Roboflow Analysis: $2M Raised
What is Roboflow?
Roboflow builds computer vision software for developers and enterprises that need to train, label, deploy, and monitor visual models. Its platform includes hosted training, AI-assisted annotation, workflow building, and deployment through Roboflow Inference. The company says more than one million engineers and over half of the Fortune 100 build with Roboflow.
Location
123 Spear Street, San Francisco, California 94105, United States
Employees
51-200
Founded
2019
Revenue
$3.8-10.5M
Business Model
Subscription-based revenue model with free and paid plans for users.
Product Features & Capabilities
- Universe, open source computer vision datasets and pre-trained models, Annotate, AI-assisted data annotation for fast labeling, Train, hosted model training infrastructure and GPU access, Workflows, low-code interface to build pipelines and applications, Deploy, run models on device, at the edge, in your VPC, or via API.
Pricing
- Public: Free plan for open source; no credit card needed.
- Basic: $49/month (billed annually) or $65/month (billed monthly); private data and models, 30 credits/month.
- Growth: $299/month (billed annually) or $399/month (billed monthly); private data and models, 150 credits/month, 20 user seats.
- Enterprise: Custom pricing; private data and models, custom credits, and features.
How much Roboflow raised
Seed - $2.1 million
January 12, 2021Lead Investor: Craft Ventures, Lachy Groom, Y Combinator, Next Level Ventures
Who are the founders of Roboflow
JN
Joseph Nelson
Founder
BD
Brad Dwyer
Founder
Market Focus
Roboflow primarily focuses on the computer vision industry, providing tools and platforms for building and deploying computer vision applications.
Investors
- Amjad Masad
- Guillermo Rauch
- GV (formerly Google Ventures)
- Craft Ventures
- Y Combinator
- Valor
- Frontline
- Jeff Dean
Competitors Overview
Roboflow operates in the computer vision market, and its main competitors include:
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Google Cloud Vision API: Offers powerful image analysis capabilities and integrates well with other Google Cloud services. It is known for its scalability and extensive documentation.
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Amazon Rekognition: Provides image and video analysis services, including facial recognition and object detection. Its advantage lies in its integration with AWS services, making it suitable for large-scale applications.
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Microsoft Azure Custom Vision: Allows users to build custom image classification models. Its strength is in its user-friendly interface and integration with the Azure ecosystem.
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IBM Watson Visual Recognition: Although it has been deprecated, it was known for its robust machine learning capabilities and enterprise-level support.
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Clarifai: Focuses on providing a comprehensive suite of tools for image and video recognition. It offers a user-friendly platform and strong customization options.
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V7: A platform that combines data annotation and model training, emphasizing ease of use and collaboration features.
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Dataloop: Offers a data management platform for computer vision projects, focusing on collaboration and workflow optimization.
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Hasty: A tool for data annotation that emphasizes speed and efficiency, making it suitable for teams needing quick turnaround times.
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TensorFlow: While primarily a machine learning framework, it is widely used for building custom computer vision models, offering flexibility and a large community.
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Encord: Focuses on providing tools for data management and model training, with an emphasis on compliance and quality assurance.
Notable differences include the level of integration with cloud services, user interface design, and specific features tailored to different use cases, such as real-time analysis or batch processing.
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