The Inference Economics Revolution: How AI Pricing Is Collapsing
The numbers are startling. In roughly 21 months, the price for equivalent-model token inference dropped from around $37 per million tokens to approximately $0.25.
That's a 150X reduction. And the trend shows no signs of stopping.
What Happened
For years, AI inference seemed expensive. Every API call cost money. Scaling meant spending more on compute.
Then pricing collapsed. Multiple factors drove this:
Competition intensified. More providers entered the market, driving prices down
Efficiency improved. Better hardware, optimization techniques, and model distillation reduced costs
Scale benefits kicked in. Larger providers spread fixed costs across more users
Why It Matters for Products
Lower inference costs change what's economically viable:
More calls per task. What once cost too much now fits budgets. Products can make more API calls for better results.
New use cases become possible. Interactions too expensive last year are affordable now.
Margins improve. For AI product companies, falling input costs directly improve unit economics.
The Speed of Change
Here's what makes this unusual: the pace of change.
Traditional software economics evolve over years. Infrastructure costs might decline 10-20% annually in stable markets.
AI inference dropped 99%+ in less than two years. This compresses what would normally be a decade of progress into months.
Implications for Strategy
Building AI products requires different thinking when input costs change rapidly:
Don't anchor on current pricing. Plans based on today's costs may be obsolete quickly
Design for efficiency. Low-cost environments reward efficient prompting and smart caching
Watch the trend. Pricing may continue falling; factor that into projections
The Winner Perspective
Some companies benefit more than others:
High-volume users see the biggest savings. Companies making millions of API calls save significant sums.
Product companies gain flexibility. Lower costs enable experiments that weren't feasible before.
End users may never see prices drop directly. But better products result from improved economics.
What's Next
The trend suggests continued decline, but with some constraints:
Hardware limits. Eventually, you run into physical limits of compute efficiency
Margin pressure. Providers can't drop prices forever without sacrificing quality
Value shift. As inference commoditizes, value may move to models, integration, or user experience
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