We’ve got some pretty big news today – is now Lightning AI!

This evolution of our company’s identity echoes our expanding focus on unifying the entire artificial intelligence development lifecycle. We’re also excited to announce a $40M Series B funding round, led by Coatue with participation from Index Ventures.


A Trip Down Memory Lane

After I began developing the open-source deep learning framework PyTorch Lightning as an undergrad at Columbia in 2015, I founded Lightning AI (which was at the time) in 2019 with Luis Capelo. While working on my PhD at NYU and Facebook AI Research, I open-sourced PyTorch Lightning and it quickly gained traction as the tool of choice for AI researchers and ML engineers in industry working on state-of-the-art artificial intelligence projects.

As companies in every industry began putting PyTorch Lightning into production, we realized that the biggest challenge holding back AI adoption at scale was fragmentation of the AI ecosystem. I first noticed the impact of the fragmented AI ecosystem back in 2019. Because AI adoption at scale was and still is so nascent, every few months we would discover a “missing” part of the machine learning stack. Things like feature stores and data versioning, for instance, appeared as key tools in 2019. The following year, in 2020, we discovered the need for experiment managers and the ability to use multiple hardware accelerators without code changes (features that Grid pioneered that same year). 

Why this matters is that every missing piece of the puzzle slows down the overall pace of AI innovation. An incomplete and fragmented stack makes it more time consuming and costly than it needs to be to build AI. Just getting a model to the point where it can be pushed into production takes hundreds if not thousands of developer hours spent purely on infrastructure. Now, “MLOPs” and “training on the cloud” are being positioned as the biggest problems facing AI adoption – problems we already solved in our previous product offerings. The real problem everyone in the AI space should be concerned with though is still the overarching problem of fragmentation.

That’s why after two years of laser focus on solving the issue of fragmentation in machine learning development, we are introducing the Lightning AI platform. With Lightning AI, researchers in academia, machine learning engineers in industry, and everyone in between will be able to bring to life end-to-end ML systems in a matter of days, rather than the years of work that currently requires.

You don’t have to know anything about the internal combustion engine to drive to the grocery store; why should you have to know about Kubernetes, cloud infrastructure, distributed file systems and fault-tolerant training to simply bring your AI project to life? Current solutions hand you the disparate pieces of a working car and hope that you’ll be able to assemble them into something that you can use to take a drive.

The vision I’ve been pursuing since my time at NYU has always been to build something like an operating system for artificial intelligence, that allowed all the disparate pieces of the AI ecosystem to work together. No extant solution like this exists, and it took the collective focused effort of our entire team years to conceptualize and build Lightning AI.

Today, we’re introducing what we see as the first automobile of artificial intelligence. You focus on going for a drive – we’ll take care of the engineering.


Our Commitment: Build AI Faster and Easier

As PyTorch Lightning and Grid grew and began to serve a constantly expanding number of users across the globe, we made a significant effort to both listen to and act on the feedback and needs of our community. Throughout this process, two things became immediately clear:

  1. The deep learning space had been underserved for years by a number of disparate tools that were not interoperable, making the AI development lifecycle expensive and time-consuming.
  2. There was a need for a unified solution that went beyond model training – something that alleviated the engineering burden from researchers, data scientists, academics, and everyone in between.

Lightning AI is our way of meeting these needs. Machine learning initiatives that would have once taken a team of dedicated engineers weeks and months to build can now be completed virtually in a matter of days.


New brand

Since its inception, PyTorch Lightning has been downloaded over 20 million (!) times. Our new brand, Lightning AI, is our way of honoring the community of developers who’ve used and trusted the things we’ve built over the last three years.


You’ll also notice some notches missing from the letter ‘n’ in our logotype. That’s a nod to Grid, the platform we built to train machine learning models on the cloud with just a single line of code.

Pretty snazzy, right?

New funding

Writing this post feels a lot like opening up a never ending supply of birthday presents, only instead of video games and toy cars we get to share really exciting news.

In addition to rebranding and launching a product that our team’s been dreaming about for years, we’re also announcing a $40M Series B funding round, led by Coatue and Index Ventures. This funding will allow us to continue expanding the Lightning AI platform, and to further simplify the AI development lifecycle.


A big thank you to everyone who’s used Grid over the years and become a part of our expert developer community. These kinds of leapfrogs in AI development are only possible because of the incredible work you all dao.