Made as a Service (MaaS) Will Eat SaaS

AI Made Building Cheaper Than Renting. Now What?

In January 2025, Zylo released its seventh annual SaaS Management Index. The headline number should have sent shockwaves through every boardroom in America: organizations are now wasting an average of $21 million annually on unused SaaS licenses. A 14.2% increase from the prior year.

But that number is the polite version of the story.

Dig deeper into the data and you find something more troubling. The average company uses only about half of the software licenses they’ve purchased. Large enterprises with 10,000+ employees spend $284 million on SaaS annually while using 660 different applications. Two-thirds of IT leaders report unexpected charges due to consumption-based or AI pricing models they didn’t fully understand when they signed the contract.

And we haven’t even talked about the integration tax yet.

When a CFO asked me recently why her engineering budget seemed to be disappearing into “connective tissue,” I had to explain what most CTOs already know but rarely say out loud: the middleware, the consultants, the custom pipelines stitching together systems that were never designed to talk to each other. These costs often exceed the subscription fees themselves.

For two decades, we accepted this trade. SaaS promised lower friction in exchange for compromise. We rented generic tools, adapted our workflows to vendor assumptions, and accumulated operational overhead in the form of integrations, administrators, and compliance scaffolding.

That trade no longer reflects the underlying economics.

The Rental Model We Never Questioned

SaaS solved a 1990s problem: how to distribute complex software when infrastructure, deployment, and maintenance were prohibitively expensive. It succeeded by centralizing ownership and standardizing behavior.

The model made sense when custom software meant six-figure development projects, 18-month timelines, and the constant dread of maintenance. Building your own tools was a rich company’s game. Everyone else rented.

But here’s what’s strange. We never stopped to ask whether the rental model still made sense once the underlying economics shifted.

Consider what’s actually happening inside most scaling organizations. According to Grip Security’s 2025 SaaS Security Risks Report, SaaS applications per employee have steadily risen, marking an 85% increase in the number of accounts per user. The average employee now juggles 13 different SaaS tools in their daily work.

Each of those tools comes with its own login, its own data model, its own API quirks. Each creates a new integration point. Each adds to the cognitive load of your teams. And each one guards its own silo of company data, preventing your AI systems from seeing the full context of your business.

The SaaS landscape has consolidated into what I call concentric circles of dependency. At the center sit platform giants: Salesforce, Microsoft, Google, Oracle, SAP. Their gravity pulls in smaller vendors who build integrations, extensions, and complementary tools. Around them orbit the integration layer: the middleware providers, the consultancies, the “implementation partners” who make their living connecting systems that don’t want to be connected.

Customers orbit furthest from the center, subject to forces they don’t control. Pricing changes ripple outward without negotiation. Feature deprecation happens on vendor timelines. Data portability remains theoretical. Switching costs compound over time.

The Math Has Inverted

Something fundamental shifted in 2024, and most CTOs haven’t fully processed it yet.

AI-assisted development didn’t eliminate the need for engineering judgment. It did reduce the cost of execution. Translation from intent to code is faster. Iteration cycles are shorter. Large codebases are more legible to machines than to humans. Routine refactoring and dependency management can be automated.

According to McKinsey’s State of AI 2024 report, 65% of organizations now regularly use generative AI in at least one business function, nearly double the percentage from the previous year. And software engineering is one of the top functions where organizations are applying these tools. Research from Microsoft, MIT, and Wharton showed developers using AI coding assistants achieved a 26% increase in productivity. GitHub’s own studies found developers completed tasks 55% faster with AI assistance.

What does this mean in practical terms? The breakeven calculation for custom software has collapsed.

Consider a typical scenario. A team of 50 using a specialized SaaS tool at $50 per seat per month pays $30,000 annually for the privilege of adapting their workflows to someone else’s product decisions. That’s before integration costs, before the consulting fees to customize it, before the productivity losses from features that don’t quite fit.

With AI-assisted development, a purpose-built alternative can now be synthesized in days, not months. The ongoing maintenance that once required dedicated engineering teams can be largely automated through continuous testing, dependency monitoring, and AI-assisted refactoring.

The historical objection, “custom software rots,” assumed maintenance was manual. It assumed you needed humans who understood the system to patch vulnerabilities and update dependencies. That assumption has dissolved.

What I’m Calling Made as a Service

I’ve started using the term “Made as a Service” to describe what’s emerging. MaaS replaces software rental with software stewardship. Customers subscribe not to a static product, but to an ongoing capability: software continuously synthesized, governed, and evolved to match their exact workflows, deployed into infrastructure they control, with full ownership of data and logic.

This isn’t the old “bespoke development” model rebranded. The difference lies in three critical areas.

First, shared foundations. Common logic like authentication, billing, compliance, and audit trails is reused across clients. The wheel isn’t reinvented.

Second, unique orchestration. Workflow logic and interfaces are custom, not templated. The surface is shaped to fit.

Third, continuous stewardship. Evolution is included, not renegotiated. The relationship persists.

Consider one example documented by a Swiss digital consulting firm: an industrial group with 500 users across three subsidiaries opted for a custom solution to centralize quality processes. The initial project cost 600,000 CHF in capital expenditure, followed by 40,000 CHF annually for maintenance. The SaaS alternative would have billed 120 CHF per user per month, totaling nearly 2,160,000 CHF over five years. Beyond the financial gain (total cost of ownership reduced by 70%), the group integrated its own continuous analysis algorithms, boosting quality performance by 15%.

This pattern is emerging across industries. One software development firm reports that a logistics client started with a well-known SaaS tracking tool. After 18 months, they had spent over $40,000 and were still manually exporting data to patch things together. A custom dashboard paid for itself in 14 months and continues to scale with their needs without a rising monthly bill.

The Security Argument Flips

One of the strongest objections to custom software has always been security. “Shared SaaS platforms have dedicated security teams. You can’t match that.”

The argument made sense until the supply chain attacks started.

Shared SaaS platforms create shared blast radius. Every supply chain attack on a major vendor reminds us: millions of customers exposed simultaneously. The Log4j vulnerability. The SolarWinds compromise. The endless parade of breaches at companies whose entire value proposition was handling data securely.

MaaS isolates risk. Smaller attack surfaces. No cross-tenant exposure. Auditable code paths. Explicit threat models. Security improves when responsibility is explicit, not abstracted.

This matters enormously for regulated industries. Healthcare, finance, and government organizations often find compliance is easier to achieve with software designed for specific regulatory requirements than with generic platforms jury-rigged for regulated environments.

Where SaaS Still Wins

I want to be clear about something. SaaS will not disappear. Its domain contracts to areas where structural advantages remain.

Commodity utilities like email, basic document editing, and video conferencing are genuinely universal with no competitive differentiation. Buy these.

Network-effect platforms like LinkedIn, Slack (for cross-company communication), and marketplaces derive their value from the people, not the code. The value is in the network.

Heavy compute applications requiring massive proprietary infrastructure belong in SaaS. Video rendering, large-scale simulation, frontier AI training. These demand infrastructure investment that makes no sense to replicate.

Everything else is vulnerable.

The question to ask yourself: Does this software create competitive differentiation, or is it a commodity? If your company’s secret sauce lives in how you do things differently, forcing those processes into generic SaaS means diluting the very thing that makes you valuable.

The Path Forward

If you’re a CTO looking at this shift, the question isn’t whether to abandon SaaS entirely. That would be foolish. The question is where to start reclaiming sovereignty.

Look at your highest-friction SaaS tools first. The ones requiring the most customization, the most integration work, the most workarounds. Calculate the true total cost of ownership over five years: subscription fees plus integration plus administration plus training plus the productivity losses from workflows that don’t quite fit.

Then ask: could a purpose-built alternative be synthesized for less than this total cost? With AI-assisted development timelines, the answer is increasingly yes for any domain where workflows materially differ across organizations, competitive advantage lives in process, data context matters for AI systems, or integration overhead dominates subscription cost.

The shift from renting software to owning it is not ideological. It is economical. The math has changed. The only question is how long before your organization catches up.

I’ve written a fuller exploration of this transition in what I’m calling “The MaaS Manifesto.” If you want to go deeper on the economics, the technical architecture, and the strategic implications for your organization, you can find it at etiennex.com.

The era of rental is giving way to the era of stewardship. SaaS was software for everyone. MaaS is software made for you.

The transition has begun.