SaaS: The Build vs. Buy Equation Just Changed
How AI Rewrites Software Economics, and What It Means for Your SaaS Expenses
Here is a story we keep hearing.
A client of ours has been using a forms application for about 15 years. When they started, it cost $10 per user per month for 10 users, so a total of $100 per month which was very reasonable.
Today, the same application costs $89 per user per month across 50 users. That is $53,400 a year. The functionality has barely changed. There are no new integrations. The workflow logic is the same as it was a decade ago. They are paying 45 times more for essentially the same product.
This is not an unusual situation. Whether you are paying for 10 applications or 500, you have probably noticed the pattern: SaaS costs climbing steadily while the value delivered may often remain relatively flat. Annual price increases that arrive with a note about "enhanced features" you never asked for and may never use.
For a long time, there was no practical alternative. Building custom software was expensive, slow, and risky. The build vs. buy equation clearly favoured buying.
That equation has changed.
Why building just got cheaper
AI has fundamentally altered the economics of software development. Not in the theoretical, futuristic sense that vendors have been promising for years, but in a concrete, measurable way that is already affecting real projects.
The shift centres on one phase of the development lifecycle: implementation.
Whether you are buying SaaS or building custom, the first two phases look similar. You still need to gather requirements. You still need technical design. These take roughly the same time and cost regardless of approach.
But implementation - the phase that traditionally consumed 50 to 70 percent of total project cost - has compressed significantly.
Traditional SaaS implementation involves months of drag-and-drop configuration, multiple consultants, integration development, and project management overhead that can consume 20 to 30 percent of total effort on its own. Even for a single application, that could mean 6 to 18 months and $50,000 to $500,000.
AI-enabled development takes days to weeks. One or two developers instead of teams. Minimal coordination overhead. The same medium-complexity system can be delivered in 2 to 8 weeks and for much less effort, which directly reduces the cost.
50 to 90 percent reductions in implementation cost have been spoken about recently and the threat of AI facing SaaS providers. And unlike most technology claims, these are numbers we have tested ourselves and are confident they are realistic in quite a few cases.
We built our own CRM in 10 days
When Hypergen needed a CRM and practice management system, we evaluated the major vendors. The core CRM functionality - contacts, account management, opportunity tracking - was well covered out of the box. But our business requires project management, timesheets, and budget tracking as well. That required extensive customisation. We would still have needed Xero integration for invoicing and workflow automation to generate weekly project updates. A basic implementation would have taken 6 weeks. Matching our actual requirements would have taken 3 to 6 months or more, with material ongoing licensing costs and extensive labour for any future enhancements.
This is a pattern we see in many organisations. The SaaS product looks like a good fit during evaluation, but once implementation begins, the tweaking required to match how the business actually operates makes it uneconomic. Every organisation is a little different, and those differences add up.
Instead, we built Hypergen OS - a custom system covering sales CRM, project tracking, timesheets, budget management, dashboards, analytics, Xero integration for invoicing, SharePoint integration for document management, and automated weekly project reporting. The initial version was delivered in 10 days - already more capable than what we would have achieved in 3 to 6 months with a SaaS product. We anticipate that within 12 weeks we will have a comprehensive and mature Hypergen OS running internally.
We built it with full control over every layer: the database, application logic, logging and alerting, analytics dashboards, and the front end. Everything sits behind our own firewalls and security controls. We have not sacrificed any level of enterprise-grade security by taking this approach.
Ongoing licensing cost: zero. When we want a new feature, we build it in days rather than submitting a feature request and waiting 6 to 12 months.
This worked because our CRM requirements hit a specific profile. The core workflows were well understood. The integrations were straightforward. We were replacing a known process, not inventing a new one. And the cost of the traditional approach was high relative to what we were actually getting.
Not every system fits this profile. Knowing which ones do is the point.
Hypergen's Four-Factor Framework for SaaS Replacement
Not all software is equally suited to AI-enabled replacement. We have developed a framework to help organisations identify where the real opportunities are. The best candidates for displacement share four characteristics.
Factor 1: High cost relative to value
The clearest signal is a growing gap between what you pay and what you get. Annual costs have increased faster than the platform's utility to your organisation. You have absorbed price increases that came without meaningful new capability. The question "are we really getting what we pay for?" keeps surfacing in budget reviews.
This factor alone does not justify replacement. But it creates the financial motivation to look seriously at alternatives.
Factor 2: Low feature utilisation
You are using a small fraction of the platform's capabilities. The vendor's feature list runs to dozens of pages, but your team touches perhaps 10 to 15 percent of it. Worse, the platform requires heavy customisation just to fit your actual workflow, and the backlog of configuration work never seems to shrink.
This matters because you are effectively subsidising features built for other customers. When you only need a small slice of what a platform offers, building that slice becomes viable in a way it was not before.
Factor 3: Weak integration dependencies
The system operates largely as a standalone tool. It does not have deep, complex connections to other critical business systems. Data flows in and out manually or through basic APIs. Nobody loses sleep about what would break if this system were replaced.
Integration complexity is the single biggest factor that makes replacement difficult. When a system is tightly woven into your operational fabric, extracting it is expensive and risky regardless of the replacement technology. Simple integration profiles remove that barrier.
Factor 4: Simple workflow logic
The system's primary value is orchestration - routing approvals, sending notifications, tracking status, managing basic records. It is not performing complex analytical processing, handling regulated transactions, or running business logic that has been refined through years of edge-case discovery.
Simple workflow logic means the requirements are well understood and can be specified clearly. This is exactly the type of work where AI-enabled development excels.
The sweet spot
Systems scoring high on all four factors represent your best displacement opportunities. The forms application we described earlier is a textbook example: high and rising cost, minimal feature utilisation, no integrations, and straightforward workflow logic.
Start here. Not with your ERP. Not with your deeply integrated CRM. Not with your accounting platform.
Where to start: a bottom-up approach
The instinct when faced with a new capability is to aim high. Resist it.
The most effective approach is to start at the bottom of your software portfolio - the tools that nobody considers strategic but everyone pays for. In a large organisation, there may be dozens of these. In a smaller one, even two or three successful replacements can materially change your cost base.
Start with:
Non-core standalone applications
High-cost, low-utilisation tools
Simple workflow orchestration systems
Systems that have had recent price increases
Tools where the team frequently complains about limitations
Do not start with:
Core ERP systems
Your primary CRM (if it is well-utilised and integrated)
Mission-critical transactional platforms
Heavily integrated systems with complex dependencies
This bottom-up approach works for three reasons.
Lower risk. If something goes wrong with a non-core system replacement, it does not break critical operations. You can learn and iterate without material business exposure.
Quick wins. Replacing a $53,000-per-year forms application with a purpose-built solution that pays back in under 12 months produces a visible result that finance understands. These wins build organisational confidence for larger projects.
Compounding benefits. Multiple small wins across your software portfolio add up. For an organisation with hundreds of applications, the aggregate savings can be substantial. For a smaller organisation, the freed budget enables investment in areas that were previously out of reach. Either way, each successful project builds internal capability for the next one.
The economics keep improving after launch
The cost advantage does not end at initial deployment. It compounds every time you need to change something.
With a traditional SaaS platform, enhancements follow one of two paths. You can submit a feature request to the vendor's backlog and wait 6 to 12 months, hoping it aligns with their roadmap. Or you can hire a developer to configure the change, which could take 6 to 8 weeks and cost enough that the business case often does not stack up. In practice, many enhancements simply never happen because the build cost exceeds the return. Most organisations have a long list of good ideas - each one valuable, but none with a large enough individual ROI to justify the consultant spend. The result is a system that stops evolving because the cost of change is too high.
With an AI-enabled custom solution, the design work remains the same - you still need to define the requirement and design the approach. That does not change. What changes is the build cost. AI generates the code and tests it, effectively removing implementation cost from the equation. Because that barrier is gone, you can act on ideas that were previously not worth pursuing. You can actually innovate on the system rather than maintaining a backlog of improvements you will never get to.
Because these loops are tighter, you can go smaller and quicker. Instead of batching changes into multi-week agile sprints, you make smaller, more frequent improvements that provide a faster feedback loop. This simplifies and accelerates progress in a way that traditional SaaS enhancement cycles cannot match. This workflow reflects a broader shift in development methodology - from iterative sprints to short spec driven development cycles, where clear specifications drive AI-powered implementation.
Organisations that want to make enhancements to their systems - and most do - see the ROI advantage widen with every enhancement cycle. You control the roadmap. You set the priorities. You move at the speed of your business, not the speed of your vendor's product team.
The abstraction evolution: why this is happening now
To understand why the economics shifted, it helps to see the broader pattern.
Software development has gone through two major abstraction shifts. The first was low-code: platforms like Power Apps, Salesforce, and ServiceNow abstracted away raw coding into drag-and-drop visual configuration. This was genuinely valuable. Configuration was faster than hand-coding, and it opened development to a broader set of people.
The second shift is happening now. AI abstracts code through natural language instead of drag-and-drop interfaces. You describe what you need in plain language. The AI implements it.
This matters because natural language removes the two biggest costs of the low-code era: learning the platform's configuration model, and the ongoing consultant hours required to maintain and extend it. With AI-enabled development, you do not need to understand how a specific vendor's configuration system works. You describe the outcome, and the code is generated.
Low-code platforms are not obsolete - they still make sense for certain use cases, particularly when deep platform ecosystem integration is required. But for standalone systems with straightforward requirements, AI-enabled development is now faster, cheaper, and more flexible than configuring a SaaS platform.
Where software remains defensible: do not waste effort here
Not everything is ripe for replacement, and knowing what to leave alone is as important as knowing what to target. Some systems have genuine defensibility that makes displacement impractical regardless of how cheap AI development becomes.
Deep domain expertise
Accounting platforms like Xero are a good example. The surface-level functionality - recording transactions, generating reports - looks simple enough to replicate. But beneath it sits years of accumulated domain knowledge: financial reporting standards (AASB, IFRS), tax calculation accuracy across jurisdictions, BAS statements and ATO reporting requirements, and thousands of edge cases discovered and handled over time.
Getting accounting wrong carries legal and financial consequences. The cost of errors in this domain exceeds any licensing savings you might capture. Leave these systems alone until you have a specific reason and deep confidence.
Network effects
Xero also illustrates network defensibility. Accounting firms access their clients' books directly through the platform, and Xero has built such a deep foothold with accounting firms that most firms run their own practice management and workflow tools through Xero's ecosystem. They are not keen to move, and they do not want their clients to move either.
The result is that many businesses stay with Xero not because of the product's value to them directly, but because switching would complicate the workflow with their accountant. Even when the software itself is not delivering what the business wants, the relationship with the accounting firm keeps them tied in. That network effect is genuinely difficult to displace.
Before targeting any system for replacement, ask: who else depends on this? What breaks if we move?
Critical integration partnerships
Some integrations cannot be replicated with code alone. Bank feed integrations require formal bank partnerships and security clearances that take months or years to establish. Employment Hero's VEVO integration connects directly to the Department of Home Affairs for employment eligibility verification - that requires government authorization, not just an API call.
Superannuation fund connections, payment processor relationships, and government verification systems all create defensibility that is rooted in partnerships, not technology. If your current platform has these, replacing it means replicating those relationships, not just the software.
Platform ecosystem lock-in
Core ERP systems like SAP, Oracle, and Microsoft Dynamics are connected to everything. Inventory talks to the general ledger, which talks to warehouse management, which connects to procurement. Supplier EDI connections, customer order management, and logistics platforms all depend on the same central system.
The platform itself matters less than the ecosystem that has been built around it over years. These are "later" projects. Build your capability and confidence on standalone systems first. Tackle the interconnected platforms once you truly understand the economics and have proven the approach.
A message for SaaS vendors: how to protect your position
If you are reading this from the other side of the equation - as a product leader, CTO, or executive at a software company - the four-factor framework should give you a clear view of where you are exposed.
If your customers are using 10 percent of your platform, paying premium prices for it, operating it as a standalone tool, and primarily using it for simple workflow orchestration, they are actively looking for alternatives. You may not know it yet, but the spreadsheet comparing your annual cost to a custom build estimate already exists somewhere in their organisation.
The good news: defensibility can be built deliberately. Here are six strategies worth considering.
1. Deepen your domain expertise
If you operate in a regulated or high-stakes domain, your compliance knowledge and risk mitigation represent genuine defensibility. Financial reporting accuracy, industry-specific regulatory requirements, and the accumulated edge-case handling that comes from years of domain focus are not easily replicated.
Emphasise this value. Make it visible to your customers. The risk of getting a compliance-critical process wrong should be part of every conversation about total cost of ownership.
2. Build network effects into your product
Products that serve multiple parties in a transaction or workflow create natural switching friction. When one party considers leaving, the impact on connected parties creates resistance. Xero's accountant-client model is the clearest example, but the principle applies broadly.
Look for opportunities to make your product the shared workspace between your customer and their partners, suppliers, or clients. Multi-party value is harder to displace than single-party value.
3. Invest in integration partnerships that cannot be replicated with code
Bank feed connections, government system integrations, payment processor relationships, and industry platform partnerships create defensibility precisely because they require formal agreements, security clearances, and regulatory approval. These take months or years to establish and cannot be shortcut by a developer with an AI coding assistant.
Prioritise integrations that require partnerships over those that only require APIs. The distinction is critical: an API can be called by anyone, but a formal partnership is exclusive.
4. Drive platform adoption, not just product adoption
A Salesforce customer using basic contact management is vulnerable. A Salesforce customer using Marketing Cloud, Service Cloud, self-service portals, and custom applications built on the platform is not.
The more components of your platform a customer uses, the higher their switching costs. Consider pricing strategies that reward deeper platform adoption rather than penalising it.
5. Evolve beyond low-code configuration
This is the strategic response that requires the most courage, but offers the highest payoff.
Traditional low-code platforms require customers to learn your configuration model, hire consultants to maintain it, and accept ongoing maintenance overhead. This creates exit motivation when AI offers a simpler path.
Microsoft Power Platform is already moving in the right direction - incorporating natural language development capabilities that let users describe what they need rather than configure it through visual interfaces.
If you offer a low-code platform, evolve to natural language development. By reducing your customer's maintenance burden, you reduce their motivation to leave. This is defensive AI adoption: using AI to protect your business model rather than letting it disrupt you.
6. Rethink your channel partner economics
This is the implication most vendors are not yet grappling with. If implementation effort drops 50 to 90 percent with AI-enabled development, your channel partners' revenue model collapses. Their business is built on implementation hours. Fewer hours means less revenue, which means less motivation to sell and support your platform.
You need new compensation models before your partners start recommending custom-built alternatives to your product. This is not a future problem - it is a current one.
How to take action
If the four-factor framework resonates, here is a practical path forward.
This week: Pick one non-critical system in your software portfolio. Score it against the four factors. Be honest about what you are actually using versus what you are paying for.
This month: For any system that scores high on all four factors, estimate the annual cost and compare it to a rough build estimate. If the payback period is under 12 months, you have a candidate worth investigating.
This quarter: Run a pilot. Replace one standalone, high-cost, low-utilisation system with a purpose-built solution. Measure the results - not just the cost savings, but the speed of delivery and the team's ability to enhance the system after launch.
The organisations that will benefit most from this shift - whether they run 10 applications or 500 - are the ones that start now, start small, and build capability through practical experience. The framework is simple. The economics are clear. The question is whether you will act on them.
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