Build vs Buy AI: Should Your Business Build Custom AI or Buy AI Products?
Most businesses should buy AI for common tasks such as drafting, summarising, invoice capture and service desk support, and only build custom AI where the process is distinctive, valuable and not served well by existing products. Building also means owning the ongoing cost of running, monitoring and maintaining the solution.
Written and reviewed by The Technology Office · Independent technology advisory
Key takeaways
- There are really four options: use AI features already in your software, buy a specialist AI product, configure a platform, or build custom.
- Buy when the task is common across many businesses; build only when the process is distinctive, valuable and poorly served by products.
- Custom AI carries ongoing costs for hosting, usage, monitoring, model changes and support that are often left out of the business case.
- Whichever path you choose, test it in a measured pilot and keep the option to switch, because AI products and models change quickly.
The short answer
Most businesses should buy AI for common tasks – drafting, summarising, invoice capture, service desk support – and build only where the process is distinctive, valuable and poorly served by existing products. Building also means owning the ongoing cost of hosting, usage, monitoring and maintenance, which is often missing from the business case. Whichever path you choose, prove it in a measured pilot first.
It is not really a two-way choice
Build vs buy sounds binary, but in practice there are four options on a spectrum:
| Option | What it means | Example |
|---|---|---|
| 1. Use what you have | Switch on AI features in software you already pay for | AI assistant in your productivity suite, AI matching in your accounting platform |
| 2. Buy a specialist product | Subscribe to a purpose-built AI tool | AI document processing, contact centre AI |
| 3. Configure a platform | Use low-code AI and automation platforms to assemble a solution | An AI workflow built in a low-code platform connecting email, forms and your CRM |
| 4. Build custom | Develop your own application using AI models via APIs | A proprietary tool that applies your pricing logic to complex quotes |
Cost, speed, control and risk all change as you move along this spectrum. Many businesses jump straight to option 2 or 4 without checking option 1.
When should you buy?
Buying – including using existing features – is usually the right answer when:
- The task is common across many businesses, such as meeting notes, email drafting, invoice extraction or ticket triage.
- Speed matters and you want results within weeks rather than months.
- You lack in-house AI engineering capability to build and maintain a solution.
- The vendor handles compliance, security and updates to a standard you can verify.
- The process is not a source of competitive advantage, so being the same as everyone else is fine.
Before buying, check for overlap. It is common to find several departments paying for separate AI tools that do similar things. Vendor management discipline helps consolidate and negotiate.
When should you build?
Custom AI is justified when most of these are true:
- The process is distinctive and tied to how you compete or deliver service.
- It depends on your proprietary data, rules or expertise that a generic product cannot use well.
- Volume is high enough that the benefit outweighs the build and running cost.
- Products have been tested and fall short, not merely assumed to.
- You can own it long term – with people or a partner to monitor, maintain and improve it.
Note that building today rarely means training your own model. It usually means combining a commercial AI model with your data, business rules, integrations and an interface. If you do train or fine-tune models on personal information, the OAIC's guidance on developing and training generative AI models sets out important privacy considerations.
The hidden costs on each side
| Buying | Building | |
|---|---|---|
| Upfront | Implementation, configuration, training | Design, development, integration, testing |
| Ongoing | Subscriptions that rise with users or usage | Hosting, model usage charges, monitoring, support |
| Change | Vendor changes features or pricing | Model updates can change behaviour; you must retest |
| People | Admin and vendor management | Engineering and product ownership |
| Exit | Data extraction, migration, lock-in | Knowledge loss if key people leave |
A business case that only counts subscriptions on the buy side, or only development on the build side, will mislead. Our article on the AI cost trap explains how underestimated running costs derail initiatives, and AI transformation cost planning covers how to budget properly.
A practical decision process
Use a consistent sequence for each use case:
- Define the problem and baseline – what process, how much time it takes and what success looks like.
- Check existing tools – can software you already own do this adequately?
- Assess the market – shortlist specialist products and test them on your real data.
- Consider configuration – could a low-code platform deliver it with less risk than custom code?
- Only then evaluate a build – with a full lifetime cost and an owner identified.
- Pilot the chosen option – measure against the baseline before committing further.
- Plan the exit – how you would switch if the product, vendor or model changes.
A worked example: a professional services firm wants AI help with proposals. Its productivity suite AI drafts reasonably well from past documents, so it starts there. After a pilot, the firm finds the real bottleneck is applying its complex pricing model, which no product handles. It then builds a small, focused tool for pricing only, rather than a full custom proposal system.
Avoid lock-in either way
AI products and models are changing quickly. Protect your options by:
- Keeping your data in formats and systems you control.
- Reviewing contract terms on data use, model training, retention and exit.
- Designing custom solutions so the underlying AI model can be swapped.
- Reviewing the decision at least annually.
If you are considering agent-style solutions, our explainer on AI agents for business covers the extra controls they need.
How The Technology Office helps businesses decide whether to build or buy
Build vs buy is exactly the kind of decision that benefits from an independent view. The Technology Office sells no software and has no development team to keep busy, so we have no reason to favour one answer. We bring CIO and CTO-level experience to define the problem, test options against your real data, model full lifetime costs and run measured pilots before you commit.
Through our AI consulting service, and with a fractional CTO where custom development is warranted, we help businesses of any size and industry across Australia choose the option that delivers evidenced value.
Frequently asked questions
Is it cheaper to build or buy AI?
Buying is usually cheaper and faster for common tasks because development and maintenance costs are shared across many customers. Building can be more economical at scale for a distinctive process, but only once ongoing running and maintenance costs are included.
When does building custom AI make sense?
When the process is core to how you compete, relies on your own data or expertise, has enough volume to justify the investment, and existing products cannot do it well. You also need the capability to run and maintain it.
What does building custom AI actually involve?
Usually not training a model from scratch. Most custom AI combines a commercial AI model with your data, business rules, integrations and a user interface, plus testing, security, monitoring and ongoing support.
What are the risks of buying AI products?
Vendor lock-in, unclear data handling terms, overlapping tools across departments, limited ability to customise and products that change or disappear. Careful contract review and an exit plan reduce these risks.
Do privacy obligations differ between building and buying AI?
Your obligations under the Privacy Act apply either way. If you build, you carry more direct responsibility for how personal information is used, including in any model training; if you buy, you must check how the vendor handles your data.
Sources and further reading
- Guidance on privacy and developing and training generative AI models — Office of the Australian Information Commissioner (OAIC)
- Guidance on privacy and the use of commercially available AI products — Office of the Australian Information Commissioner (OAIC)
Mentioned services
These service and regional NSW pages expand on the topics covered in this article.
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