AI economics
Estimate AI API costs by modeling input tokens, output tokens, retries, caching, traffic, routing, evaluation runs, and monthly usage before shipping an LLM product.
Comparison
| Option | Best for | Strengths | Tradeoffs | Use when |
|---|---|---|---|---|
| Per-call estimate | Early prototypes, prompt testing, and single workflow modeling | Simple and fast to understand. | Misses retries, background jobs, evaluations, and traffic spikes. | You are deciding whether a feature is plausible. |
| Monthly usage model | Product pricing, budgets, support bots, and content pipelines | Connects token cost to users, sessions, and business volume. | Needs traffic assumptions and real usage distribution. | You are preparing a launch plan or unit economics model. |
| Production log model | Optimization, vendor negotiation, routing, caching, and margin protection | Uses actual prompts, outputs, latency, retries, and failures. | Only available after enough traffic has been collected safely. | You are optimizing a live product. |
The obvious cost is input plus output tokens. The hidden cost is everything around it: retries, tool calls, summarization jobs, eval runs, long context, and unnecessary prompt boilerplate.
The best optimization is usually routing: send simple tasks to cheaper models, reserve expensive models for hard work, shorten prompts, cache stable context, and avoid stuffing full documents when retrieval can supply evidence.
AI pricing, model quality, and user behavior change. Revisit the cost model after launch, after major model releases, and whenever the product adds new workflows.
Decision Rules
Estimate cost from real token counts as soon as possible.
Budget for retries, evals, background jobs, and failure handling.
Use smaller models or cached context for predictable low-risk tasks.
Do not promise customer pricing from a single happy-path prompt.
Related Guides
Estimate per-call, daily, and monthly AI API usage costs.
OpenTranslate documents into token scale before estimating cost.
OpenUse the interactive calculator for monthly token estimates.
OpenUnderstand token scale before estimating API usage.
OpenCompare quality, speed, and model choice before optimizing cost.
OpenChinese Archive
Topic Hubs
FAQ
Estimate average input tokens, output tokens, calls per user, users per month, retries, background jobs, and evaluation runs. Then multiply by current vendor pricing and validate with real logs.
Common causes include longer outputs, retries, tool calls, hidden background jobs, larger context, evaluation batches, and traffic distribution that differs from the prototype.
Not always. Compare cost per successful task. A cheap model that fails or needs multiple retries can cost more than a stronger model routed only to hard cases.
Source Links
The strongest decision is always local to your workflow. Save the vendor links, define a representative task, record the exact prompt or command, and compare the final evidence instead of the marketing claim.
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