Why I Think Most AI Tools Don’t Deserve to Be SaaS

Almost every AI founder today follows the same default path:

Build a tool.
Add subscriptions.
Call it SaaS.

But after closely studying dozens of AI products, user behaviors, cost structures, and real-world adoption patterns, I’ve come to a strong conclusion:

Most AI tools do NOT deserve to be SaaS.

Not because they’re bad tools.
But because the SaaS model itself is often misaligned with how AI actually delivers value.

Let me explain why this matters, and why blindly forcing AI into a SaaS box is hurting both founders and users.

1. SaaS Was Built for Predictable Software, Not Probabilistic Intelligence

Traditional SaaS works beautifully when:

outputs are deterministic

• workflows are fixed
• costs are stable
• usage is consistent
• behavior is predictable

AI violates every one of these assumptions.

AI systems are:

• probabilistic
• context-dependent
• usage-spiky
• compute-expensive
• variable in output quality
• unpredictable in cost

Yet founders still try to lock them into:

• flat monthly plans
• rigid tiers
• unlimited usage promises

This creates instant economic tension between:

• what the user expects and what the system can sustainably deliver

2. Most AI Tools Don’t Deliver Daily, Habit-Level Value

SaaS works best when a product becomes a daily habit:

• email
• CRM
• project management
• accounting
• team communication

But many AI tools are:

• used once a week
• used only for specific tasks
• used episodically
• used during special workflows
• used during “bursts” of activity

Forcing these tools into monthly subscriptions creates:

• unused capacity
• churn
• pricing frustration
• perceived low value
• “why am I still paying?” thinking

If a tool is not habit-forming, it probably doesn’t deserve a SaaS model.

3. Variable Compute Cost Breaks the SaaS Promise

Classic SaaS benefits from:

• near-zero marginal cost per user
• predictable infrastructure expenses
• scaling efficiency

Most AI tools have:

• real-time inference costs
• token-based billing
• GPU dependency
• expensive multimodal pipelines
• volatile usage patterns

Which means:

More users ≠ more profit
More users often = more loss

This flips SaaS economics upside down.

Founders then start:

• rate limiting aggressively
• reducing quality silently
• cutting context
• throttling responses
• removing features

And users immediately feel the degradation.

4. Many AI Tools Are Actually “Outcomes”, Not “Platforms”

SaaS works best when users manage:

• workflows
• data
• multiple tasks
• long-term processes

But many AI tools deliver:

• one output
• one transformation
• one result
• one analysis
• one generation

These are not platforms.
They are outcome engines.

Outcome engines are better suited for:

• usage-based pricing
• credit-based systems
• pay-per-result
• performance-based billing

Not monthly subscriptions.

5. AI Tools Evolve Too Fast for Static Pricing Tiers

SaaS pricing assumes:

• slow product evolution
• clear feature boundaries
• stable versions

AI evolves monthly. Sometimes weekly.

Models upgrade.
Capabilities explode.
Costs fluctuate.
Expectations shift.

This makes fixed SaaS tiers:

• outdated quickly
• misaligned with reality
• hard to justify
• difficult to manage
• confusing to users

Users don’t want to pay for “tiers.” They want to pay for value delivered now.

6. Most AI Tools Should Be Utilities, Not Subscriptions

Many successful AI products should behave like:

• cloud compute
• storage
• APIs
• power usage

You pay when you use them, not because they simply exist.

This aligns:

• cost with value
• usage with payment
• growth with sustainability
• user satisfaction with transparency

Subscriptions hide usage reality. Utilities reveal it.

7. SaaS Forces Founders to Chase Retention Instead of Outcomes

When founders lock into SaaS thinking, they obsess over:

• churn
• retention curves
• engagement dashboards
• monthly active users
• usage spikes

Instead of obsessing over:

• did the user actually win?
• did the tool truly save time?
• did it reduce headcount?
• did it improve decisions?
• did it automate something painful?

AI should be judged by outcomes, not logins.

8. The Best AI Businesses Will Blend Multiple Revenue Models

The future belongs to hybrid models:

• usage-based + subscriptions
• enterprise licensing + credits
• API-first + outcome pricing
• automation-as-a-service
• intelligence-as-a-utility
• performance-based automation

Pure SaaS will survive. But it will not dominate AI.

Here’s My Take

SaaS is not wrong.
But it is not universal for AI.

AI tools are:

• dynamic
• outcome-driven
• usage-variable
• compute-dependent
• continuously evolving

Trying to squeeze them into a rigid SaaS box creates:

• broken economics
• frustrated users
• hidden throttling
• artificial limits
• forced retention games

Most AI tools don’t need subscribers. They need users who pay for value when value is delivered.

That’s not SaaS. That’s the next-generation AI business model.

And the founders who understand this early will build far more sustainable companies than those blindly copying the old SaaS playbook.

Next article:

“What I’d Do Differently if I Were Building an AI Tool Today.”