Pricing PLANS

Pricing that works with you as you scale

Power-up your ML team with the fastest growing training data platform.

Yearly
Monthly
Education
$0
forever
Monthly usage
1,200 Annotation Hours
OR
120,000 Automated Annotations
50,000 files
Open Source Data
Free, forever:
Image & Video Annotation
AutoML Model Training
Unlimited Workers
Workflow Orchestration
Apply
V7 for Startups
Zero to Launch
| 1-10 employees
Monthly usage
2,500 Annotation Hours
OR
250,000 Automated Annotations
250,000 files
Private data
Everything in Education, plus:
60+ Second & 4K Video
Ultra-High Resolution Images
Email Technical Support
Free Trial
Business
Scaling Usage
Monthly usage
10,000 Annotation Hours
OR
1,000,000 Automated Annotations
3 Million files
Private data
Everything in Startup, plus:
Native Medical Imaging Support (DICOM + NIfTI)
Advanced Workflow Orchestration
Private AWS, GCP, Azure Integrations
Daily Data Backups
Dedicated Customer Success
Integration Engineering Support
Contact us
Pro
High Volume
Monthly usage
42,000+ Annotation Hours
OR
4.2M+ Automated Annotations
5 Million+ files
Private data
Everything in Team, plus:
SSO
Enforced 2FA
Security Reports
Contact us
Billing cycle
Yearly
Monthly
Education
$0
forever
Apply
V7 for Startups
Zero to AI
/year
Companies <10 Employees
Request a Demo
Business
Automated Pipelines
Request a Demo
Pro
Production Scale
Request a Demo
Data Ownership
Open source
Private data
Private data
Private data
Features
Image & Video Annotation
Unlimited Labelers
AutoML Model Training
Workflow Orchestration
Email Technical Support
60+ Second & 4K Video
Ultra-High Resolution Images
Integrate any Model
Native Medical Imaging Support (DICOM + NIfTI)
Private AWS, GCP, Azure Integrations
Daily Data Backups
Advanced Workflow Orchestration
Dedicated Customer Success Manager
Integration Engineering Support
Private Data Center Storage
SSO
Enforced 2FA
Security Reports
Data Managed
Files Managed
50,000 files
250,000 files
3 Million files
5M+ files
V7 was chosen as a data engine by
Tractable
GE Healthcare
MIT
Honeywell
Cancer Research UK
Fujfilm
Simens Healthineers
Tractable
GE Healthcare
MIT
Honeywell
REVIEWS

Hear it from our customers

Learn how world-class ML teams build AI products with V7

What I appreciate the most about V7 is flexible UI and API, responsive support, and active development of new features and bug fixes.

With V7 workflows, it’s very easy to see the project's status - I know how far along the labelers are. I know what’s in review and what’s completed. We can see all status changes happening in real-time. That is probably my favorite V7 feature.

We needed a tool that lets us keep the data in one place, annotate, and version it. Having found V7, we decided not to build the internal solution.

V7 is super sleek, intuitive, and easy to use. Within a couple of minutes, you're off to the races and can annotate quickly. The team is highly responsive and helpful.

Visibility on metrics and annotators' work in V7 is very helpful to us, and it's something we didn’t have in our internal solution. The option to check past annotations and review the work is also valuable, along with V7’s ability to interactively define the workflows and the flexibility in task assignment.

Thanks to V7, the image annotation is 30% faster, but realistically, considering the whole process - transferring files and QA - we more than doubled the number of images we can do in the same span of time.

V7 did everything that we wanted—it enabled us to label videos in the way we needed, the turnaround time for new features was really fast, and the reviewing and sorting process was much better.

V7 is helping us manage a complicated, intricate, pixel-perfect labeling exercise. Their model-assisted labeling is the best around.

I like the auto-segmentation feature. To me, that’s a nice AI feature that V7 took beyond the gimmick feature - it’s mature enough to be useful.

We were looking for an annotation tool that would be much faster, and V7 sped up our labeling 9–10x compared to VGG. The appeal of using V7 is that it’s commercial off-the-shelf, very intuitive, and easy to use for non-technical people involved in our project.

Managing our data from one place is particularly important for us. Previously, our data was stored in many different formats and in different places. Having a single source makes our data more robust and also greatly reduces the development time for new algorithms, as the learning curve for developers is small.

We needed a tool that could do annotating and data versioning because we distribute our tools to farms, and we need to make sure that they have the same version of data for the same models. V7 met our needs.

We use V7 to make our workflow for deep learning training and annotation streamlined and efficient. From the pathologist’s point of view, V7 turned out to be much easier to learn and use than other software - I can easily understand what I’m doing.

Having accurately annotated datasets was crucial to catch the typical features of malignant melanoma. V7 lets us visualize the balance of this data across populations.

Platform comparison

Compare V7 with regular labeling tools

Save months on development

Build VS Buy

How switching to a dedicated platform can double your ML team's output

Talk to Sales
Average internally built solution
$12,000+ in cloud fees
$300k+ in development costs
$120k in maintenance
No dedicated support team
No MLOps integrations
No integrated outsourcing system
Critical dependency on 2-3 employees who built the tool’s codebase
Using V7
99.9% Uptime
5 new features per month
20min average for tech support
50+ upcoming features on our roadmap
Labelers available in a few clicks
Customer Success for Labeling
Built in Elixir for ultra-scalable performance
VS
Start a Free Trial
Gain control of your training data
15,000+ ML engineers can’t be wrong