How to Estimate AI API Cost Before Launch
The most reliable pre-launch estimate starts with user behavior. A model price alone is not a budget. You need traffic, calls per workflow, token counts, and cache assumptions.
The planning formula
Monthly cost = DAU x active days x calls per user per day x cost per call
Cost per call = input tokens x input price / 1,000,000 + cached input tokens x cached input price / 1,000,000 + output tokens x output price / 1,000,000
Inputs to collect before launch
| Input | How to estimate it | Why it matters |
|---|---|---|
| DAU | Use your expected active users, not signups. | Inactive users do not create API calls. |
| Calls per user | Count each model request inside the workflow. | Agents and retries can multiply calls. |
| Input tokens | Paste real prompts and retrieved context into the homepage estimator. | Long context is often hidden inside system prompts and RAG chunks. |
| Output tokens | Set a target response length and test real answers. | Output tokens often cost more than input tokens. |
| Cache hit rate | Estimate the repeated share of system prompts or reference context. | Cache hit pricing can materially reduce repeated-context workloads. |
Three real planning scenarios
| Scenario | Monthly volume | Token assumption | Cost lesson |
|---|---|---|---|
| Support chatbot | 2,000 DAU x 1.5 calls x 30 days = 90,000 calls | 900 input, 350 output, optional cached policy text | Output length and follow-up turns matter more than the first reply alone. |
| Internal writing assistant | 300 users x 8 calls x 22 workdays = 52,800 calls | 1,800 input, 1,200 output | Draft length dominates spend. Templates and shorter first drafts help. |
| Agent workflow | 10,000 tasks x 4 model calls = 40,000 calls | 3,200 input, 900 output, repeated tool instructions | Budget by calls per task, not tasks alone. |
Provider and model choice
- Use low-cost models for routing, classification, and extraction when mistakes are easy to detect.
- Use stronger models for final answers, legal-style reasoning, complex code changes, or high-value support cases.
- For repeated system prompts or reference policies, compare cached input pricing and cache support before launch.
- For long-context Gemini or document workflows, verify whether the official price has context-size tiers.
Common estimation mistakes
- Counting only one call when the product actually uses planning, retrieval, tool calls, retries, and final response calls.
- Using average output length from demos instead of production maximums.
- Counting cached tokens again as normal input tokens.
- Ignoring failed calls, retries, moderation, embeddings, or non-token provider charges.
FAQ
Should I estimate with average or peak usage?
Use both. Average usage gives a baseline budget; peak usage shows cash-flow risk and rate-limit pressure.
How do I use the homepage calculator with this method?
Enter the selected model, average input tokens, cached input tokens, output tokens, calls, and monthly period. Then test a high-output scenario.
Can I use cached input for every provider?
No. Cache support and prices differ by provider and model. Treat cache hit rate as an assumption until verified in production.