Quick Answer (TL;DR)
Token Cost per Interaction measures the average expenditure in tokens and dollars for each user interaction with an AI feature. The formula is Total tokens consumed x Price per token / Total interactions. Industry benchmarks: Simple chat: $0.001-0.01, Complex analysis: $0.05-0.30, RAG workflows: $0.02-0.15 per interaction. Track this metric from day one of any AI feature launch to manage unit economics.
What Is Token Cost per Interaction?
Token Cost per Interaction captures the direct inference cost of serving each user request through an AI model. Every prompt and response consumes tokens --- the fundamental billing unit for LLM APIs --- and those tokens have a price. This metric translates raw token consumption into a per-interaction cost that product managers can map to business models and pricing.
This metric is critical because AI features have variable marginal costs, unlike traditional software where the cost of serving one more request is near zero. A single complex interaction can cost 100x more than a simple one. Without tracking cost per interaction, you cannot build sustainable pricing, set usage limits, or forecast infrastructure spend.
Understanding token cost also reveals optimization opportunities. A prompt that uses 2,000 tokens of system instructions for a task that only needs 500 tokens is wasting money on every call. Breaking down costs by component --- system prompt, user input, retrieved context, model output --- shows exactly where the spend goes and where cuts are possible without degrading quality.
The Formula
Total tokens consumed x Price per token / Total interactions
How to Calculate It
Suppose your AI feature processed 50,000 interactions in a month, consuming 150 million tokens at a blended rate of $0.008 per 1,000 tokens:
Token Cost per Interaction = (150,000,000 x $0.000008) / 50,000 = $1,200 / 50,000 = $0.024
This tells you each interaction costs about 2.4 cents. If your subscription price implies a per-user budget of $2/month and users average 100 interactions, you are spending $2.40 per user --- already over budget before accounting for infrastructure, storage, and other costs.
Industry Benchmarks
| Context | Range |
|---|---|
| Simple chatbot (short Q&A) | $0.001-0.01 per interaction |
| Complex analysis or summarization | $0.05-0.30 per interaction |
| RAG-augmented workflows | $0.02-0.15 per interaction |
| Code generation (multi-file context) | $0.10-0.50 per interaction |
How to Improve Token Cost per Interaction
Trim System Prompts
System prompts are the hidden cost driver in most AI features. Audit every system prompt for redundant instructions, verbose formatting, and context that does not improve output quality. A 40% reduction in system prompt length directly reduces cost by that proportion on every single call.
Implement Smart Context Windows
Not every interaction needs the full conversation history or every retrieved document. Build logic to select only the most relevant context for each query. Techniques like conversation summarization and selective retrieval can cut context tokens by 50-70%.
Route to Cheaper Models
Use smaller, cheaper models for tasks that do not require the largest model capabilities. Classification, entity extraction, and simple Q&A can run on models that cost 10-20x less per token. Save the expensive model for complex reasoning and generation.
Cache and Pre-Compute
Identify interactions with predictable outputs and serve cached responses instead of making fresh API calls. Semantic caching, pre-computed embeddings, and result reuse can eliminate 15-30% of API calls entirely.
Set Token Budgets per Feature
Assign each AI feature a maximum token budget per interaction. Implement hard limits on input context length and output generation length. This prevents runaway costs from edge cases --- a single user pasting a 50,000-word document should not cost $5 per interaction.