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The TWIML AI Podcast · 4 min · Nov 10, 2026
Owning the model stack creates more room to cut inference costs
Stefano Corazza says Canva improves inference across hardware, kernels, model steps and serving.
The episode’s best minutes
Full analysis- 5:09Canva's data makes smaller design models more economical3 min
- 16:34Detailed scene descriptions help smaller models create better designs5 min
- 39:35Owning the model stack creates more room to cut inference costs4 min
- 5 more moments in the full episode
“you can only make it really efficient if you own the full stack”
Takeaways
- Inference improvements require work across the full stack.
- Canva says video generation costs fell by two orders of magnitude in a year.
- Lower costs can make AI accessible to more free users.
About this segment
Stefano Corazza says Canva improves inference across hardware, kernels, model steps and serving. Those optimizations make AI more affordable and could drive another order-of-magnitude cost drop.
Stefano Corazza is head of AI research at Canva.
Sam Charrington, Host · Host, The TWIML AI Podcast
From “AI Design Still Feels Like a Slot Machine”, Nov 10, 2026. Watch the full episode
Thanks. We'll look at and fix what's off.
Transcript
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
- 39:38
to align with the wind right and and the capabilities as opposed to trying to like fight it earlier i mentioned like kind of these two north stars you had quality and efficiency and i think we double clicked on quality but didn't really double click on efficiency recap your kind of vision for efficiency uh and you know talk through in the case of crap and brush like, what were the levers that you manipulated to drive that efficiency?
- 40:06
Yeah, efficiency in inference specifically really enables affordability of AI, right? And the majority of our users are actually free users of Canva and we love that, like students, non-profits can use Canva for free. If we can bring the cost of AI inference low, we can also allow them to use more of AI. And so we really wanted to make sure AI was something that was within reach for everybody. And so the optimizations, what we have learned is we are overall relatively early in the journey of optimizing AI inference.
- 40:46
If you can squeeze 10x performance improvement in a month, means you're very early, right? We are so far from the plateau right now and that's very exciting because it's also very satisfying for our AI engineers to be able to work on a problem for like two weeks and be like, oh, okay, it's five times faster than it was two weeks ago, right? And so we have put a lot of effort and you can only make it really efficient if you own the full stack, right?
- 41:18
Because there are optimizations you do at the GPU level, at the hardware level, the optimization you do at the kernel level, this optimization reducing the number of steps the model does, there's distillation. And so going from basically high level to low level hardware, and how you serve it, how you batch it, how you cache it, there's a pile of things that you can really do. And then if you tackle all of them, And you're going to squeeze like significant gains out of each step.
- 41:52
And that's how we have seen, for example, the cost of generation of a second of 720p video in literally over a year go down two orders of magnitude. Right now you can generate some cent where like a year ago it was like half a dollar per second of generation. And so there's still a lot to do there. I'm not going to forecast that AI inference is going to be as cheap as storage but we have seen what happened with storage at the beginning we were paying by the gigabytes and it was so expensive and you had to do your math and I don't even know how many terabytes I have of storage in my Dropbox account I don't know I don't think I will ever have the problem to think about it and so I don't think AI inference will get to that level but it's getting so much better than it was a year ago.
- 42:50
And I think given the first derivative is definitely still very healthy, I think we're going to see probably another order of magnitude going down. And when you get to like 0.1 cent per generated image or generated second of video, I think it's another breakthrough. And all of a sudden, the democratization and affordability go really beyond what we see today. In building that full stack and operating that full stack, are you operating the full stack?