How product teams should estimate LLM costs before launch
A lightweight planning model for estimating token usage, model cost, caching impact, and monthly AI feature spend.
Before an AI feature launches, teams should estimate more than model quality. They need to understand request volume, context size, output length, retries, and caching.
Start with the workflow
Estimate how many times the feature runs per user, per day, and per month. Then multiply by expected input and output tokens. This gives a planning baseline before real telemetry exists.
1. Separate input and output
Input and output tokens often have different prices. Long generated responses can dominate spend even when prompts are short.
- Estimate average input context.
- Set output limits.
- Plan for retries.
2. Model caching assumptions
Repeated instructions, policy text, and retrieval context may be cacheable depending on provider and architecture. Model this as an assumption, then validate with real logs.
- Cache stable system prompts.
- Avoid repeating large context unnecessarily.
- Compare cached and uncached cost.
3. Replace estimates with telemetry
Calculators help before launch. After launch, teams need actual tokens, latency, error rates, and cost by feature or key.
- Route traffic through managed keys.
- Track project headers.
- Review cost per workflow.
How OggyCloud helps
Use the free calculator for planning, then use OggyCloud to capture real token usage and model costs after your feature ships.
