Replicate, a San Francisco, CA-based startup making it easier for software teams to build artificial intelligence applications, exited stealth mode and announced that it secured $17.8 million in funding.
The company raised the capital over two rounds $12.5 million of the total came from Series A funding. It earlier closed a $5.3 million seed round.
The Series A round was led by Andreessen Horowitz with participation from Y Combinator, Sequoia, and angel investors including Figma CEO Dylan Field and Vercel’s Guillermo Rauch.
“AI is currently too hard to use for software engineers and you have to be a machine learning engineer to use it,” Ben Firshman, Founder & CEO of Replicate said. “Companies and the industry as a whole is being held back by the lack of machine learning experts. We’re making it possible for software engineers to use machine learning with zero experience, with just a few lines of code, so they can build products with AI and apply it to business problems.”
Replicate offers a platform that promises to reduce manual work for software teams. According to the startup, the platform enables developers to deploy AI models with a few lines of code.
Replicate has developed an open-source tool called Cog that eases the task. The tool, which is included in the company’s platform, enables users to configure containers with less customization than the task usually requires.
Cog can generate a Dockerfile, the files that define a container’s configuration, based on a limited number of instructions provided by software teams.
Company: Replicate, Inc.
Round: Seed Round, Series A
Funding Month: February 2023
Lead Investors: Andreessen Horowitz
Additional Investors: Y Combinator, Sequoia, Dylan Field, and Vercel’s Guillermo Rauch
Company Website: https://replicate.com/
Software Category: Machine Learning
About the Company: Replicate is a developer of a python library-based machine learning platform designed to provide a version control tool for machine learning. The company's platform automatically generates an API server for custom machine learning models and tracks the experiments and models with two lines of code including codes, hyperparameters, training data, weights, and metrics, enabling software developers to easily share and run machine learning models.