đ Iâm Ivan. I study how top 1% startups grow.
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Hello there!
This week Iâm diving into Fireworks AI, a company running open AI models for other companies (i.e. Cursor, Uber, Notion) that recently raised $1.5B at a $17.5B valuation (4 years after its founding, another AI-wave rocket-ship):
In a nutshell:
Product: think about it like an âengine roomâ for AI. Companies that donât want to depend on OpenAI / Anthropic take a free open model (i.e DeepSeek or Llama) and Fireworks runs it for them (fast, cheap and inside their product). Today it apparently already handles 40T+ tokens a day (tokens are the chunks of words AI reads + writes) for 10K+ companies.
Money: the model is whatâs quickly becoming a classic = pay per use (i.e. $0.30 per million tokens in, $1.20 per million out on a model like DeepSeek), renting chips by the hour (i.e. $8/hr for an Nvidia H100), or pay to train your own custom model. Theyâve already passed $1B in annualized revenue as of July this year with around ~200 people ($5M per employee!).
Driver: loved how they frame it here: âCompanies are no longer renting general intelligence. Theyâre building their own.â
8 growth levers in this drop:
Sold Metaâs internal speed tricks to everyone
Made switching painless at the moment costs typically start to hurt
Had every hot new free model running on launch day
Built custom for the fastest-growing customer + then sold it to everyone
Made engineers part of the âunofficialâ sales team (inside the customerâs Slack)
Made a custom model cost the same as a generic one
Turned big clouds into both suppliers and a sales channel
Bet on deciding which model runs for each task
đ Quick note on editorial + methodology: this analysis focuses on the 80/20 mechanics that explain their growth (itâs not a comprehensive profile, not an endorsement or investment advice). I use AI like a fund leverages an analyst for groundwork, the direction + judgement are mine. Company-reported figures are marked as such, treat directional estimates as directional.
Zero to one
Who: 7 engineers who used to run the AI plumbing at my good old employer Meta + Google (6 from Meta and four of them actually from the PyTorch team aka the free toolkit most of the world uses to build AI models today).
Lin Qiao (CEO): led the PyTorch there growing the team from 5 to ~300 people, before that IBM and LinkedIn. She started Fireworks at age 48 (after shelving a 2015 startup plan to learn how to lead people at Facebook). She meant to stay âone year or twoâ and stayed 7.
Dmytro Dzhulgakov (CTO): from Ukraine whoâs a PyTorch core maintainer after 11 years at Meta, before intern at Google.
Benny Chen: a new Zealand-born engineer who ran ads infra also at Meta.
Plus Dmytro Ivchenko, James Reed, Pawel Garbacki (all ex-Meta) and Chenyu Zhao (ex-Google).
Where they started (fall 2022): they all apparently left Meta about 2 months before ChatGPT came out and the first plan was a platform to help companies use PyTorch. Benny, in 2024 said: âExactly how, we didnât really figure out completely... ChatGPT wasnât out yet, so we had to pivot somewhere in the middle.â They raised $25M from Benchmark and Sequoia before having let alone launching a product. Apparently, fun detail, they started in a building literally called the âSequoia buildingâ before Sequoia invested.
The wedge: PyTorch was of course free and open so companies like Walmart, Disney, Tesla, Netflix etc used it too and they kept coming back to Linâs team asking âcan you build this training platform for us? Can you build this serving platform for us?â Lin took it as a signal the industry was ready / market timing was right, âand then more importantly, we have the key.â
The MVP: a platform that launched in August 2023 (11 months in). Run and customize free models through a simple web connection with a free tier for devs:
First users: âa lot of ex-coworkers from Meta,â says Benny plus teams who trusted the PyTorch name (this is a classic Silicon Valley dense network kindling effect), and by mid-2024 the list already included Cursor, DoorDash and Quora.
Growth Mechanics
Lever 1: Sold Metaâs internal speed tricks to everyone
âOur biggest differentiation is... Fireworks off the shelf is faster than both of the offerings and second is weâre building a system, not just a library.â (Lin, Sequoia)
