Most build-versus-buy comparisons for AI tooling look at a single point in time: what does the SaaS tool cost this month, what would building the equivalent workflow cost this month, pick whichever is lower. That snapshot approach misses the more important question for an Australian business planning past the next financial year: which side of the comparison moves faster as vendor pricing changes, and what does that do to the decision eighteen months from now.
Prices are not static, and they are not moving the same direction
AI vendor pricing has moved in two directions simultaneously over the past two years. Underlying foundation model costs have generally fallen as competition and efficiency gains compound, which should make building your own workflow cheaper over time. At the same time, many SaaS tools built on top of those models have held or raised their per-seat pricing, capturing the margin improvement for themselves rather than passing it through to customers. A business that built its own workflow two years ago is now paying meaningfully less per task than it did at launch. A business renting the equivalent SaaS tool is often paying the same or more.
Why this flips the maths over time, not just today
A build-vs-buy comparison run honestly should model both sides forward, not just compare today's numbers. The build side's ongoing cost, largely API usage, tends to track down with model pricing trends. The buy side's ongoing cost tends to track the vendor's pricing decisions, which are influenced by their own margin targets more than by underlying model costs. Over a three-year horizon, that divergence compounds into a meaningfully different total cost of ownership than a first-year snapshot suggests.
Model both build and buy costs over a three-year horizon, not just the first year, using recent trend data for each side.
Separate the vendor's per-seat price from the underlying model cost it is likely built on, to judge how much margin the vendor is capturing.
Weight the comparison by how much your usage is likely to grow, since a buy-side per-seat model scales cost with headcount in a way a build-side API model does not.
Revisit the comparison annually rather than treating the original build-vs-buy decision as permanent.
A worked example
A Melbourne professional services firm built its own client-report drafting workflow eighteen months ago rather than buying a $45-a-seat SaaS tool, at a build cost of roughly $6,000 across a 22-person team. At the time, the build option's ongoing API cost was close to the SaaS tool's price once amortised. Eighteen months later, the firm's actual API cost has fallen by roughly a third as model pricing has improved, while the SaaS alternative they evaluated originally has raised its per-seat price twice. The gap between the two options, roughly break-even at the original decision point, is now firmly in the build option's favour, saving the firm close to $9,600 a year compared to what the SaaS tool would now cost the same team.
This does not mean always build
The direction of price trends favours ownership over time for high-volume, well-defined workflows, but it does not make building the right answer for every situation. A tool doing genuine multi-system orchestration, real integration engineering, and ongoing maintenance a small business cannot easily replicate still earns its markup regardless of underlying model pricing trends. The point is narrower: for workflows that are essentially a well-tuned prompt plus a template, rising or flat SaaS pricing against falling underlying model costs is a trend worth factoring into the decision, not just a one-time comparison.
What to actually track
Two numbers are worth watching over time for any AI tool your business rents rather than owns: the vendor's per-seat price history, easy enough to track from your own invoices going back a year or two, and the general trend in foundation model pricing for the capability tier the tool likely uses. Neither number needs to be exact. What matters is the direction and the gap between them. A vendor whose price has climbed 15 percent over a year while comparable model pricing has fallen is capturing that entire swing as margin, and that is worth knowing before the next renewal conversation, not after.
Before renewing any AI SaaS contract this year, it is worth asking what the vendor's price has done over the contract's life against what the underlying model costs have done over the same period. If the gap between those two lines is widening in the vendor's favour, that is a build-vs-buy conversation worth having now rather than at the next renewal.
