“We need a custom AI solution.”

I’ve said this. I’ve heard this from dozens of founders. Usually, it’s wrong.

Custom AI sounds impressive. It feels like innovation. But it’s almost always the expensive path to a mediocre outcome.

Let me explain when custom actually makes sense.

What “Custom AI” Actually Means

There’s a spectrum:

Level 1: Custom prompts You’re using ChatGPT or Claude with carefully crafted prompts for your use case. Cost: $20/month. Difficulty: Easy.

Level 2: Custom integration You’re connecting AI APIs to your systems. Your CRM, database, or product. Cost: $5,000-20,000. Difficulty: Moderate.

Level 3: Custom fine-tuning You’re training models on your data to improve performance for specific tasks. Cost: $20,000-100,000. Difficulty: Hard.

Level 4: Custom models You’re building models from scratch for proprietary capabilities. Cost: $500,000+. Difficulty: Very hard.

Most startups claiming “custom AI” are doing Level 1 or 2. That’s fine. Just don’t overpay for it.

When Custom Integration Makes Sense

Level 2 is the sweet spot for most startups. Worth doing when:

Your data lives in multiple systems You need AI that pulls from your CRM, reads your docs, and updates your database. No off-the-shelf tool does this.

Standard tools hit walls You’ve tried Zapier. You’ve tried no-code AI builders. They can’t handle your specific workflow.

The integration is core to your product Not just nice-to-have. The AI feature is why customers buy.

Work with AI consultants Sydney if you go this route. General contractors often underestimate AI integration complexity.

When Fine-Tuning Makes Sense

Level 3 is rarely necessary. Seriously. But it’s right when:

Prompt engineering hits limits You’ve spent weeks optimizing prompts. You’ve tried every technique. Performance isn’t good enough.

You have unique, proprietary data Medical records. Legal documents. Technical manuals. Stuff the base models haven’t seen.

Accuracy matters critically Not 90% good. 99% good. Legal liability. Medical decisions. Financial compliance.

If you’re not in this category, skip fine-tuning. Prompt engineering gets 95% of the value at 5% of the cost.

The Real Costs

Here’s what custom AI actually costs in Australia:

Level 2 (Integration)

• Discovery and architecture: $3,000-8,000
• Development: $10,000-30,000
• Testing and deployment: $2,000-5,000
• Ongoing maintenance: $500-2,000/month

Level 3 (Fine-tuning)

• Data preparation: $5,000-20,000
• Training and evaluation: $10,000-40,000
• Integration: Everything from Level 2
• Model updates: $5,000-15,000/year

Add 50% for things going wrong. They will.

Questions Before You Build

Before any custom AI project, answer honestly:

1. Have you maxed out off-the-shelf tools?
2. Can you quantify the value AI will create?
3. Do you have the data AI needs?
4. Who maintains this after launch?
5. What happens when the AI is wrong?

If any answer is “I don’t know,” you’re not ready.

The Minimum Viable Approach

Start ugly. Always.

Use ChatGPT manually for a month. Understand the task deeply. Document edge cases.

Then automate with Zapier or Make. See if basic automation is good enough.

Only then build custom. You’ll know exactly what you need. You’ll spec it better. You’ll spend less.

Custom AI is sometimes right. It’s never the first step.