With tech companies globally now building out their own AI tools, many face the same question: should they build their own models, fine-tune or distil existing offerings, or just wholly rely on a third party?
For Canva, the one-word answer is 'hybrid', but the company is clearly favoring training its own models and it starts to make a lot of sense when you uncover why.
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It makes sense for Canva to train its own models
When Canva launches a new AI feature, it doesn't wait to build everything from scratch. While its growing portfolio of models might now start acting as a basis, the company has long been plugging into some of the most popular models on the market during the early stages.
"If there's a good third-party model, we will just use it, so that we start seeing usage," Head of AI Research Stef Corazza explained to TechRadar Pro at Canva Create 2026.
This 'buy first' phase gives Canva quicker access to something more valuable – real user behavior. The whole goal is to meet the customer where they are with an effective solution, so if a certain tool turns out to be a flop, endless hours and resources training a specific model variant could end up being wasted.
When using a third-party model, the goal isn't perfection: it's learning. It's when a real use case becomes apparent that Canva then goes on to invest in proprietary solutions, and this is where it gets better, Corazza notes.
Firstly, once a feature is being used by millions of people, the economics change. Third-party models become expensive to run at scale, but more importantly, generic outputs stop being good enough.
Why build at all?
By training its own models, Canva can reduce inference costs dramatically, optimize performance for specific tasks and control how the AI behaves inside its platform.
General-purpose models try to do everything, but with that comes a lot of unnecessary noise. Canva's proprietary models can become much lighter, more streamlined and dedicated to a specific task.
Instead of building one large model to do everything, it can build many focused ones that each do one thing well, and feedback about how users generate content, how they edit it and what they publish creates a continuous loop.
Ultimately, by being in control of all the layers of Canva AI 2.0 beneath the Visual Suite that users interact with, it gives Canva control over the full stack, building efficiency into the platform that keeps costs low and allows the company to offer a free tier and unpaid access to the likes of education users.
It also serves as a gentle reminder that bigger isn't always better, and that a more focused approach can make AI feel more like an extension of an existing process rather than a disconnected feature.
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