The Ledger
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fal employs over 30 engineers with zero dedicated engineering managers
“Yeah, we don't have engineering managers. We have around like. 30 to 34 engineers. We do have leads. Obviously we have leaders in the In the team, but you know, we don't have this engineering manager role. Everyone is always, like, contributing, writing code. …”
Major film studios actively seek generative AI video tools from fal
“Something changed this summer and we are getting a ton of interest from basically all the studios in LA or elsewhere. Everyone is really interested to at least do something about it because now they understand this is good enough and they can actually save mon…”
Small mixed-group feedback discussions are more constructive than traditional 1-on-1s
“Instead of one-on-ones, we try to do like smaller groups of discussions, like one-on-three or whatever, one-on-four. And we try to bring like people from within the team, but maybe someone who joined recently, someone who's been there for a while, someone who'…”
Optimizing common AI workflows creates more value than arbitrary custom deployments
“Everyone wants to do the same thing over and over again. And therefore we thought there is value in actually optimizing the most common workflow.”
Overwhelming inbound demand turns AI software sales into a qualification challenge
“With AI, you have so much demand coming from the market. You have to qualify, like your problems are very different. Your problem is you have to qualify who to spend time with. You have to qualify who is going to have the most spend among these companies for y…”
fal hired specific employees simply because they had active X profiles
“We hired couple people just because they had an active X and They ended up being like really active members of the community as well.”
fal reached $100M ARR with only 6 to 10 go-to-market staff
“We have around six, maybe, maybe 10 if you include CSM and like all that.”
fal hired six account executives before hiring a head of sales
“We built a sales team before we hired the head of sales. I think this is number one question, like series A or series B companies ask themselves what comes first. We decided to hire I think like six AEs first, everyone reported to either me or Burkai, and then…”
AI research labs actively approach fal for day-zero model releases
“And now with FAL's position in the market, we also get some early information from the research labs. Everyone like tries to talk to us. And release their models on file on day zero.”
fal built managed APIs to control code over arbitrary GPU orchestration
“Instead of Focusing on like GPU orchestration and letting people deploy whatever they want. We decided to build, you know, APIs. Every single code that's deployed is owned by us and we control the whole process.”
Cursor is better suited for product engineering than low-level ML optimization
“Our product team uses cursor or equivalent tools a lot. Like I see the monthly bill and it keeps increasing. Yeah, I think it's better suited for product engineering type work as opposed to some of the low level optimizations we are doing. On the ML side.”
Raising alongside competitors puts startups at disadvantage due to investor fatigue
“And that's something we underestimated how, how disadvantaged of a situation it is to raise at the same time with seemingly all your competitors, because they all say the same story. Investors hear it over and over again, and there's some Fatigue of hearing th…”
Low-level systems engineers can master GPU optimization without prior GPU experience
“If they were that database company before, or if they did any like low level systems engineering, that is a big plus, even if they haven't worked with a GPU before. We believe they can learn very fast”
Off-the-shelf models eliminate data prep for all but the largest enterprises
“If there's a ready-made model that changes everything, this whole like data preparation stage. Can be skipped and only like the biggest of the companies are going to do that. Everyone else, they'll just use something off the shelf.”
fal hosts around 600 models compared to under 10 at AI labs
“So the number of models they have to host is like less than 10. And for us, it's like around 600, which is, complicates things like a lot.”
fal never monetized its viral real-time image-to-image inference demo
“Still, we didn't make any money from that demo. It makes a really impressive, you know, technical demo for people to see how fast we can run inference, but we couldn't find any use case for that, like very fast image to image inference. Still to this day, it i…”
fal maintains a 15-person applied ML team dedicated to model deployment
“We have a Applied ML team. It's around 15 people right now. And, you know, all they do every day is either deploy these models, optimize them, play with them, and they're obsessed with it.”
fal converts pay-as-you-go AI usage into multi-million dollar annual commitments
“We built a sales team early on, maybe earlier than some of our competitors. And we tried to get as much of this revenue in form of yearly commitments rather than pay as you go. To this day, I think we are doing an incredible job at that. And that protects the …”
Running two different products simultaneously creates conflicting messaging for sales
“It's very hard when you are not screaming exactly what you are doing to your customers, to the potential customers, to people you work with. It's really hard to sell because, you know, they look at your website, they see something else, like So it is really ha…”
Early indie developer customers on fal spent tens of thousands daily
“Everyone was spending serious money on the platform, tens of thousands of dollars a day, all of a sudden.”
fal maintains roughly 500 shared Slack channels with customer engineering teams
“We have, I don't know, 500 different Slack channels with all the engineers from companies we work with. And the response rate of those Slack channels, you measure that daily and like we obsess over that.”
fal struggled to raise Series A because VCs doubted image inference
“And we tried to explain this to people because it was so new, like no one got it. Everyone thought, An inference platform is an inference platform. Doesn't matter what kind of model it is. There are other people who are more qualified or more prepared to do th…”