Inference is the only market in technology where the price is collapsing 300–360× in three years and the dollar volume is exploding at the same time. The deflation of intelligence is fueling the inflation of intelligence budgets.
Per-million-token cost across the frontier, 2023 → 2026.
| Model | Launch | Input price / 1M tokens | Output price / 1M tokens |
|---|---|---|---|
| GPT-4 (initial) | March 2023 | $30.00 | $60.00 |
| GPT-4 Turbo | Nov 2023 | $10.00 | $30.00 |
| GPT-4o | May 2024 | $5.00 | $15.00 |
| GPT-4o (latest) | 2025 | $2.50 | $10.00 |
| Claude 3.5 Sonnet | 2024 | $3.00 | $15.00 |
| DeepSeek V3 / R1 | Late 2024–2025 | $0.14 – $0.55 | $0.28 – $2.19 |
| Gemini 2.5 Flash-Lite | April 2026 (market floor) | $0.10 | $0.40 |
Sources: OpenAI, Anthropic, DeepSeek, Google pricing pages, May 2026. Prices are public list prices; enterprise contracts differ.
Deflation × Volume = Inflation. The single most important equation in the AI economy right now.
The market floor for capable model inference falls roughly 10× per year. In 37 months, GPT-4-class output went from $60/M tokens to $0.40/M tokens. 150× in three years.
IDC: token and API-call loads grow 1,000× by 2027. Agents get hungrier as they get more capable, and there are 10× more of them.
10× cheaper × 1,000× more = 100× aggregate spend growth, even after the 87% per-action efficiency gain. The per-unit opex falls. The total opex explodes.
The 100×-spend curve terminates in a $68B+ annual market for token delivery alone. Add GPU rental, networking, observability, FinOps tools — the addressable market is much larger.
Inside the Fortune 500, the line item is moving fast. Per-agent-platform estimates, all referenced against IDC's forecast.
| Period | Large-enterprise annual inference spend | What it buys |
|---|---|---|
| 2024–2025 (baseline) | $5M – $15M | Early copilots, internal chatbots, a handful of agent pilots |
| 2026–2027 (projected) | $50M – $150M | Department-scale agents, customer-facing copilots, embedded AI in products |
| 2028–2029 (projected) | $500M – $1.5B | Enterprise-wide agent fleets, autonomous workflows, AI as primary interface |
Source: IDC FutureScape 2026, agent-economy forecast, scaled to large-enterprise baseline. Actual spend varies dramatically by industry, deployment model, and model mix.
When a line item grows 100× in 4 years, you can't manage it with spreadsheets. A whole product category — AI FinOps — has emerged to handle it.
Per-model, per-prompt, per-agent, per-workflow cost tracking. New vendors and new open-source tools (Langfuse, Helicone, OpenLLMetry) are building this layer in real time.
Route cheap requests to cheap models, expensive ones to the frontier. GPT-4-class problems get GPT-4-class compute; everything else gets Gemini Flash-Lite. Same answer, 1/50th the cost.
Continuous batching, prompt caching, prefix caching, speculative decoding. Each one shaves another 2–5× off the cost of a given workload. The rack now ships with these features baked in.
"AI agent inference cost is deflating 10× annually — faster than PC compute or dotcom bandwidth — but aggregate enterprise spend is increasing 3–5× because agent demand growth outpaces cost decline."
— Perea.ai, Agent Inference Unit Economics research"The math: 10× cheaper per token + 1,000× more tokens = 100× aggregate spend growth, even with 87% per-action efficiency gains."
— IDC FutureScape 2026 synthesis