Costing It Out - The Real Price of DIY AI

Let’s talk money, because AI isn't cheap.

There’s a common assumption that building your own AI model is a one-time investment that pays for itself over time. But the actual costs, especially in the first 18 to 36 months, can be far higher than anticipated.

Breaking Down the True Costs

1. Talent and Team

Hiring the right people is expensive. Here’s a rough view of annual salaries (GBP):

• ML Engineer: £80K–£120K
• MLOps Engineer: £70K–£100K
• Data Scientist: £60K–£100K
• Data Engineer: £60K–£90K
• Compliance & Security: £50K–£80K

A fully functioning in-house team can easily run you over £500K per year.

2. Compute and Storage

Training large models requires serious GPU horsepower:

• Renting A100/H100 instances can cost £2–£6/hour, depending on availability and configuration
• Training cycles often require thousands of GPU hours
• Add in storage for datasets, backups, and checkpoints, costs quickly spiral

Even modest custom models can exceed £100K+ in compute spend annually.

3. Tools and Platforms

• ML experiment tracking
• Model versioning and monitoring
• CI/CD pipelines for ML (MLOps platforms)
• Security controls and API management

These are rarely free, and often grow in cost as usage increases.

4. Compliance, Risk, and Legal

• Data residency and handling audits
• Legal reviews for model bias or explainability
• Risk of GDPR or regulatory breaches (especially in finance/health)

Getting it wrong isn’t just expensive, it can cause irreversible brand damage.

5. Opportunity Cost

Every hour your team spends on infrastructure and model training is time not spent improving business outcomes. If your AI isn’t your product, are you burning resource on the wrong priority?

Where DIY Fails Most Often

• Overestimating data readiness: Poor quality or limited data tanks performance.
• Underestimating iteration time: From prototype to production can take months.
• Ignoring lifecycle costs: Models degrade, constant updates and retraining are essential.

It’s not uncommon to hear companies say:

"We spent £400K on building a model that now no one uses."

So, What’s the Smarter Spend?

For many, the right move is to start with pre-built and only invest in full model builds when:

• The business case is proven
• The data is ready
• The need for control or IP is genuine

This lets you build a financial case, win early, and scale intelligently.

In the next post, we’ll explore that question head-on: When does building your own model actually make sense?

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