ActiveTigger vs Potato

ActiveTigger9.0Visit Potato8.2Visit

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

vs

Potato #2 in Active Learning Tools 8.2/10 Free plan Free plan Visit Potato

• ActiveTigger fits best

  Collaborative corpus annotation and text modeling

• Potato fits best

  Teams annotating multiple data types with active learning

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Side by side

Feature	ActiveTigger 9.0/10 Visit ↗	Potato 8.2/10 Visit ↗
At a glance
Editor score	9.0	8.2
Ranking	#1 in Active Learning Tools	#2 in Active Learning Tools
Best for	Collaborative corpus annotation and text modeling	Teams annotating multiple data types with active learning
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	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	Not published
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.	Covers text, image, audio, and video annotation workflows Combines active learning, AI-assisted labeling, and quality controls Offers APIs, CLI tools, and exports in multiple formats
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.	Requires self-managed installation and deployment Configuration uses YAML, which may not suit every workflow Support is through community resources and documentation
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 →	Potato is an open-source data annotation platform developed at the University of Michigan. It is aimed at teams that need to label more than text: the browser-based interface supports text classification and spans, image regions and masks,… 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.

• Potato — where it wins

  • Covers text, image, audio, and video annotation workflows
  • Combines active learning, AI-assisted labeling, and quality controls
  • Offers APIs, CLI tools, and exports in multiple formats

  Where it doesn't

  • Requires self-managed installation and deployment
  • Configuration uses YAML, which may not suit every workflow
  • Support is through community resources and documentation

• ActiveTigger9.0/10 · Open source

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

  Visit ActiveTiggerFull verdict →
• Potato8.2/10 · Free plan

  A self-hosted annotation toolkit for teams combining active learning with varied media.

  Visit PotatoFull verdict →