AIWatch.

Open-source models are free, sure... but the hard part is tailoring them so they perform best on your own use cases. And making them run fast, cheaply, and reliably in production. This is the mega hard work.

*Fireworks is the layer that does this exact work:* it sits above the chips, but below the model, abstracting away the complexity of deploying and optimising open models.

As a result, they already crossed >$1bn in run-rate revenue, up 5x YoY... and they are accelerating at scale!

20VC invested $10m in the most recent round. Here is why @HarryStebbings and I at @20vcFund were excited to do so:

*First, the team:* the company was founded in 2022 by @lqiao and six co-founders, largely the team that built and ran PyTorch (the framework nearly every AI model on earth is trained on) at Meta. That is truly a unique right to win in that space: if anyone knows how to make models run fast on GPUs, it is the Fireworks team.

*Second, what they do:* the best analogy I can come up with is Formula 1. Models are the engine, and Fireworks is the race team (prep, pit crew, strategy, and fine tuning of the car). You can have the best engine, but without the best race team, you are not winning. 🥇

*So how does it work in practice?* Two options:

(1) You just pick an open model off the shelf (DeepSeek, Kimi, Llama, gpt-oss...) from the 400+ already running on their platform, you call the API, and you pay per token. You do not need to rent GPUs or manage clusters: their hand-written GPU code (down to the kernel level!) runs those models. And they do so faster and cheaper than you could yourself.

Or, and most importantly (2) when off-the-shelf is not enough, they help you fine-tune an open model on your own data. With that: a specialist model, at a fraction of the cost, will beat a frontier generalist on your specific tasks. That approach is available self serve on their platform, or, for large customers - through FDEs who work directly with them to optimise models for their specific workloads.

That is, in part, how @cursor_ai built its Composer model: on Fireworks. Similarly, @harvey achieved above frontier performance at 10% of the cost (yes, that's mindblowing) working hand in hand with the Fireworks AI teams.

*And why will they win, structurally:*

(a) inference is where the money is going: ~90% of a model's lifetime compute is spent running it (as opposed to training it). And agentic AI is a driving force behind the explosion in token volumes ==> the tide rises fastest where Fireworks sits, and I think this mega-trend will continue working in their favour

(b) open models have reached near-frontier quality ==> every CFO is starting to worry about their closed-model API bill. And now Fireworks enables them to off-ramp from that exponential cost scaling: they can continue performing at frontier level (or above!), but at 10% of the cost

(c) speed: per @ArtificialAnlys (independent benchmarks), Fireworks is consistently the fastest GPU-based provider on top open models. Only the custom-silicon players beat them on raw speed - but often at higher costs and with a fraction of the model coverage

(d) no lock-in: CEOs and CTOs do not want vendor lock-in given how fast the space moves. DeepSeek beats Kimi, Kimi beats Llama, Llama beats DeepSeek again... It does not matter: Fireworks earns on all of them. Model churn benefits Fireworks.

*I also reviewed 20 expert calls. Four observations stood out:*

1) one head of AI rated their support "14 out of 10". And no, that is not a typo! He added they would be "much worse off without them" 2) one customer moved a flagship AI feature off GPT-4o onto an open model on Fireworks: 70-80% cheaper... and usage went UP 20% (👋 Jevons Paradox) 3) a global creative-software giant scored them 9/10 head-to-head vs 7 for their closest rival, and is shifting more towards Fireworks as a result 4) a top-5 US healthcare company, asked to pick one inference provider to bet on: "I would go with Fireworks"

When Cursor writes your code, when Notion's AI summarises your meeting, when Upwork drafts a freelancer's proposal... they share the same engine underneath: Fireworks AI.

The winning layer may not be the model itself, but the infrastructure that makes every open model usable. Great infrastructure companies are invisible to users, but indispensable to developers!

Let's go Lin and Fireworks team 🚀

cc. Harry Stebbings, @Kieranleehill, @codorniou, @alexandre_dewez, @NiallKiely20VC