Prodigy

Paid#9 of 24 in Data Labeling SoftwareActive Learning Tools

Prodigy: A flexible active-learning workspace for ML teams, with paid licensing and local deployment. Ranked #9 of 24 in Data Labeling Software by our editors (6.5/10); pricing: Paid; best for NLP and multimodal teams using active learning.

6.5/10★★★☆☆Editor score

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At a glance

• Editor score
  6.5 / 10
• Pricing
  Paid
• Best for
  NLP and multimodal teams using active learning
• Free plan
  No
• Model-assisted labeling
  Yes
• Facts checked
  21 Sep 2026

• Where it wins

  • Supports text, image, audio, and video annotation
  • Model-assisted labeling, active learning, and review workflows
  • Python, REST APIs, custom recipes, and ML integrations

• Where it doesn't

  • No free plan
  • Starting price of 390
  • Company seats are sold in packs of five

Our verdict on Prodigy

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 local or infrastructure-controlled labeling. The browser-based annotation interface supports text, images, audio, and video, while user-managed hardware, cloud infrastructure, and on-premises deployment provide flexibility for teams with specific infrastructure requirements.

Prodigy uses paid lifetime licenses rather than a free plan. The Personal plan includes 12 months of free upgrades, the installer, web application, plugins, basic HTTP authentication, community forum support, and unlimited personal and professional use. The Company plan adds HTTP authentication and SSO with OpenID Connect, priority forum and email support, and transferable seats within the company; company licensing is sold in packs of five. The starting price is 390, making the product a considered purchase for teams that need its workflow and deployment model rather than a lightweight labeling utility.

Its main strength is breadth combined with developer control. Teams can annotate entities, spans, classifications, relations, bounding boxes, polygons, segmentation, transcriptions, and other formats through customizable interfaces and Python recipes. Model-assisted labeling and active-learning workflows can reduce repetitive annotation, while review and conflict-resolution workflows support multiple annotators. REST and Python APIs connect the tool with spaCy, Hugging Face, PyTorch, TensorFlow, Jupyter, and LLM API providers, with exports and storage for JSON, SQLite, MySQL, and PostgreSQL. Prodigy fits teams building ML datasets across modalities; teams seeking a free plan or a simpler hosted service should look elsewhere.

Prodigy pricing

All 2 Prodigy plans and prices →

Prodigy fact sheet

Prodigy integrations

Prodigy lists 6 integrations on its own site.

• spaCy
• Hugging Face
• PyTorch
• TensorFlow
• Jupyter
• LLM API providers

Alternatives to Prodigy

• ArgillaA free, open-source option for teams labeling text and multimodal AI data.9.0
• PotatoA broad self-hosted labeling platform for multimodal datasets and model-assisted workflows.8.2
• CVATCVAT combines broad annotation tools, AI assistance, collaboration, and open-source deployment.7.9

See all Prodigy alternatives →

Also listed in

• Active Learning Tools

Featured on iTechGuides

Prodigy is listed in our Data Labeling Software directory. Add the badge to your site — it links back to this page.

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Reviewed by iTechGuides Editors · Editorial team · Updated Sep 2026

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Last updated 28 Sep 2026 · How we research and update