AI is making it easier for SaaS companies to build integrations. Give a coding agent decent API docs, some context about the systems involved, and a clear prompt, and it can get surprisingly far. It can write the logic faster than most teams could a year or two ago, saving time, reducing repetitive work, and making it easier to respond to customer requests.
Faster creation can make the overall problem look simpler than it really is. The code may come together more quickly, but the operational burden underneath it doesn’t go away.
Building Isn’t the Hard Part
Building the integration itself isn’t the hard part. A good engineer could always get a long way with API docs and some time, and now AI can help do a lot of that faster. The hard part is everything that comes after the code is written.
This is where teams run into unexpected problems. For example, a credential could expire, causing hundreds of synchronization jobs to fail without notice. A change to a third-party API could send back unexpected values, letting bad records slip through unnoticed. A workflow that was quiet all day might suddenly have thousands of events to process at once. Support teams may need to answer a customer asking why an invoice never arrived, but the logs are scattered across different systems, so engineers have to help. This is what integration work looks like when you have more than just a few integrations and customers.
Platform and Infrastructure Are What Makes It All Work
This is why AI doesn’t reduce the importance of integration infrastructure. In many ways, it increases it. If teams can build integrations faster, they’ll build more of them. They’ll promise more integrations, support more workflows, and ship more things that have to run reliably. That puts more pressure on the layer underneath. The question is whether teams have the infrastructure to make integrations work at scale.
Building out integrations to more and more systems with a larger and larger customer base can cause significant operational headaches. At first, developing integrations seems easy. However, as soon as you have to support hundreds or thousands of distinct customers with different configurations, credentials, rate limits, data models, and support expectations, it can become a nightmare. The operational complexity increases rapidly with each integration, including retry behavior, monitoring, tenant isolation, support tools, and security boundaries. Those are the circumstances in which teams realize they were never really building just integrations. They were building an integration platform badly, piece by piece.
Part of the reason this sneaks up on teams is that the early signs of strain rarely look strategic. They look like one-off support tickets, odd edge cases, or temporary patches. A failed sync gets rerun manually. A customer-specific exception gets hard-coded. Another dashboard gets added so someone can keep an eye on it. None of that feels like platform work in the moment. But over time, it turns into exactly that.
For technical leaders, the practical question is where to draw the build-versus-buy line. The cleanest way to think about it is this: Build the parts that are tightly tied to your product’s value, your workflow, and your customer experience. Build where your team’s domain knowledge creates outsized value for your customers, whether that means workflow design, business logic, user-facing configuration, or the parts of the product that make the integration feel native rather than bolted on. Be much more skeptical about building parts that exist solely to keep integrations operational, because those systems usually become more expensive and more brittle as customer count, data volume, and support demands increase.
Ask yourself whether the work your team is doing to develop its own retry systems, auth handling, support dashboards, job runners, or a customer-specific config layer adds value to your product. Or does it simply recreate infrastructure that every software company eventually will need? Some teams will have the scale, constraints, or internal platform maturity to justify owning more of that stack. In those cases, the choice may be reasonable. Most teams, though, should treat that decision as a real trade-off, not a default reflex.
As generating code becomes more cost-effective, that decision becomes more important. When you have an easier way to create integration logic, you tend to underestimate the effort required to run it successfully. AI speeds up building integrations, but it doesn’t remove the operational burden that surfaces later, when the system is live, customers are waiting, and failures are real.