AI Pricing Is Going Up: Why Today's Cheap LLM Costs Won't Last
Today's AI prices are partly subsidized by investors chasing market share, so as that money tightens, per-token prices and tiers can climb. Build your margins for the expensive future, not the cheap present.
Key takeaways
- Current LLM pricing is partly a land-grab, not a steady-state cost.
- Token prices have fallen fast, but that trend is not guaranteed to continue forever.
- If your unit economics only work at today's prices, you do not have a business, you have a subsidy.
- Usage-based and hybrid pricing protect you better than flat plans when input costs move.
- Model your margins at 2x and 3x today's token price before you commit to a plan.
Why is AI so cheap right now?
Because somebody else is paying for part of it. Frontier model providers are competing for developers and market share, and a lot of capital is absorbing the true cost of compute. Cheap tokens are a customer-acquisition strategy as much as a reflection of real serving cost.
That does not mean prices must spike tomorrow. Hardware and efficiency gains are real and have pushed costs down. But cheap because of efficiency and cheap because of subsidy look identical on your invoice, and only one of them is durable.
Hasn't AI pricing only ever gone down?
Mostly yes, and that is the trap. Per-token prices for many models have dropped sharply over the last couple of years, which trains everyone to assume the line only points one way. The contrarian view: a falling price trend driven partly by competition for share can reverse the moment the priority shifts from growth to profitability.
You do not have to believe prices will double. You only have to accept they might not keep falling, and plan as if your single cheapest provider could get more expensive or disappear.
What happens to your margins if token prices rise?
This is where it gets concrete. Say your product spends $0.50 per active user per month on inference and you charge $15. Comfortable. Now say effective token prices double. Your cost is $1.00, still fine. But if you built a heavy, agentic product at $4 of inference per user on a $15 plan, a 2x move takes you to $8, and your gross margin on that tier collapses.
The point is not the exact multiple. It is that flat-priced, inference-heavy products are the most exposed, and they are exactly the products being launched right now on the assumption that tokens stay cheap.
How do you price for an expensive future?
A few defensive moves. First, prefer usage-based or hybrid pricing for inference-heavy features, so your revenue moves with your cost instead of lagging it. Second, set plan limits that map to a token budget, not a vague unlimited. Third, keep provider optionality so you can route to whatever is cheapest without re-pricing your whole product.
Most importantly, run your model at 2x and 3x today's token price before you lock in a plan. If the business only survives at current prices, you are running on a subsidy you do not control.
The takeaway: treat today's cheap tokens as a promotional rate, not a fixed cost, and price for the version of the market where the discount ends. You can stress-test your margins against higher token prices in Calcaas.
Frequently asked questions
Will AI and LLM prices keep falling?
Maybe, but it is not safe to assume. Recent token-price drops reflect both genuine efficiency gains and competition for market share. The subsidy-driven portion can reverse when providers prioritize profitability over growth.
Why is current AI pricing considered unsustainable?
Because part of it is funded by investors buying market share rather than by the true cost of serving each request. Pricing that depends on outside money tends to normalize upward once that money tightens.
How do I protect my SaaS margins from rising token costs?
Use usage-based or hybrid pricing so revenue tracks cost, tie plan limits to token budgets, and keep more than one provider option. Then stress-test your unit economics at 2x and 3x today's prices.
Which products are most exposed to AI price increases?
Inference-heavy, flat-priced products. An agentic tool spending several dollars per user on a fixed low plan can lose its margin entirely if token prices double, while a light feature on a healthy plan barely notices. Note: place the JSON-LD above inside a <script type="application/ld+json"> tag in the page head.
More from the blog
Pricing StrategyFlat vs Usage-Based AI Pricing: Stop Billing Your Users for Tokens
Per-token billing feels fair, but it hands your customer a cost-modeling problem even you find hard. In most cases, model the token cost yourself and charge a flat price.
LLM EconomicsGPU Cloud Providers in Europe 2026: The Real Cost of Data Residency
European GPU clouds offer B200 and H200 capacity with EU data residency and sovereignty, but residency usually carries a price premium that you should model as part of cost per token, not treat as a free checkbox.
LLM EconomicsCustom AI Chips vs NVIDIA in 2026: What It Means for Your Inference Cost
Hyperscaler custom chips like Trainium, Google TPU, Maia, and Meta MTIA are built to cut the provider's cost of serving AI, but that only lowers your bill if it shows up as a cheaper per-token price or GPU-hour rate.
Pricing math, in your inbox.
One short note a week on AI pricing, token economics, and margin. No spam, unsubscribe anytime.