Flat pricing works really well for traditional SaaS.
You charge a fixed monthly fee, users get access, and things stay predictable for both the company and the customer.
But once AI enters the picture, this model starts to feel… off.
I’ve been thinking about this recently, and the issue seems pretty simple:
AI doesn’t behave like traditional software.
Where the mismatch starts
In most SaaS products, the cost of serving an extra user is relatively low.
Whether someone logs in once a day or ten times a day, the infrastructure cost doesn’t change dramatically.
AI changes that completely.
Every interaction, whether it’s:
- generating text
- processing images
- running predictions
…comes with a real, measurable cost.
And that cost scales with usage.
The problem with flat pricing in AI
At first glance, flat pricing still seems reasonable.
But then you run into situations like this:
Two users are on the same plan.
One runs 100 requests a month.
The other runs 100,000.
From a pricing perspective, they’re identical.
From a cost perspective, they’re not even close.
That creates a few issues:
- Heavy users can become expensive to support
- Light users may feel like they’re overpaying
- Costs and revenue stop aligning
Over time, this becomes difficult to sustain.
It’s not just about cost, it’s also about value
There’s another layer to this.
AI products often deliver value based on how much they’re used.
A team generating thousands of outputs per month is getting significantly more value than someone using the product occasionally.
Flat pricing treats both the same.
Which means:
- Some users get more value than they pay for
- Others pay more than the value they receive
That imbalance shows up quickly as products scale.
Why usage-based pricing feels more natural
This is where usage-based pricing starts to make more sense.
Instead of charging a fixed fee, pricing is tied to actual usage.
That could be:
- API calls
- tokens processed
- compute time
- data handled
Now the relationship is clearer:
More usage → more cost → more revenue
Less usage → lower cost → lower spend
It aligns things in a way flat pricing can’t.
But usage-based pricing isn’t perfect either
It solves the cost alignment problem but introduces new challenges.
For example:
- Costs become less predictable for customers
- Budgeting becomes harder
- Pricing can feel more complex
So while it’s more accurate, it’s not always simpler.
What most AI products seem to be doing
From what I’ve seen, many teams are moving toward hybrid models.
Things like:
- Base plans + usage limits
- Overage charges
- Volume discounts
- Feature-based add-ons
This creates a middle ground:
- Some level of predictability
- But still aligned with usage
The bigger shift
AI isn’t just changing features, it’s changing how software behaves underneath.
And pricing has to adapt to that.
Flat pricing wasn’t designed for systems where:
- cost scales with usage
- value scales with usage
That doesn’t make it wrong.
It just makes it incomplete for AI.
Curious how others are approaching this
If you’re building or working with AI products:
- Are you sticking with flat pricing?
- Moving toward usage-based?
- Or experimenting with hybrid models?
Would be interesting to hear what’s actually working in practice.
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