Prodigy vs Encord

Prodigy7.7Visit Encord7.1Visit

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

vs

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

• Prodigy fits best

  NLP teams wanting polished, model-assisted workflows

• Encord fits best

  Teams needing review-heavy multimodal labeling

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Side by side

Feature	Prodigy 7.7/10 Visit ↗	Encord 7.1/10 Visit ↗
At a glance
Editor score	7.7	7.1
Ranking	#3 in Active Learning Tools	#5 in Active Learning Tools
Best for	NLP teams wanting polished, model-assisted workflows	Teams needing review-heavy multimodal labeling
Pricing model	Paid	Paid
Starting price	Not published	Not published
Free plan	—	—
Free trial	—	—
Deployment	Cloud, Self-hosted	Cloud, Self-hosted
Platforms	Web, Windows, macOS, Linux	Web
Support	Email, Community, Docs	Email, Docs
Integrations	6 integrations	9 integrations
Built for	Solo, Small business, Mid-market, Enterprise	Small business, Mid-market, Enterprise
Specs
Query strategies	Not published	Not published
Human annotation workflow	Not published	Not published
Supported data types	Not published	Not published
Model frameworks	Not published	Not published
Deployment options	Both	Not published
Our review
Pros	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	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	No free plan; Prodigy is a paid product Python recipes and local deployment favor technical teams Company seats are sold in packs of five	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	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 →	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

• 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

• 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

• Prodigy7.7/10 · Paid

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

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

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

  Visit EncordFull verdict →