Had this conversation three times last week with different founders. “Should we build our own AI capability or use existing tools?”

They’d all done some analysis. Spreadsheets comparing build costs against subscription fees over three years. And they’d all made the same mistake: underestimating the hidden costs of building.

The Optimistic Build Estimate

Every founder’s build estimate follows a similar pattern:

“Our developer costs $150K. We’ll need maybe two months of their time. So roughly $25K to build, versus $12K per year for this SaaS tool. We break even in year two!”

This math is seductive. It makes building look like the obviously smart financial decision.

It’s also almost always wrong.

What the Estimate Misses

Time overruns. Two months becomes four. AI projects hit unexpected complexity—edge cases, data quality issues, integration problems. I’ve never seen a startup AI project finish on the original timeline.

Opportunity cost. Those two months (really four) of developer time mean something else doesn’t get built. Your core product feature roadmap slips. Your technical debt accumulates. Your best engineer is doing AI plumbing instead of differentiated product work.

Maintenance burden. Building is the beginning, not the end. AI models drift. Dependencies break. New requirements emerge. You’ve now created a permanent maintenance obligation that grows over time.

Quality gap. Unless AI is your core product, your in-house solution will be worse than specialised tools built by teams who’ve been iterating for years. Sometimes “worse” is acceptable. Often it’s not.

When Building Actually Makes Sense

Building is the right choice in specific situations:

AI is your product. If AI capability is your competitive differentiation, buying it from someone else means your competitors can too. Build the core.

No adequate tool exists. Genuinely novel applications sometimes don’t have off-the-shelf solutions. Emphasis on “genuinely novel”—most founders overestimate their uniqueness.

Data privacy makes SaaS impossible. Some industries can’t send data to third-party AI services. If that’s you, building may be the only option.

Scale makes unit economics work. At massive volume, built solutions can be cheaper. But you need serious scale—millions of operations, not thousands.

The Hybrid Path

Most startups end up somewhere in between. Use SaaS tools for standard capabilities (customer service, content generation, basic analytics) and build only where you have genuine differentiation.

A fintech client built their own fraud detection models—that’s core to their value proposition. But they use Intercom for customer support chatbots. No point building what already exists.

This hybrid approach requires honest assessment of where your actual competitive advantage lies. It’s usually a smaller area than founders think.

Running the Real Calculation

If you’re tempted to build, run these numbers honestly:

Development cost: (Hourly rate × estimated hours) × 1.8 for overruns

Ongoing maintenance: 20% of development cost annually

Opportunity cost: What else could this time produce? Be specific.

Quality gap penalty: What’s the business impact of your solution being 80% as good as the commercial alternative?

Timeline value: What’s the cost of launching 6 months later than using an existing tool?

Add these up. Compare against the tool subscription. The math usually favours buying.

The Ego Factor

Let’s be honest about something: there’s an ego component to “we built our own AI.”

Building sounds more impressive than buying. It suggests technical sophistication. It makes good fundraising stories.

But investors don’t actually care whether you built or bought. They care whether you’re using resources efficiently to grow the business. Spending $100K building what you could buy for $15K/year isn’t sophistication, it’s waste.

Practical Recommendations

For most early-stage startups:

• Default to buying unless you have a compelling reason to build
• Evaluate build decisions on three-year total cost, not year-one estimates
• Factor in your team’s AI experience—inexperienced teams underestimate build complexity dramatically
• Consider whether the AI capability is core or supporting

If you’re genuinely unsure, start with a commercial tool. You can always replace it later with a built solution if scale or differentiation demands it. The reverse is harder—untangling a failed internal AI project takes time and morale.

The Exception

One scenario where building makes sense even for early-stage startups: when experimentation cost is low and learning value is high.

If you can build something in two weeks that teaches your team about AI applications in your domain, that’s probably worth doing. The knowledge often transfers even if the specific project doesn’t scale.

Just don’t confuse a learning experiment with a production system decision. The two require very different approaches.