CVAT vs Labellerr: Pricing and Ratings Compared (2026)
Entry prices and free plans as read on each vendor's pricing page, and user ratings as published on review sites, for CVAT and Labellerr side by side. This page compares data; it is not a hands-on review.
On the data: On the data below, Labellerr leads on higher user rating. We do not name an overall winner for this pair.
Short on time? Here's the quick answer
Based on published vendor pricing and features and on public review data, here's who should pick what:
CVAT
The industry-leading open data annotation platform for machine learning.
Best for you if:
- • You want accelerates data annotation up to 10x with AI tools
Labellerr
Accelerate AI model development with high-quality, automated data labeling and annotation for various data types.
Best for you if:
- • You want significantly reduces time and cost for data preparation and AI model development
| At a Glance | CVAT | Labellerr |
|---|---|---|
Starts at | Free plan, paid from $23/mo, billed monthly Solo Yearly plan | Free plan, paid from $833.25/mo, billed yearly Pro Plan plan |
Category | Computer Vision Developer Tools | AI Data Labeling Developer Tools |
Rating | 4.5/5 G2 4.5 54 reviews | 4.8/5 G2 4.8 21 reviews |
Free plan | Yes | Yes |
Watch out for | Enterprise tier starts at $12,000/year | n/a |
Choose CVAT or Labellerr?
Choose CVAT if
- You want accelerates data annotation up to 10x with AI tools
- You want supports a wide range of data types (images, videos, 3D) and annotation tools
- You work in computer vision, not AI data labeling
Choose Labellerr if
- You want significantly reduces time and cost for data preparation and AI model development
- You want achieves high accuracy (99%) in data labeling
- You work in AI data labeling, not computer vision
In-Depth Analysis
CVAT
The industry-leading open data annotation platform for machine learning.
CVAT's pricing is fair, offering a generous Free tier for individuals and a clear progression for teams.
Watch out
Enterprise tier starts at $12,000/year
Strengths
- Pro: Accelerates data annotation up to 10x with AI tools.
- Pro: Supports a wide range of data types (images, videos, 3D) and annotation tools.
- Pro: Offers flexible deployment options including free community version, online plans, and self-hosted enterprise solutions.
- Pro: Provides robust quality control and analytics for managing annotation projects.
- Pro: Integrates with popular cloud storage providers and AI model hubs.
Weaknesses
- Con: Free plan has significant limitations on project, task, and storage capacity.
- Con: Enterprise features like advanced security and customization come at a high annual cost.
- Con: Some advanced features like SSO and audit logs are only available for annual or enterprise plans.
Key features
Labellerr
Accelerate AI model development with high-quality, automated data labeling and annotation for various data types.
The Researcher Plan is very generous, offering substantial features for free, making it highly accessible for individual users.
Strengths
- Pro: Significantly reduces time and cost for data preparation and AI model development.
- Pro: Achieves high accuracy (99%) in data labeling.
- Pro: Supports a broad range of data types and annotation techniques.
- Pro: Offers robust security and flexible hosting options.
- Pro: Provides comprehensive project management and analytics for quality control.
Weaknesses
- Con: Specific details on the extent of customization for custom export formats are not explicitly detailed.
- Con: The free plan has limitations on data credits and seats, suitable mainly for researchers or small projects.
- Con: Advanced features like IP Whitelisting and Whitelabeling are only available in the Enterprise plan.
Key features
Plans and prices: CVAT vs Labellerr
| Tool | Plans and prices | Source |
|---|---|---|
| CVAT |
| cvat.ai/pricing read Oct 30, 2026 |
| Labellerr |
| labellerr.com/pricing read Nov 2, 2026 |
Prices as read on each vendor's own pricing page on the date shown. Plans are listed per tool and are not matched against the other tool's plans: a plan name or price in one column says nothing about the same row in the other. Details on CVAT pricing and Labellerr pricing.
Key Takeaways
CVAT
- Choose if you want the industry-leading open data annotation platform for machine learning
Labellerr
- Higher user rating: 4.8/5 vs 4.5/5
What the data shows
On the data below, Labellerr leads on higher user rating. We do not name an overall winner for this pair.
- Labellerr has the higher user rating (4.5/5 from 54 reviews for CVAT, 4.8/5 from 21 reviews for Labellerr).
- Entry prices are not ranked, they are charged on different bases: CVAT $23/mo, billed monthly; Labellerr $833.25/mo, billed yearly.
- Both have a free plan.
Criteria: user rating (only above 20 reviews on both sides, gap of 0.2 or more), entry price (only on the same billing basis), free plan. How we rate
Frequently Asked Questions
How do CVAT and Labellerr compare in 2026?
On the data below, Labellerr leads on higher user rating. We do not name an overall winner for this pair. Labellerr has the higher user rating (4.5/5 from 54 reviews for CVAT, 4.8/5 from 21 reviews for Labellerr). Entry prices are not ranked, they are charged on different bases: CVAT $23/mo, billed monthly; Labellerr $833.25/mo, billed yearly. Both have a free plan.
What are CVAT and Labellerr used for?
CVAT: The industry-leading open data annotation platform for machine learning. Labellerr: Accelerate AI model development with high-quality, automated data labeling and annotation for various data types.
What does CVAT cost vs Labellerr?
CVAT's cheapest paid plan is Solo Yearly at $23/mo, billed monthly; it also has a free plan. Labellerr's cheapest paid plan is Pro Plan at $833.25/mo, billed yearly; it also has a free plan. Prices read on the vendors' pricing pages, latest check Nov 2, 2026.
What's the main difference between CVAT and Labellerr?
CVAT's main strength: accelerates data annotation up to 10x with AI tools. Labellerr's main strength: significantly reduces time and cost for data preparation and AI model development.