ActiveTigger vs Encord

ActiveTigger9.0Visit Encord7.1Visit

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

vs

Encord #5 in Active Learning Tools 7.1/10 Pricing on request Visit Encord

• ActiveTigger fits best

  Collaborative corpus annotation and text modeling

• Encord fits best

  Teams needing review-heavy multimodal labeling

Advertiser disclosure: iTechGuides is reader-supported. We may earn a commission when you click some links. How we rank.

Side by side

Feature	ActiveTigger 9.0/10 Visit ↗	Encord 7.1/10 Visit ↗
At a glance
Editor score	9.0	7.1
Ranking	#1 in Active Learning Tools	#5 in Active Learning Tools
Best for	Collaborative corpus annotation and text modeling	Teams needing review-heavy multimodal labeling
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
Support	Email, Community, Docs	Email, Docs
Built for	Solo, Small business, Mid-market	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	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.	Supports annotation across image, video, audio, text, documents, DICOM, and NIfTI Combines AI-assisted labeling with consensus and customizable review workflows Offers APIs, SDKs, cloud integrations, and enterprise deployment options
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 is offered, and published pricing requires contacting sales Single sign-on, SLA support, VPC, and on-premises deployment are Enterprise features The broad feature set may be more than teams labeling a single data type need
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 →	Encord is a multimodal data platform for AI teams that curate, annotate, and evaluate training data. It supports image, video, audio, text, documents, DICOM, NIfTI, LiDAR, and other data types. Teams can define custom ontologies with… 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.

• Encord — where it wins

  • Supports annotation across image, video, audio, text, documents, DICOM, and NIfTI
  • Combines AI-assisted labeling with consensus and customizable review workflows
  • Offers APIs, SDKs, cloud integrations, and enterprise deployment options

  Where it doesn't

  • No free plan is offered, and published pricing requires contacting sales
  • Single sign-on, SLA support, VPC, and on-premises deployment are Enterprise features
  • The broad feature set may be more than teams labeling a single data type need

• ActiveTigger9.0/10 · Open source

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

  Visit ActiveTiggerFull verdict →
• Encord7.1/10 · Pricing on request

  Review-focused annotation with AI assistance, quality controls, APIs, and cloud integrations.

  Visit EncordFull verdict →