Quick Answer (TL;DR)
AI Cost per Output measures the total cost to generate each AI output, including inference API costs, infrastructure overhead, retrieval pipeline costs, and any post-processing. The formula is (Inference cost + Infrastructure cost + Retrieval cost + Post-processing cost) / Total outputs generated. Industry benchmarks: Text generation: $0.005-0.05, Image generation: $0.02-0.10, Code generation: $0.01-0.15 per output. Track this metric to ensure your AI features have sustainable unit economics.
What Is AI Cost per Output?
AI Cost per Output is the fully-loaded cost of producing each AI-generated result. Unlike Token Cost per Interaction, which only captures inference API spend, this metric includes everything: the compute cost of running the model, the infrastructure that hosts your retrieval pipeline, the storage for embeddings and documents, the post-processing steps that validate and format outputs, and the monitoring overhead.
This metric is essential for building sustainable AI products because inference API costs are often just 40-60% of the total cost. A product manager who only tracks token spend is blind to the infrastructure, retrieval, and operational costs that can double the true cost per output. When setting pricing, usage limits, and ROI projections, you need the full picture.
AI Cost per Output also enables meaningful build-vs-buy and model selection decisions. A cheaper API model that requires more post-processing, more retrieval calls, and more retries might actually cost more per output than a more expensive model that produces acceptable results on the first try. Only a fully-loaded cost metric reveals these tradeoffs.
The Formula
(Inference cost + Infrastructure cost + Retrieval cost + Post-processing cost) / Total outputs generated
How to Calculate It
Suppose in a month your AI feature produced 100,000 outputs with the following costs:
AI Cost per Output = ($3,000 + $800 + $200 + $100) / 100,000 = $0.041
This tells you each output costs about 4.1 cents. If your pricing assumes 500 AI outputs per user per month, each user costs $20.50 in AI compute alone --- a critical number for evaluating subscription pricing against cost of goods sold.
Industry Benchmarks
| Context | Range |
|---|---|
| Text generation (short-form) | $0.005-0.05 per output |
| Text generation (long-form, multi-step) | $0.05-0.30 per output |
| Image generation | $0.02-0.10 per output |
| Code generation with context | $0.01-0.15 per output |
How to Improve AI Cost per Output
Audit Your Full Cost Stack
Most teams only track API costs and miss 30-50% of their total spend. Map every component that contributes to generating an output: embedding generation, vector search, document retrieval, model inference, response validation, formatting, logging, and monitoring. You cannot optimize what you have not measured.
Reduce Retries and Failures
Failed outputs that require regeneration double your cost. Track your first-attempt success rate and invest in improving it. Better prompts, more relevant context, and improved error handling reduce the number of outputs you need to generate per successful delivery.
Right-Size Your Infrastructure
Many teams over-provision retrieval infrastructure for peak load and pay for idle capacity during off-hours. Implement auto-scaling for vector databases, embedding services, and any GPU-based processing. Serverless options can reduce infrastructure costs by 30-50% for variable workloads.
Optimize the Retrieval Pipeline
Retrieval costs add up when you run multiple embedding lookups, cross-encoder re-rankings, and document fetches per output. Cache frequently accessed embeddings, pre-compute common query results, and reduce the number of retrieval calls through smarter query routing.
Batch Processing Where Possible
For non-real-time outputs (reports, summaries, analysis), batch multiple requests together. Batch API pricing is typically 50% cheaper than real-time pricing, and batching amortizes fixed costs across more outputs.