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AI Cost Optimization Template for AI Products
A template for tracking and reducing AI infrastructure costs across model inference, training, data storage, and API usage with specific optimization...
Updated 2026-03-04
AI Cost Optimization
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Frequently Asked Questions
What percentage of revenue should AI costs represent?+
For most SaaS products, AI costs should stay below 10-15% of the revenue generated by AI-powered features. If costs exceed 20%, the feature may not be viable without optimization. Consumer products with lower ARPU have tighter margins and should target 5-10%.
When should we self-host models instead of using APIs?+
Self-hosting becomes cost-effective when your request volume exceeds roughly 1 million requests per month for a given model, and your team has the ML infrastructure skills to manage deployment. Below that volume, API providers are almost always cheaper when you factor in engineering time.
How do we estimate costs for a feature that has not launched yet?+
Use the cost projections section with conservative estimates. Multiply expected daily active users by estimated requests per user by cost per request. Add a 30% buffer for edge cases, retries, and usage patterns you did not anticipate.
Should we optimize costs during development or after launch?+
Build cost awareness into development from the start. Set a cost per request target in your PRD. After launch, use the first 30 days of real usage data to calibrate your projections and prioritize optimizations based on actual cost drivers.
How do we handle cost spikes from prompt injection or abuse?+
Set hard rate limits per user and per API key. Monitor for anomalous usage patterns (sudden spikes in token count or request volume). Include abuse-related cost controls in your governance rules section.
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