arbeiten-mit-ki ki-modelle token-budget llm-pricing

Usage-based instead of flat-rate: The AI cost turn was predictable — and here is how to absorb it

Martin Rau · 16. Mai 2026 · 10 Min. Lesezeit

It was clear all along that this day would come. The flat-rate plans from the major AI providers — €20 here, €200 there, unlimited AI for everyone — were a customer-acquisition lever from the start, not a sustainable business model. They opened the market, got millions of people used to working with AI, let real workflows grow on top of them. And now that we are all inside, the providers are flipping the switch: toward metered billing. Anthropic moved first with Extra Usage and volume tiers, OpenAI has openly said that “unlimited” cannot stay, Cursor switched from request-based to token credits. If you work with AI seriously, you should brace for this — and ideally have done your homework long before.

The core idea in one sentence

Anyone who has treated AI as a flat-rate buffet will pay for it in the coming months — anyone who built in token awareness, model routing and clean architecture will barely feel the shift.

Why this had to happen

Providers do not make money on the flat-rate plans. Anthropic CEO Dario Amodei and OpenAI’s Nick Turley now say it openly: an unlimited flat rate on compute is like an unlimited electricity plan — it only works as long as nobody really turns the tap. The moment users start running agents, doing long coding sessions, or working hours on end with Claude Code or Cursor, consumption explodes. What used to be “a few thousand tokens per chat” becomes “millions of tokens per task”.

The painful part for providers: those power users are the ones who use AI seriously — and the same ones who break the flat-rate model fastest. My own desk is a good case in point. Over the past weeks I have run the test repeatedly: a larger refactor run once through the API hits 5 € in consumption easily — and that is a small task. Real work — architecture, research, implementation, reviews — burns 500,000 to a million tokens per session. Per session. Multiply that across a working day and the picture is obvious. This is not a 20-€ subscription anymore. This is an electricity bill.

And meanwhile, millions of people have wired exactly this into their processes — and budgeted against the flat-rate plan. Workflows, tools, entire agencies are built on the assumption that the machine just runs in the background. When the switch flips, that hits margins directly.

What is actually changing right now

Three movements are happening in parallel, and they reinforce each other:

1. Flat rates get hollowed out without raising the headline price. Anthropic showed how in April: Opus 4.6 quietly disappeared from the picker, Fast Mode now runs only through the separate Extra Usage budget, and the new Opus 4.7 tokenizer emits up to 35 % more tokens for the same text. The list prices stay where they were — but your plan quota drains faster. That is a price hike through the back door.

2. Volume tiers and pay-per-token become the norm. Anthropic introduced staggered API discounts on May 1, 2026 — 15 % off above $50,000 monthly spend. Sounds nice, but the real signal is: if you scale, you get rewarded. If you sit at small volumes, you pay full price. Cursor shifted from request-based to token credits earlier this spring — same logic.

3. OpenAI is preparing the end of “unlimited”. Nick Turley, head of ChatGPT, said openly on a podcast: “In a world where technology changes this fast, there is no world where the pricing system does not change dramatically.” Sam Altman talks about selling AI “like electricity, on a meter”. This is no longer a fringe view — this is the official roadmap.

Put together, the picture is clear: providers are pulling the brake. Not loudly, not overnight, but in small steps. A toggle here, a tiered discount there, a new tokenizer, a limit that bites earlier. Until usage-based is the default.

My own wake-up moment

I remember the first session where I ran a task through the API that I would normally have just thrown into the chat — without thinking about whether I really needed it. A small experiment, started out of curiosity. Within minutes, the dashboard read $5 in consumption. Five dollars. For a task I would not even have noticed inside the subscription.

That was the moment when it hit me: what we all do all day has a real price. We just do not see it because the subscription swallows it. The moment you start running real tasks — bigger refactors, long research, multi-step reasoning chains — 500,000 tokens is nothing unusual. A million either. With Opus 4.7 at $5 / MTok input and $25 / MTok output, that adds up to double-digit euro amounts per session quickly. Per session.

And these are exactly the tasks we have wired into our workflows. The shock therefore does not come from moving from the €20 plan to the €200 plan. It comes from moving from “I pay for compute” away from flat-rate altogether — when every session has a visible cost.

Why this does not throw me — and why it does throw many others

I have been working toward this scenario for months. Not because I am a fortune teller, but because the cost curve in the API dashboard does not lie. If you work directly against the API regularly, you see the true price immediately — and you adjust your architecture.

Three levers run in parallel for me, and they make the difference between “this hurts a lot” and “this barely matters”:

1. Model routing by task, not by habit

Architecture and planning belong on the big model — that is the spot where bad decisions poison the rest of the workflow. Skimping on the model here makes you pay double later. But: once the plan is in place, almost everything else can be handled by a smaller, cheaper model. Sonnet instead of Opus, sometimes even Haiku. With a clean plan, the small model often does surprisingly good work — because it does not have to think, it has to execute.

In practice: one session with Opus for the heavy planning, then Sonnet for 80 % of the follow-up work. That drops token costs by a factor of 5 to 10 — at the same outcome. This is not a theoretical tip; this is the standard path I run every day.

2. KIDOKU and RAG: less searching, less reading-in

The second big lever: tokens that never get burned in the first place. A typical AI coding workflow spends shockingly many tokens just digging around the repository — reading files, scanning them, re-reading them, because the last answer no longer holds the context. KIDOKU (my MCP-based knowledge layer) and targeted RAG flip the ratio: instead of letting the model search, it gets the right snippets directly. Instead of 50,000 tokens spent on research, it is maybe 3,000.

Across a full session, this is often a larger lever than model routing. Together, the two cut real token consumption dramatically, without the output suffering visibly.

3. Frugality discipline in the prompt itself

The third lever is the smallest, but mentally the most important: teach the model to answer tersely. “Do not explain what you are about to do — do it.” “Answer in bullets, not in prose.” “Keep text between tool calls to two sentences.” Sounds banal, saves enormous output tokens in aggregate — and those cost five times what input tokens cost on Opus 4.7.

I will say this clearly: this discipline hurts if you are not used to it. Most users never questioned the comfort of the flat-rate plan because they did not have to. Once every answer carries a visible price, this exact discipline becomes a core skill.

Three levers that hold up in metered billing

Model routing: Big model for planning only, small model for execution — factor 5–10 savings at the same outcome.
Context preparation (RAG / KIDOKU): Less searching means fewer input tokens. Often the single biggest lever.
Output discipline: Enforce terseness in the system prompt — output tokens are 5× more expensive than input.

What you should do right now

If you are still working exclusively on flat-rate plans and have no real sense of your own token consumption, use the next few weeks — before the shift hits your own workflows.

1. Set up an API account and let it do three days of “real” work. That forces you to look at the dashboard. You do not need to migrate anything yet, you just need to understand once what your standard tasks actually cost. These numbers are uncomfortable — and they are the basis for every sensible decision after.

2. Name your most expensive workflow. There is likely one task you run daily or weekly that takes much longer than everything else — a refactor, a research routine, a content briefing. That is the one to make “cost-stable” first. Split the model, shorten the context, keep output terse.

3. Stop running “Opus for everything”. That is the most expensive habit we collectively share. Sonnet is more than enough for 70 % of tasks, costs a fraction, and is often faster. If you cannot break the reflex “big model = safe outcome”, you will pay triple once usage-based kicks in.

4. Automate workflows instead of repeating them manually. Any task you push through the chat five times a week is a candidate for automation — with a fixed model, fixed context, and a negotiated terse output. That is where the real lever sits long term.

The good side of the shift

I know this sounds gloomy at first — and for people who have used AI without reflection, it will be. But there is a second side: metered billing makes quality visible. A sloppy question, a model that is too big, a messy context — those carry a price tag now. And price is the sharpest feedback signal a market has.

If you work token-aware, in the new model you will pay less, not more, than someone who simply burns €200 a month flat. And the workflows that are well built today actually become more economically attractive under metered billing, because they have a hard cost floor instead of a fixed monthly fee that you cannot fully use.

That is exactly the line we work on at boostN: architecture that saves tokens. Routing that picks models by fit. Tools like KIDOKU that take the search burden off the model. If the market is catching up now, it is not the end of the party — it is the start of serious work with AI.

News on the market move

The matching news piece with hard facts, quotes and dates around this industry shift is here: → AI providers move toward usage-based pricing — what is shifting right now

Entdecke mehr

Blog

Themenuebersicht

Arbeiten mit KI Token-Budget KI-Modelle LLM-Pricing