Leveraging Cloudinary for Efficient Image Processing: A Case Study on Keep.com
Technology Category
- Analytics & Modeling - Computer Vision Software
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
- Apparel
- Retail
Applicable Functions
- Procurement
- Product Research & Development
Use Cases
- Clinical Image Analysis
- Time Sensitive Networking
Services
- System Integration
About The Customer
Keep.com is an innovative online commerce startup that provides users a place to discover and buy the latest trends in fashion, decor, accessories, and design. Unlike traditional online retailers, Keep aggregates products from across the Internet into a showcase of emerging trends. They call this Commerce 4.0, where goods from across the Web are collected and curated by people with a passion for products. Keep’s philosophy is that these passionate consumers will be better merchandisers than traditional retailers, whose merchandising decisions are dictated by business restrictions.
The Challenge
Keep.com, an innovative online commerce startup, was facing a significant challenge in managing image transformations for their website and mobile app. The application displays hundreds of thousands of product and brand images, user profile pictures, and user uploaded images. Each time a redesign took place, Keep needed new image sizes, which required reprocessing of all the images. This process was not only time-consuming from a server perspective but also caused delays in the development process due to long waiting times as all the images were being transformed. Engineering resources were wasted trying to set up new image transformations and getting jobs running efficiently. Despite these efforts, image processing remained a bottleneck, hindering the development of their website and business.
The Solution
Keep.com turned to Cloudinary, a cloud-based image management platform, to solve their image processing challenges. Cloudinary was cost-effective and allowed for fast image processing and automatic caching. It also provided a 'fetch' interface that allowed Keep to grab images directly off Rackspace, making integration very easy. This solution significantly reduced the percentage of resources Keep spent on image transformations. By leveraging Cloudinary, Keep was able to quickly process images on-demand, enabling them to swiftly push a new version of their design to production. The implementation of Cloudinary not only reduced the time required to release new image sizes but also freed up developer resources to work on other projects, further increasing Keep’s velocity of innovation.
Operational Impact
Quantitative Benefit
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