annotation in one tool
  • Python, REST APIs, review workflows, and broad ML integrations

  Where it doesn't

  • No free plan; Prodigy is a paid product
  • Python recipes and local deployment favor technical teams
  • Company seats are sold in packs of five

• ActiveTigger9.0/10 · Open source

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

  Visit ActiveTiggerFull verdict →
• Prodigy7.7/10 · Paid

  A paid, developer-oriented annotator spanning NLP, vision, audio, and video.

  Visit ProdigyFull verdict →