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Head-to-head · Active Learning Tools

Encord vs Active Learning Toolbox

  • Updated Sep 2026
  • Both researched from official sources
  • 1 check side by side
Higher score Encord #5 in Active Learning Tools 7.1/10 Pricing on request Visit Encord
Active Learning Toolbox #8 in Active Learning Tools 6.6/10 Free plan Free plan Visit site

Encord leads on 0 checks, Active Learning Toolbox on 1, and 0 are even. Who comes out ahead on the 1 yes/no, price and count check where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreEncord · 7.1/10
  • Free planonly Active Learning Toolbox

Encord scores higher on our rubric for active learning tools: 7.1 against 6.6 out of 10; our editors rank them #5 and #8.

Active Learning Toolbox offers free plan; Encord doesn't.

Encord is the better fit for teams needing review-heavy multimodal labeling. Active Learning Toolbox is the better fit for jupyter users annotating image and text datasets.

  • Encord fits best

    Teams needing review-heavy multimodal labeling

  • Active Learning Toolbox fits best

    Jupyter users annotating image and text datasets

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Side by side

Feature Encord 7.1/10 Visit ↗ Active Learning Toolbox 6.6/10 Visit ↗
At a glance
Editor score 7.1 6.6
Ranking #5 in Active Learning Tools #8 in Active Learning Tools
Best for Teams needing review-heavy multimodal labeling Jupyter users annotating image and text datasets
Pricing model Paid Free
Starting price Not published Not published
Free plan — ✓ (best)
Free trial — —
Deployment Cloud, Self-hosted Self-hosted
Platforms Web Linux, macOS, Windows
Support Email, Docs Docs
Integrations 9 integrations 2 integrations
Built for Small business, Mid-market, Enterprise Solo, Small business, Mid-market
Specs
Query strategies Not published Not published
Human annotation workflow Not published Built_in
Supported data types Not published image, text
Model frameworks Not published scikit-learn
Deployment options Not published Self_hosted
Our review
Pros
  • 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
  • Combines active-learning query utilities with a Jupyter annotation widget
  • Integrates with libact and supports scikit-learn workflows
  • Includes example annotation workflows for MNIST and 20 Newsgroups
Cons
  • 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
  • Requires a local or self-hosted Python and Jupyter environment
  • Its demonstrated workflows focus on image and text datasets
  • Documentation is the listed support channel
Our verdict

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 →

Active Learning Toolbox is an open-source Python repository for machine-learning practitioners who want to iteratively select examples for expert labeling while building a labeled dataset. Its main components are active-learning query…

Read the review →
  1. EncordActive Learning Tools 7.1Pricing on request
  2. Active Learning ToolboxActive Learning Tools 6.6Free plan

Strengths and trade-offs

  • 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

  • Active Learning Toolbox — where it wins

    • Combines active-learning query utilities with a Jupyter annotation widget
    • Integrates with libact and supports scikit-learn workflows
    • Includes example annotation workflows for MNIST and 20 Newsgroups

    Where it doesn't

    • Requires a local or self-hosted Python and Jupyter environment
    • Its demonstrated workflows focus on image and text datasets
    • Documentation is the listed support channel
  • Encord7.1/10 · Pricing on request

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

    Visit EncordFull verdict →
  • Active Learning Toolbox6.6/10 · Free plan

    A free, self-hosted Python toolbox for annotating image and text datasets in Jupyter.

    Visit siteFull verdict →

More comparisons

All active learning tools comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Sep 2026

Last updated · How we research and update