V7

4.3 (9 reviews)

V7 is a high-performance data engine for computer vision that prioritizes automated labeling and MLOps orchestration, making it a top-tier choice for teams handling complex segmentation or medical imaging.

Best fit for scaling ML teams in healthcare and manufacturing who need to automate pixel-perfect labeling, but likely overkill for basic classification or low-budget projects.

Computer Vision Data Analysis Datasets Machine Learning Automation Healthcare Manufacturing Mlops

Analysis based on product data, pricing structure, traffic signals, and public user sentiment.

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Who Should Use V7?

Typical users

ML engineers, data operations managers, and computer vision researchers in specialized industries like Life Sciences or Autonomous Systems.

Maturity fit

scaling to advanced

Choose this if…

Your priority is reducing manual labeling time through AI-assisted segmentation.
You work with complex data formats like DICOM (medical) or high-resolution video.
You need a centralized platform to manage both data labeling and model training workflows.
Your project requires strict data versioning and detailed audit trails for regulatory compliance.

Skip this if…

You only need simple image classification or bounding boxes that cheaper tools can handle.
Your budget is minimal; open-source alternatives like CVAT or Label Studio are more cost-effective for small teams.
You require a tool with a massive, built-in marketplace of cheap manual annotators (Scale AI is better for pure outsourcing).

About V7

V7 is a training data platform designed to automate the computer vision lifecycle. It combines image and video annotation, dataset management, and model training into a single interface to accelerate the path from raw data to production AI.

Official profiles

X (Twitter) LinkedIn YouTube GitHub

What it actually does

The platform allows users to upload massive visual datasets, use AI-powered tools to automatically outline objects (segmentation), and manage the review process through custom workflows. It also provides infrastructure to train and deploy models directly on the annotated data.

What makes it different

V7 stands out through its 'Auto-Annotate' feature, which is significantly more precise at complex polygon segmentation than most competitors. Unlike general-purpose labeling tools, it offers deep native support for medical imaging (DICOM) and frame-accurate video interpolation.

AI-assisted polygon and mask segmentation Native DICOM and medical imaging support Frame-by-frame video annotation with interpolation Dataset versioning and lineage tracking Customizable multi-stage review workflows Model training and hosted inference (V7 Go) API and SDK for programmatic data management Role-based access control and audit logs

Ratings across the web

4.3 (9 reviews)

Capterra 9 reviews

4.3/5

Ratings aggregated from independent review platforms.

Key Features

Auto-Annotate 2.0

Uses a pre-trained model to snap to object edges, reducing manual clicks by up to 90%.

Workflow Builder

A visual interface to create multi-step pipelines including labeling, QA, and model-in-the-loop stages.

Video Interpolation

Automatically tracks objects across frames, eliminating the need to label every single frame in a sequence.

DICOM Web Viewer

Specialized tools for radiologists to annotate 3D medical volumes directly in the browser.

Dataset Management

Handles millions of images with metadata filtering and version control for reproducible ML.

V7 Go

An integrated environment for building and deploying generative AI and LLM-powered vision workflows.

Model Management

Allows teams to plug in their own models to assist in the labeling process via API.

Pricing

Education & Open Source

Free

For non-commercial research
Basic annotation tools
Public datasets only
Community support

Popular

Business

Custom monthly/yearly

Private datasets
Auto-Annotate 2.0
Full Workflow Builder
API & SDK access

Enterprise

Custom yearly

SSO & Advanced Security
HIPAA compliance
Dedicated success manager
On-premise deployment options

Pricing checked 5 months ago

Pricing guidance

Best plan for most users: The Business plan is the standard for most commercial AI teams, providing the necessary automation tools to justify the cost.

Free plan enough? No — the free plan is strictly for academic or open-source work and cannot be used for commercial product development.

Upgrade when:

When you need to handle private/proprietary data.
When you require HIPAA or SOC2 compliance for medical/sensitive data.
When you need to automate labeling via the API.

Watch out for:

Usage-based credits may apply to AI-assisted features.
Storage limits can apply depending on the negotiated contract.
Seat-based pricing can scale quickly as you add manual annotators.

Premium enterprise positioning with a focus on ROI through automation rather than low-cost seats.

Pros & Cons

Strengths

Superior auto-segmentation
The AI-assisted labeling is highly intuitive and handles complex shapes better than Labelbox or Superb AI, significantly lowering the cost per label.
Deep medical industry focus
Native support for medical formats and specialized viewing tools makes it the default choice for healthcare AI startups and research labs.
Robust workflow customization
The ability to define complex 'if-this-then-that' labeling pipelines ensures high data quality for enterprise-grade models.
Clean, performant UI
The interface remains responsive even when handling large datasets or high-resolution video, which is a common pain point in browser-based tools.

Weaknesses

High cost of entry
Pricing is geared toward enterprises; small teams may find the per-seat or usage-based costs prohibitive compared to self-hosted options.

Affects: Early-stage startups and solo developers
Steep learning curve
Setting up advanced workflows and integrating the API requires significant technical overhead compared to simpler 'point-and-click' tools.

Affects: Non-technical project managers
Opaque pricing
The lack of public pricing for commercial tiers makes it difficult for teams to budget without going through a sales cycle.

Affects: Procurement and small teams

Real User Sentiment

Users generally view V7 as the 'Pro' tool in the space, praising its technical depth while noting its premium price tag.

Users tend to like

The precision of the Auto-Annotate tool
Ease of use for medical imaging (DICOM)
The responsiveness of the support team
The workflow automation capabilities

Users commonly complain about

Lack of transparent pricing for small businesses
Occasional complexity in the API documentation
Cost can become high as dataset size grows

Recurring tradeoffs

You pay more for V7 to save money on manual labor; if your labor is already cheap, the ROI is harder to see.

Happiest users

ML teams in regulated industries who value data quality and automation over raw tool cost.

Often frustrated

Small teams looking for a simple, cheap labeling tool who find the platform's feature set overwhelming and expensive.

Use Cases

Radiology AI

Annotating X-rays and MRIs using specialized DICOM tools for model training.

Manufacturing Quality Control

Detecting microscopic defects in silicon wafers using automated segmentation.

Autonomous Agriculture

Labeling crop types and weeds in high-resolution aerial footage.

Livestock Monitoring

Tracking animal movement and health across video feeds using interpolation.

Retail Analytics

Counting foot traffic and shelf stock levels using automated bounding boxes.

Frequently Asked Questions

How does V7 compare to Labelbox?

V7 is often preferred for complex segmentation and medical imaging due to its superior Auto-Annotate tool and native DICOM support. Labelbox offers a broader ecosystem for different data types (text, audio) and a more mature external workforce marketplace. Choose V7 for technical computer vision and Labelbox for general-purpose multi-modal AI.

Is there a free version of V7?

Yes, V7 offers a free tier specifically for researchers, educators, and open-source projects. However, this tier requires all datasets to be public. For any commercial or private project, you must move to a paid Business or Enterprise plan.

Does V7 support medical imaging like DICOM?

Yes, V7 is one of the few platforms with native, high-performance DICOM support. It includes a specialized web viewer that allows radiologists to annotate 3D volumes, handle windowing/leveling, and manage multi-frame instances without converting files to standard image formats.

Can I host V7 on my own servers?

On-premise or private cloud deployment is available, but typically only for Enterprise-level customers. Most users utilize V7's secure cloud infrastructure, which is HIPAA and SOC2 compliant.

What is V7 Go?

V7 Go is a newer part of the platform designed for Generative AI. It allows users to build workflows that use Large Language Models (LLMs) and Vision Models to process and extract data from images and documents at scale, moving beyond simple labeling into data extraction.

How does the Auto-Annotate feature work?

It uses a pre-trained deep learning model that understands object boundaries. You simply click on an object or draw a rough box around it, and the tool automatically creates a precise polygon mask. It works out-of-the-box without needing to train a model on your specific data first.

Why trust this page?

This evaluation combines product positioning, pricing analysis, traffic and market signals, and public user sentiment into a single decision-support page. Content is generated editorially — not copied from the vendor's website.

Funding & Company

Founded

2018

Stage

Series a

Total Raised

$36M

Latest Round

Series A (Nov 2022)

Notable Investors

Radical Ventures Temasek Amadeus Capital Partners Partech Air Street Capital

V7 has raised a total of $36 million over two rounds, including a significant $33 million Series A in late 2022. This funding, from AI-focused investors like Radical Ventures and Temasek, provides substantial runway for product development and US market expansion.

Full funding report high confidence

Market Signals & Traffic

Estimated visits, global rank, geography, traffic sources, monthly visit trends, and organic search keywords (Similarweb)—on a dedicated page built for depth and search.

Estimated visits: 270,624
Global rank: #105,885
Snapshot: Apr 2026
Traffic trend: Surging