I get it. You’re a founder. You’re scrappy. You’ve built your MVP with duct tape and determination. Your CTO can code anything. So when someone on the team says “we should add AI to the product,” the obvious move is to build it in-house.

I’ve watched this play out at about fifteen startups over the past two years. The hit rate? Maybe two of them shipped something that actually worked well enough to matter. The rest burned through time and money and ended up either scrapping the project or hiring outside help anyway — after they’d already wasted the runway.

Let me walk you through what keeps going wrong.

The “how hard can it be?” phase

Every DIY AI project starts with optimism. The team reads some documentation, watches a few tutorials, spins up a notebook. Within a week, they’ve got a prototype that sort of works. The demo looks promising. The founder gets excited and starts telling investors about their “AI-powered” feature.

This is the dangerous moment. Because the gap between a prototype that works in a notebook and a production system that works reliably at scale is enormous. I’d estimate it’s 10-20% of the total effort to get the prototype and 80-90% to get it production-ready. Most founders massively underestimate that ratio.

Where it actually breaks down

Data quality. Your prototype worked on clean sample data. Your real data is a mess. Missing fields, inconsistent formats, duplicates, edge cases nobody anticipated. I talked to a fintech founder who spent three months building a transaction categorisation model, only to discover that 40% of their real transaction data had descriptions that were just merchant codes — meaningless strings that the model couldn’t learn from. They had to go back and build a data cleaning pipeline first, which took another two months.

Infrastructure. Running a model locally is one thing. Deploying it to handle real traffic with acceptable latency, setting up monitoring, managing model versions, handling failures gracefully — that’s engineering work that your team probably hasn’t done before. One SaaS startup I know had their AI feature go down for 12 hours because nobody had set up proper error handling for when the model service exceeded memory limits. Their customers noticed.

Ongoing maintenance. Models degrade over time. Data distributions change. The world moves on. That model you trained six months ago? Its accuracy has probably dropped, and someone needs to retrain it. But your engineering team has moved on to other features. The AI thing is nobody’s full-time job anymore. It rots.

Opportunity cost. This is the one founders don’t think about enough. Every week your CTO spends wrestling with model training is a week they’re not building the core product features your customers are actually asking for. At a Series A startup with a 20-person team, that opportunity cost is brutal.

The real cost of DIY

Let me put some numbers on this. A typical startup AI project — let’s say you’re building a recommendation engine or an automated classification system — might involve:

• 2-3 months of an engineer’s time for the initial build
• $5K-15K in cloud compute for training and hosting
• Ongoing maintenance of maybe 20% of an engineer’s time
• Lost productivity from the inevitable debugging and firefighting

All in, you’re looking at $100K-200K in the first year when you factor in salary costs. And that’s if it goes reasonably well.

For context, bringing in experienced AI strategy support typically costs $30K-80K for a well-scoped project, delivered in 4-8 weeks, with the model deployed and documented so your team can maintain it. You get the expertise of people who’ve done this dozens of times, you avoid the common pitfalls, and your engineering team stays focused on what they’re best at.

I’m not saying never build AI in-house. I’m saying don’t do it by default. Do it when you’ve genuinely got the expertise, when AI is core to your product’s differentiation, and when you’ve budgeted realistically for the full lifecycle — not just the prototype.

When DIY actually makes sense

There are situations where building in-house is the right call:

• AI is your core product. If you’re an AI-native startup and the model IS the product, then yes, you need that capability in-house. But even then, consider bringing in advisors for the first build.
• You have genuine ML expertise on the team. Not someone who did an online course — someone who’s shipped production ML systems before. There’s no substitute for that experience.
• The use case is simple and well-understood. Basic text classification, sentiment analysis, standard recommendation systems — these are well-trodden ground with good open-source tooling. If your use case fits neatly into existing frameworks, the DIY risk is lower.

When to call for help

• You’re building your first AI feature. The learning curve is steep and the mistakes are expensive. Get help for the first one, learn from the process, then consider doing the next one in-house.
• Your timeline is tight. If you need this shipped in the next quarter, you probably can’t afford the DIY learning curve.
• The stakes are high. If the AI feature is customer-facing and errors have real consequences, the cost of getting it wrong outweighs the cost of getting help.

The ego trap

I’ll be direct about this because I’ve been the founder who fell into it. There’s an ego component to DIY AI builds. “We built it ourselves” sounds better than “we hired someone.” Investors sometimes reward the narrative of technical self-sufficiency.

But investors reward results more. Nobody cares whether your recommendation engine was built in-house or by a partner. They care whether it moves the metrics.

The smartest founders I know are ruthless about buying expertise where it makes sense and building only where they have a genuine advantage. AI, for most startups, is something you should buy or borrow the first time around.

Ship the thing that works. Save your ego for the pitch deck.