The eternal software question of “should I build this, or use someone else’s service?” is very applicable to AI. The benefits of building it yourself are that you have control of proprietary technology, which gives you patent defensibility and reduces the risk of relying on another company.
The costs are pretty substantial however. Building machine learning product is very different from building other kinds of software. It takes different skillsets, expertise, and processes. Release cycles take longer, observability is lessened, and fewer colleagues intuitively understand how it works.
I generally only recommend building AI in-house if you are willing to properly invest in the people and resources required to get it right. You can’t put a team on it for a few months and see how it goes. You need to hire specialised people and help them to work in a specialised way.
Those people are expensive, and demand for their skills will grow much quicker than supply. The same is true for hardware required, like GPUs. That’s why companies building their own models need to raise such large amounts of money.
A good rule of thumb is also to ask yourself: is the use case that we have here relatively unique to our company? For example, every company needs to do customer support, so there will likely be decent customer support AI emerging. However there are very few companies that offer ridesharing like Uber, so there is unlikely to be an off-the-shelf solution for their pricing algorithms.
One general argument against building your own AI is that pre-PMF you likely won’t have hordes of data that is both proprietary and useful. This means that building your own thing only makes sense if you can get useful data from another source, and populate your use case better than third parties would.
At the end of the day, the best thing that you can do is to scout around the market and see what’s on offer. There will always be benefits and costs to whatever route you take, so it’s best to scope out your options.