There’s a pitch that goes around boardrooms constantly: “We should build our own AI capability in-house.” It sounds strategic. It sounds like you’re investing in the future. And sometimes it genuinely is the right call. But more often than not, the people making this decision haven’t done the maths properly.

I’ve watched a bunch of Australian companies wrestle with this decision over the past two years, and the gap between what people expect AI development to cost and what it actually costs is enormous. Let’s break it down honestly.

The True Cost of In-House AI Development

The first mistake companies make is thinking about AI talent purely in terms of salary. Yes, a mid-level machine learning engineer in Sydney will cost you somewhere between $150,000 and $200,000. A senior one? North of $220,000, easily. But salary’s just the starting point.

You need a team, not a person. One ML engineer can’t do it all. You’re looking at a minimum viable AI team of three to four people: a machine learning engineer, a data engineer, someone who understands your domain well enough to define the problem properly, and ideally someone who can handle MLOps and deployment. That’s $500,000 to $800,000 in salaries alone before anyone’s written a line of code.

Then there’s infrastructure. Cloud compute for training models isn’t cheap. GPU instances on AWS or Azure can run $2 to $30 per hour depending on what you need. A serious training run might cost thousands of dollars. Multiply that by the iterative nature of ML development—where you’re running hundreds of experiments—and your compute bill adds up fast.

Don’t forget the hidden costs: recruiting (good luck finding ML talent in Australia quickly), onboarding time, software licenses, data infrastructure, and the opportunity cost of your existing team members being pulled into AI-adjacent work.

A realistic first-year cost for a small in-house AI team building a meaningful product? Somewhere between $800,000 and $1.5 million. And that’s assuming you find the right people quickly, which you probably won’t.

What Outsourcing Actually Looks Like

Outsourcing AI development comes in different flavours. There’s the offshore dev shop approach, which tends to be cheaper upfront but often produces disappointing results because the team doesn’t understand your business context. There are specialist AI consultancies that charge premium rates but bring deep expertise. And there’s the growing middle ground of local firms like Team400 that combine technical depth with enough business context to actually deliver useful outcomes.

A typical outsourced AI project might cost anywhere from $50,000 for a focused proof of concept to $300,000 or more for a production-ready system. That’s a fraction of the first-year in-house cost, and you’re paying for outcomes rather than building capability you might not need long-term.

The trade-off is real though. When you outsource, you don’t build institutional knowledge. Your team doesn’t develop the muscle memory of working with ML systems. If AI is genuinely core to your business strategy—not just a nice-to-have—that matters.

When In-House Actually Makes Sense

Building in-house is the right call when AI is your product, not just a feature. If you’re a company whose entire value proposition depends on machine learning models, outsourcing your core capability is risky. You need the deep understanding that comes from having the team on-site, iterating daily.

It also makes sense when you’re dealing with highly sensitive data that genuinely can’t leave your organisation. Healthcare, defence, and certain financial services fall into this category—though even then, there are ways to structure outsourced engagements with proper data governance.

And it makes sense if you’re playing a long game and willing to invest for two to three years before seeing meaningful returns. Building a great AI team takes time. The first year is mostly about hiring and infrastructure. Real productivity kicks in from year two.

When Outsourcing Is the Smarter Move

For most Australian SMBs and mid-market companies, outsourcing makes more sense than people want to admit. If you need a specific AI capability—say, automated document processing or demand forecasting—you’re better off paying specialists to build it than assembling an entire team for what might be a six-month project.

Outsourcing is also sensible when you’re exploring. If you’re not sure AI can actually solve your problem, spending $50,000 to $80,000 on a proof of concept is a much better use of money than hiring three people and finding out six months later that the data isn’t there.

The Hybrid Approach

The approach I see working best for most companies is a hybrid model. Outsource the initial build to people who’ve done it before. Have them work closely with your internal team so knowledge transfers naturally. Then bring the ongoing maintenance and iteration in-house once you understand what you’ve got.

This gives you speed (external experts can move fast), quality (they’ve made the mistakes already), and eventual ownership (your team learns by working alongside them).

The Numbers Don’t Lie

Here’s the uncomfortable truth: most Australian businesses that try to build AI in-house from scratch either spend far more than they budgeted, take much longer than they planned, or both. The companies that get AI working fastest tend to be the ones that start with external expertise and build internal capability gradually.

Whatever path you choose, make sure you’re comparing real numbers. Not the optimistic salary figure for one engineer, but the actual all-in cost of building and maintaining an AI capability. The difference between what you expect to pay and what you actually pay is almost always larger than you think.