ActiveTigger vs Dataloop

ActiveTigger9.0Visit Dataloop7.4Visit

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

vs

Dataloop #4 in Active Learning Tools 7.4/10 Pricing on request Visit Dataloop

• ActiveTigger fits best

  Collaborative corpus annotation and text modeling

• Dataloop fits best

  Enterprise teams labeling diverse data at scale

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Side by side

Feature	ActiveTigger 9.0/10 Visit ↗	Dataloop 7.4/10 Visit ↗
At a glance
Editor score	9.0	7.4
Ranking	#1 in Active Learning Tools	#4 in Active Learning Tools
Best for	Collaborative corpus annotation and text modeling	Enterprise teams labeling diverse data at scale
Pricing model	Free	Paid
Starting price	Not published	Not published
Free plan	Not published	Not published
Free trial	—	—
Deployment	Cloud, Self-hosted	Cloud
Platforms	Web	Web
Support	Email, Community, Docs	Email, Tickets, Docs
Built for	Solo, Small business, Mid-market	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	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.	Model-assisted annotation and active-learning workflows Studios cover image, video, audio, text, PDF, LiDAR, and GIS Python and JavaScript SDKs, REST API, and cloud-storage 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.	Pricing requires contacting Dataloop or agreeing plans directly Broad workflow scope may be more than teams need for image-only labeling Cloud deployment only
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 →	Dataloop is a cloud-based AI development platform for organizing unstructured data, building annotation workflows, and integrating models. It is aimed at data engineers, data scientists, developers, AI leaders, and human reviewers, with a… 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.

• Dataloop — where it wins

  • Model-assisted annotation and active-learning workflows
  • Studios cover image, video, audio, text, PDF, LiDAR, and GIS
  • Python and JavaScript SDKs, REST API, and cloud-storage integrations

  Where it doesn't

  • Pricing requires contacting Dataloop or agreeing plans directly
  • Broad workflow scope may be more than teams need for image-only labeling
  • Cloud deployment only

• ActiveTigger9.0/10 · Open source

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

  Visit ActiveTiggerFull verdict →
• Dataloop7.4/10 · Pricing on request

  A collaborative labeling platform with model-assisted workflows, APIs, and cloud integrations.

  Visit DataloopFull verdict →