Choosing the right AI API isn't just a technical decision. It’s a business decision that can make or break your company. I’ve seen it happen. A wrong turn here can cost you months of engineering time, hundreds of thousands of dollars, and a massive competitive disadvantage. I’ve been fortunate to have a couple of successful exits with RemoteTeam (acquired by Gusto) and MovieLaLa (acquired by Gfycat), and I’ve invested in over 200 startups, including some of the biggest names in AI like Anthropic, OpenAI, and Scale AI. I’ve seen this movie play out dozens of times, and I want to share the top three mistakes I see founders make when it comes to AI APIs.
Mistake #1: Chasing the Hype
The AI world is full of shiny objects. Every week there’s a new model that’s supposedly the next big thing. I see so many founders get caught up in the hype, thinking they need the latest and greatest model to succeed. They read a blog post, see a cool demo on Twitter, and immediately want to rip out their existing integration and switch to the new hotness.
This is almost always a mistake.
When we were building RemoteTeam, we needed a reliable way to handle payroll calculations for different countries. There were a lot of fancy new fintech APIs coming out at the time, all promising to solve our problems with a few lines of code. We could have spent months chasing the latest and greatest, but instead, we focused on what we actually needed: a stable, reliable, and well-documented API that could handle the basics. We ended up choosing a more established player that had been around for years. It wasn’t the sexiest choice, but it was the right one. It allowed us to build a robust and scalable platform that we eventually sold to Gusto.
Mistake #2: Ignoring the Full Cost
Founders are often so focused on the per-API-call price that they completely miss the bigger picture. The sticker price of an API is just one small part of the total cost of ownership. You also need to factor in the cost of integration, maintenance, and the potential for vendor lock-in.
I once advised a startup that was building a customer support chatbot. They chose a new, seemingly cheap AI API from a small, unknown provider. They were thrilled with the low per-call price. But then the problems started. The documentation was a mess. The API was constantly changing. They had to spend countless engineering hours just to keep the integration working. And when they finally decided to switch to a more reliable provider, they realized they were locked in. The new provider had a completely different data format, and it would take them months to migrate everything over. That "cheap" API ended up costing them a fortune.
Don't be that startup. When you're evaluating an AI API, think about the total cost. How much time will it take your team to integrate it? What’s the quality of the documentation? How often does the API change? What’s the migration path if you decide to switch? These are the questions that will save you from a world of pain down the road.
Mistake #3: Building When You Should Be Buying
As a founder, your most valuable resource is your time. You need to be laser-focused on building your core product and delighting your customers. Yet, I see so many founders fall into the trap of building their own AI models from scratch. They think it will give them a competitive advantage. They think it will be cheaper in the long run. They are almost always wrong.
Unless you are an AI research company, you have no business building your own models. The big players like OpenAI, Anthropic, and Google have teams of thousands of the world’s best AI researchers. They have access to more data and computing power than you could ever dream of. You are not going to out-innovate them.
Instead of trying to build your own models, you should be focused on building a unique application on top of their APIs. That’s where the real value is. When we built MovieLaLa, we didn’t try to build our own recommendation engine from scratch. We used existing APIs to pull in data about movies and TV shows, and then we built a fun and engaging user experience on top of that. We focused on what we were good at – building a great product – and we let the API providers handle the heavy lifting on the backend. That’s how we were able to build a company that was eventually acquired by Gfycat.
The API-First Philosophy
At the heart of these mistakes is a failure to adopt an API-first philosophy. An API-first approach means that you treat your APIs as first-class citizens. You design your product around your APIs, not the other way around. This has a number of advantages. It forces you to think about your product in a more modular and scalable way. It makes it easier to integrate with other services. And it gives you the flexibility to switch out APIs as your needs change.
At RemoteTeam, we went all-in on the API-first approach. We knew we needed to support a wide range of payroll and HR services in different countries. So we designed our entire system as a collection of microservices that communicated with each other through APIs. This allowed us to quickly add new services and integrations without having to re-architect our entire platform. It was a lot of work upfront, but it paid off in the long run. It’s what allowed us to scale to thousands of customers all over the world.
How to Choose the Right AI API
So, how do you avoid these mistakes and choose the right AI API for your business? Here’s a simple checklist to get you started:
- Focus on your core problem. What is the one thing you need the API to do, and do it well? Don’t get distracted by fancy features you don’t need.
- Do your homework. Read the documentation. Check out the company’s track record. See what other developers are saying about them.
- Think about the total cost. Don’t just look at the sticker price. Factor in the cost of integration, maintenance, and potential vendor lock-in.
- Don’t build what you can buy. Unless you’re an AI research company, you should be using off-the-shelf APIs. Focus on building a unique application on top of them.
- Run a small-scale test. Before you commit to a provider, run a small-scale test to see how the API performs in the real world. This will help you catch any potential issues before they become big problems.
The Bottom Line
Choosing the right AI API is one of the most important decisions you’ll make as a founder. Don’t take it lightly. Do your research, think about the long-term costs, and don’t be afraid to go with a boring, reliable solution. Your future self will thank you for it.
Frequently Asked Questions
How can I apply this thinking to my own situation?
Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.
How has this view evolved over time?
My thinking on most topics has changed significantly over the years. Early in my career, I held many conventional views that experience proved wrong. I try to update my beliefs when the evidence changes.
What's the most common pushback you get on this?
People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.