Build vs. Buy: Evaluating AI Tools as a Business

You need an AI capability. You have two choices: build it yourself or buy it from someone else.

This decision usually goes wrong. Teams build things that already exist. Or they buy tools that are too generic for their use case. Or they build something half-baked because they underestimated the effort. There's a framework for thinking about this clearly.

The real cost of building

Building sounds cheap. You have engineers. You have a problem. You build a solution.

But the real cost is higher than people think.

Development time. Building an ML system takes longer than anyone predicts. Feature engineering, data collection, model experimentation, debugging failures. You're looking at 3-6 months minimum for something production-ready. That's expensive.

Infrastructure. You need to serve the model, monitor it, and set up retraining pipelines. This is software engineering work, not just ML. Maybe another 2-3 months if you do it well.

Data. You need good data to train on. If you don't have it, you need to collect it. Labelling data is expensive and slow.

Ongoing maintenance. The model will degrade over time. You'll need to retrain it and monitor it. That's a permanent cost - maybe one person part-time, maybe full-time.

Opportunity cost. While your engineers are building this AI system, they're not building other things. What are they not doing?

A realistic total cost for building a production ML system: £150,000 to £800,000+ depending on complexity, scope, and your salary base. That's building the model, infrastructure, and setting up operations. This might be worth it. But you need to know the number going in.

When building makes sense

Build when you have proprietary data or methods that give you competitive advantage - buying a generic tool won't capture that. Build when your specific requirements aren't met by available products. Build when you have in-house ML engineering and infrastructure expertise - if you don't, you're either hiring (expensive) or struggling (slow). Build when the problem is unique to your business. Build when you've already failed with products and know exactly what you need.

But the default should not be "build." Most organisations overestimate their need to build.

When buying (or using APIs) is smarter

Buy when the problem is general. Fraud detection, demand forecasting, customer churn - these are solved problems. Products exist. Buy when you don't have the ML expertise to build well. Buy when you need to move fast - you can't afford a 6-month development cycle. Buy when the cost of failure is low and you're learning what's possible. Buy when you need the vendor to keep the product updated as your domain changes.

For most organisations, buying is smarter. The cost is lower, you get something faster, and you reduce risk.

The hidden costs on both sides

Building has hidden costs: hiring the right people, learning from failures slowly, integration nightmares discovered late, and ongoing maintenance that's more expensive than estimated.

Buying has hidden costs: vendor lock-in (expensive to switch later), integration work (the product almost never fits perfectly), customisation costs (making the product work for your use case requires engineering), increasing licensing costs over time, and depending on the vendor for updates and support.

A product that costs £40,000 per year sounds cheap. But if it requires £80,000 of integration work and six months of your engineer's time, the total cost of ownership is high. Calculate the full number, not the sticker price.

What the default should be

Build is seductive. Engineers like building. It feels like you're creating something. It feels smart.

But the default answer should be buy. Every time.

Here's why: building is expensive, slow, and risky. The chance you'll end up with something better than a professional product is low. The chance you'll exceed budget and timeline is high.

Start with buying. Try a product. Set a clear timeline - 3 months. If the product works, great, you got ROI. If it doesn't, you've learned something. You now know what you actually need. Either you customise the product or you know enough to build better.

The organisations that build first waste time building the wrong thing. The organisations that buy first learn fast and either find the product works or know what to build next.

This is controversial in engineering circles. Engineers think buying is "not invented here" syndrome. I think building first is "we're smarter than everyone else" syndrome. A company that focused on this specific problem for years probably solved it better than you will in six months. Most organisations that reverse this - build first, buy later - end up spending more.