Inference economics is the study of the costs, pricing models, and business dynamics of running trained AI models in production. While training a model is a one-time investment, inference — generating responses to user queries — is an ongoing operational expense that scales with usage and increasingly dominates AI budgets. ¹⁾

Three primary factors drive inference cost:

GPU compute: Generating output tokens is inherently sequential — each token depends on all previous tokens. This autoregressive decoding cannot be fully parallelized, requiring sustained GPU utilization throughout generation. ²⁾

Memory: Model weights must reside in GPU memory during inference. A 70-billion-parameter model in FP16 precision requires approximately 140GB of GPU HBM — often spanning multiple GPUs. Additionally, the KV cache (storing attention state for prior tokens) grows with context length, consuming memory proportional to the context window size. ³⁾

Energy: High-end GPUs like the NVIDIA H100 cost $2.85-$3.50 per hour in cloud deployments, and inference workloads keep them running continuously. Energy costs compound at scale.

API providers charge by the token, with output tokens costing 3-5x more than input tokens. This reflects the computational asymmetry: input tokens are processed in parallel (prefill phase), while output tokens are generated one at a time (decode phase).

Representative pricing as of late 2025:

Prices have fallen roughly 10x per year for equivalent performance levels since 2022, driven by competition, optimization, and hardware improvements. ⁴⁾

Larger models are proportionally more expensive to run:

• They require more GPU memory (and often more GPUs)
• Each forward pass involves more computation
• They generate tokens more slowly

This creates a strong incentive to use the smallest model that meets quality requirements for each task — a principle that drives the popularity of LoRA adapters and distilled models.

The industry employs several techniques to reduce inference costs:

• Quantization: Reducing numerical precision (e.g., FP16 to INT4/FP8) cuts memory use and speeds computation. NVIDIA Blackwell GPUs enable up to 4x throughput gains through native low-precision support. ⁵⁾
• Distillation: Training smaller “student” models to mimic larger “teacher” models, achieving most of the quality at a fraction of the cost.
• Speculative decoding: Using a fast draft model to predict multiple tokens ahead, then verifying with the full model — reducing the number of expensive full-model forward passes.
• Batching: Grouping multiple requests together to improve GPU utilization. Continuous batching dynamically adds requests to in-flight batches.
• Prompt caching: Reusing KV cache entries for common prompt prefixes, dramatically reducing input processing costs for repeated system prompts.
• Sparse attention: Skipping unnecessary attention computations for long contexts, achieving 40-60% savings on million-token workloads. ⁶⁾

AI inference is a challenging business. OpenAI reportedly spent $1.35 for every $1 earned in 2025, with GPU costs outpacing API revenue. ⁷⁾

Key dynamics include:

• Cross-subsidization: Cloud providers and investors subsidize current pricing to build market share
• Price wars: Competition from Chinese providers (DeepSeek) and open-weights models drives aggressive undercutting
• Commoditization: Budget-tier models now cost less than $0.10 per million tokens, compressing margins
• Inference share growing: Inference now represents over 55% of total AI compute spend, up from 33% in 2023 ⁸⁾

While per-token costs fall by 10x annually, demand is growing by over 300%, causing total inference spending to increase even as unit prices drop. This “inference famine” dynamic means organizations often find their AI bills rising despite cheaper per-unit costs. ⁹⁾

Hardware improvements (3nm chips, custom AI silicon like AWS Trainium, Google TPU v6e), software optimizations, and competition will continue driving per-token costs down. However, the shift toward reasoning models that consume more inference compute per query may partially offset these gains. The net trajectory is toward cheaper but dramatically more prevalent AI inference. ¹⁰⁾

• Reasoning-on-Tap
• Value of 1-Million-Token Context Windows
• What Is a LoRA Adapter
• Post-Training RL vs Model Scaling