By Alex Billingsley, VP of Engineering at DeveloperTown

We’ve used the same time-tracking platform at DeveloperTown for roughly 15 years.

It has hundreds of thousands of our time entries in it. Our team knows how to use it. It works. And for most of those 15 years, we’ve been perfectly happy to pay for it.

Then we received an email telling us our cost was about to increase by roughly 8x.

There wasn’t some massive new capability being introduced alongside the increase. Our needs hadn’t suddenly changed. The economics of the relationship simply changed enough that we had to ask a question we hadn’t seriously considered before:

What would it take to replace it ourselves?

A few years ago, the answer probably would have been enough to keep us paying.

Today, it’s a very different calculation.

Within about a week, we had rebuilt much of the functionality our team actually uses.

AI has changed the build-vs.-buy equation.

The old SaaS equation was pretty simple

For years, SaaS companies benefited from a very reasonable argument.

You could build your own time tracker, CRM, project management platform, reporting tool, or internal workflow system.

But why would you?

Building software requires developers. Developers are expensive. Then you have to host it, maintain it, fix bugs, keep dependencies updated, manage security, and continue investing in the thing indefinitely.

Paying $10, $50, or $100 per user each month starts looking pretty attractive by comparison.

That logic still holds in a lot of cases.

We are not about to start building our own email provider, cloud infrastructure, payroll processor, or every other piece of technology we use.

But there is now a growing category of SaaS where the calculation is becoming less obvious.

A fairly contained application with known workflows, predictable requirements, and a limited number of internal users can be dramatically less expensive to build than it once was.

And once your SaaS bill becomes large enough, the math can flip surprisingly quickly.

Price increases can force customers to reconsider the relationship

Our time-tracking platform had historically used a very normal SaaS pricing model: we paid based primarily on the number of people using the system.

The proposed pricing structure added consumption-based charges tied to things like clients, projects, and tasks.

For a consulting company, those numbers accumulate quickly.

Our team records time against a client, a project, and a task. Under the new pricing structure, the underlying organizational structure around that time entry becomes part of what drives the bill.

The result for us was an increase from roughly $600 per month to around $4,000.

At $600 per month, replacing the platform wasn't worth thinking about.

At $4,000 per month, it absolutely was.

There is a point between those two numbers where the equation changes. That point will be different for every business.

Cost wasn't the only factor, either.

A dramatic pricing change also changes the relationship you have with a vendor.

We had used this product for around 15 years. We had built business processes around it. Suddenly, we had to consider the possibility that the economics could change again a year from now.

Once that trust is disrupted, switching becomes much easier to justify.

AI dramatically lowers the cost of replacement

DeveloperTown obviously has an advantage here.

We build software for a living.

We have engineers who understand architecture, integrations, security, deployment, and the long-term implications of creating an internal system.

But even for us, rebuilding an existing SaaS application from scratch would have been a meaningful investment a few years ago.

AI coding tools compress that investment.

In a little over a week, we were able to build a functional internal alternative that covers the workflows that actually matter to our team.

We don't need to recreate every feature in a mature SaaS product.

That's another important part of the equation.

A SaaS platform has to serve thousands of customers. It accumulates features for industries, workflows, edge cases, and personas that may have nothing to do with your organization.

An internal application only has to serve you.

We need our people to record time against the correct work. We need that data to flow into our billing and reporting processes. We need the system to be reliable enough that our invoicing can depend on it.

That's a much smaller product surface.

AI makes building that smaller surface significantly faster.

The bigger opportunity may be building above your SaaS stack

Replacing individual SaaS products is only one possible outcome.

There is another model we're increasingly interested in: keeping the systems that are good at storing or processing information while building a custom layer that works the way your business actually works.

We already see versions of this internally.

We use Salesforce, for example, but we've built sales tooling and dashboards on top of Salesforce because the workflows and information our team needs don't always map neatly to the standard Salesforce experience.

That creates an interesting progression.

You might start with several major SaaS platforms as your systems of record.

Then you build a bespoke internal application that connects them.

That application can create workflows across systems, provide reporting that doesn't exist in any individual platform, automate repetitive work, and give employees one interface designed specifically for the way your company operates.

Over time, you may discover that one of those underlying SaaS products is no longer providing enough unique value to justify its cost.

At that point, you can replace it.

The custom software doesn't necessarily start by replacing your SaaS stack.

It can slowly make pieces of that stack less important.

Maintenance has changed too

Historically, maintenance has been one of the strongest arguments against custom software.

Building version one is only the beginning.

Someone still has to own it.

Bugs appear. Requirements change. APIs get updated. Users request features. Security patches need to be applied.

That work does not disappear because AI exists.

We still have humans reviewing every piece of code going into a system our invoicing will eventually depend on.

But AI can take a substantial amount of the repetitive maintenance work off their plate.

We've already started experimenting with workflows where a well-formed bug report can automatically be assigned to a coding agent.

The agent investigates the problem, makes the change, and produces a pull request for a developer to review.

A human still decides whether that code belongs in production.

But the amount of human effort required to get from "something is broken" to "here is a proposed fix" shrinks significantly.

That matters because ongoing maintenance has always been part of the total cost of ownership calculation.

If AI reduces that cost too, custom software becomes viable in situations where it previously wasn't.

When should you consider replacing a SaaS product?

We're not advocating for companies to start rebuilding their entire software stack.

Most SaaS products exist for a reason, and buying will continue to make more sense than building in many situations.

But there are a few circumstances where the conversation is worth having.

Your SaaS cost has outgrown the complexity of the product

If you're spending tens of thousands of dollars every year for software that supports a relatively simple internal workflow, estimate what a purpose-built alternative would cost.

The answer may surprise you.

Your team works around the software more than it works in it

Spreadsheets, manual exports, duplicate entry, Zapier workflows, custom dashboards, and endless workarounds are signals that the software no longer maps cleanly to the way your business operates.

A custom layer connecting your existing systems may create more value than replacing any individual application.

Your data is fragmented across several platforms

CRM data lives in one place. Delivery data lives somewhere else. Financial information is somewhere else again.

Bringing that information together can create capabilities that none of the individual vendors are motivated to build for you.

The software supports a process unique to your business

The more differentiated the workflow, the harder it becomes for generalized SaaS to fit.

That is often where custom software creates the most leverage.

The vendor relationship has become unpredictable

Pricing changes. Packaging changes. Features disappear. Products get acquired.

Vendor risk should be part of the build-vs.-buy calculation just like development cost.

"Build vs. buy" deserves another look

For most of the SaaS era, the default answer has been to buy, but the cost of building software is falling quickly.

That means decisions your company made five or ten years ago deserve another look.

The software you once had no practical alternative to may now be reproducible.

The five SaaS tools your employees bounce between every day may be better experienced through one application built around your workflows.

And a pricing increase that once would have been painful but unavoidable may now be the thing that finally makes custom software economical.

We're learning firsthand that software has become a lot more replaceable than it used to be.

And if you're looking at your own SaaS bill wondering whether you really need to keep paying it, it might be worth running the numbers too.

Curious about replacing a tool in your tech stack? Reach out to explore your options.