Hasty

A computer vision platform that uses active learning to automate the labeling process, making it a strong choice for teams that need to scale dataset production without linear increases in manual labor.

Excellent for teams moving from manual annotation to model-assisted workflows, weaker for those requiring a purely open-source or offline-only toolset.

Computer Vision Machine Learning Datasets Automation Model Deployment Ai Development Data Analysis

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

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Who Should Use Hasty?

Typical users

Computer vision engineers and data scientists in mid-sized startups or enterprises who are bottlenecked by manual data labeling.

Maturity fit

scaling to advanced

Choose this if…

You want the labeling tool to learn from your manual inputs and start suggesting annotations in real-time
Your priority is reducing the time-to-market for vision models by automating the data-centric loop
You need integrated model training and evaluation within the same environment as your labeling

Skip this if…

You require a strictly open-source tool with no cloud dependency
Your project involves non-vision data types like NLP or audio
You have a very small dataset where the overhead of setting up active learning outweighs manual labeling speed

About Hasty

Hasty is a data-centric vision platform acquired by CloudFactory that focuses on the entire lifecycle of vision AI. It aims to solve the 'data bottleneck' by using machine learning to assist in the annotation process itself. The platform is built for teams that view data quality as the primary driver of model performance.

What it actually does

Hasty provides an environment where users can annotate images using AI-assisted tools that learn from every click. Once enough data is labeled, the platform trains a model in the background to automate the remaining annotations, which users then simply verify or correct.

What makes it different

Unlike traditional labeling tools that treat annotation and training as separate steps, Hasty integrates them into a feedback loop. Its 'Automated Assistants' (like DEXTR and GrabCut) and active learning features mean the tool gets faster the more you use it, specifically for your unique data distribution.

AI-assisted manual annotation Automated object detection and instance segmentation Active learning for dataset curation No-code model training and hyperparameter tuning Model-to-label feedback loops Quality control and consensus workflows API and SDK for custom pipeline integration

Ratings across the web

Capterra 0 reviews

0.0/5

Ratings aggregated from independent review platforms.

Key Features

Automated Assistants

Uses pre-trained models to turn rough clicks into precise masks or boxes.

Active Learning

Identifies which images in your unlabeled pool will most improve model performance if labeled.

Model Playground

Allows users to train and test models on their datasets without leaving the platform.

AI Consensus

Automatically flags potential labeling errors by comparing human annotations against model predictions.

Attribute Labeling

Supports complex classification and metadata tagging alongside spatial annotations.

DEXTR (Deep Extreme Cut)

An assistant that creates complex polygons from just four extreme points on an object.

Instant Feedback Loop

Background models update as you label, providing immediate automation for the next batch.

Pricing

Community

Free

Limited credits for AI features
Basic annotation tools
Public projects only (usually)
Community support

Popular

Enterprise / CloudFactory Accelerate

Custom monthly/yearly

Unlimited AI-assisted labeling
Full active learning suite
Priority support
Managed labeling services integration
Custom model training

Pricing checked 4 months ago

Pricing guidance

Best plan for most users: The Enterprise/Custom tier is the only viable path for teams running production-grade computer vision pipelines.

Free plan enough? No — the free tier is strictly for hobbyists or testing the UI; the credit limits on AI assistants make it impractical for large datasets.

Upgrade when:

When you need to label more than 1,000 images per month
When you need to keep your data private
When you want to use the automated background training features

Watch out for:

Credit-based system for AI assistants can be hard to predict
Storage limits on high-resolution video frames
Export formats may require custom scripting for niche frameworks

Premium enterprise positioning, especially after being folded into CloudFactory’s managed service ecosystem.

Pros & Cons

Strengths

Significant reduction in manual effort
The model-assisted labeling can reduce annotation time by up to 70% for complex tasks like instance segmentation compared to manual polygon drawing.
Integrated data-centric workflow
Having labeling, training, and error detection in one place prevents the 'data silos' that occur when switching between CVAT, custom scripts, and PyTorch.
High-quality polygon generation
The DEXTR and GrabCut implementations are particularly effective at handling irregular shapes that are tedious to draw manually.

Weaknesses

Cloud-first architecture
While it offers high performance, teams with strict data residency requirements or air-gapped environments may find the cloud dependency a hurdle.

Affects: Defense, medical, and high-security enterprise teams
Learning curve for active learning
Setting up the automated feedback loop correctly requires an understanding of how active learning works to avoid introducing model bias into the dataset.

Affects: Beginner data scientists or non-technical annotators
Pricing transparency
Since the CloudFactory acquisition, public self-service pricing has become less clear, often requiring a sales conversation for production-level use.

Affects: Small teams or solo developers on a tight budget

Real User Sentiment

Users generally praise the technical sophistication of the AI assistants but express some frustration with the shift toward enterprise-only sales.

Users tend to like

The DEXTR tool for fast segmentation
The 'AI that learns as you go' philosophy
Clean, responsive web interface
Ability to find labeling errors automatically

Users commonly complain about

Lack of transparent pricing for small teams
Occasional bugs in the polygon editor
Limited support for 3D/Lidar data

Recurring tradeoffs

You trade tool simplicity for automation power; it takes longer to set up than a basic tool like LabelImg but pays off on large sets.

Happiest users

Vision engineers at mid-sized companies who have outgrown manual tools and want a more 'intelligent' labeling environment.

Often frustrated

Individual researchers or students who want a simple, free, offline tool for a one-off project.

Use Cases

Agricultural Tech

Segmenting crops and weeds in thousands of field images using DEXTR.

Manufacturing

Training defect detection models where the AI learns to spot anomalies as you label the first 500 examples.

Medical Imaging

Annotating cell structures or X-rays where precision is critical and AI assistance reduces fatigue.

Autonomous Vehicles

Labeling street scenes where active learning helps prioritize rare edge cases like construction zones.

Retail Analytics

Tracking shelf stock and customer movement by automating bounding box creation for thousands of SKUs.

Frequently Asked Questions

How much does Hasty cost?

Hasty no longer publishes a standard monthly price list. Since its acquisition by CloudFactory, pricing is typically custom and based on your data volume and whether you are also using CloudFactory's workforce. You must contact their sales team for a quote.

How does Hasty compare to Roboflow?

Roboflow is more focused on the end-to-end developer experience and ease of use for beginners, while Hasty is more focused on the 'data-centric' automation of the labeling process itself. Hasty's segmentation tools are often considered more sophisticated for complex shapes.

Can I use Hasty offline?

No, Hasty is a cloud-based platform. While they have explored on-premise options for enterprise clients, the standard version requires an internet connection to access their AI-assisted labeling servers.

Does Hasty support video labeling?

Yes, Hasty supports video by breaking it down into frames. It includes features for object tracking across frames to speed up the annotation of moving objects.

What is 'Active Learning' in Hasty?

Active Learning is a feature where Hasty's models analyze your unlabeled data and suggest which images you should label next to most effectively improve the model's accuracy, preventing you from wasting time labeling redundant data.

Can I export my data to use in PyTorch or TensorFlow?

Yes, Hasty supports exports in common formats like COCO, Pascal VOC, and CSV, making it compatible with all major machine learning frameworks.

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

Acquired

Total Raised

$3.7M

Latest Round

Seed (Nov 2020)

Notable Investors

Shasta Ventures coparion iRobot Ventures

Hasty raised a single $3.7M seed round in late 2020 before being acquired by CloudFactory in September 2022. Its stability and longevity are now entirely dependent on the strategy and financial health of its parent company, CloudFactory.

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: 2,947
Global rank: #5,790,193
Snapshot: Apr 2026
Traffic trend: Falling