
What happened
Postman's Akihiko Kusanagi writes that LLM token prices fell roughly 8割 between 2025 and 2026, yet AI bills rose as spending on LLM APIs topped 84億ドル in 2025.
Why it matters
Per Anthropic research cited in the piece, agentic workflows consume about 4x the tokens of chat and multi-agent setups about 15x, so cheaper units do not guarantee smaller invoices.
What to watch
Kusanagi says the fix is governance and API design, not price comparison — and points to a next installment with five ways to curb token consumption.
WHO IT HITSThis lands hardest on platform and API teams that expose internal services to AI agents, plus the finance and IT managers who now track AI spend as its own budget line. Teams wrapping legacy APIs into agent tools may already be paying for context they never intended to send.
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The article builds on the two earlier installments in this series, which examined API keys and access tokens — the credentials agents use to reach outside services — and the risks of handing agents a human's keys. Kusanagi frames credential management as only the entry point to running agents, with cost as the equally overlooked second problem that shows up later.
The mismatch between unit price and total bill has drawn investor attention too. Andreessen Horowitz has analyzed that the token price for the same capability keeps falling at roughly a tenth per year, and the piece notes that GPT-3-class performance dropped from about $60 per million tokens in 2021 to about $0.06 in late 2024 — a nearly thousandfold decline in three years.
The piece also connects tool design directly to cost. Wrapping existing REST APIs one-to-one into MCP tools is easy and works at first, but mechanically generating a server from an OpenAPI spec turns 200 endpoints into 200 tools, inflating context and causing selection errors. A cited study found that narrowing tools to only what is needed raised tool-selection accuracy more than threefold and cut prompt tokens by more than half. Whether enterprises can bend their spending curve downward therefore appears to hinge less on model vendors' price lists than on how carefully they curate the tools and context they hand to agents.
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