last deploy · 2026.05.29 · cd57f71
Real-World Cost Estimation for Agentic AI Workflows
Real-world cost estimation for agentic workflows
Total: 22,000 tasks/month
Cost per invocation
$0.0937
Monthly
$2.1K
22.0K tasks
Annual
$24.7K
projected
Per User
$206.17
/month
Savings Applied
Optimization Suggestions
Use Batch API for Async Workloads
If some tasks don't need real-time responses, the Batch API offers 50% cost reduction.
~$309.25/mo savings
Save the current config to compare side-by-side with alternatives.
Every AI product leader needs to answer one question: what does this actually cost to run? Existing calculators model simple token pricing, but agentic workflows are fundamentally different — context windows accumulate across multi-step loops, tool definitions add overhead to every call, orchestration patterns multiply costs non-linearly, and prompt caching can save up to 90% on repeated system prompts.
This calculator models the real cost drivers that no other tool handles: arithmetic context accumulation across agent steps, tool call overhead, orchestration pattern multipliers (sequential, parallel, hierarchical, iterative), reflection/retry costs, and prompt caching savings. It produces interview-ready per-component stack breakdowns showing exactly where every fraction of a cent goes.
When planning an agentic AI deployment — whether a RAG-powered fraud investigation copilot, a multi-agent research system, or an autonomous code review pipeline — this tool provides the realistic cost projections needed for business cases. Toggle between direct API and AWS Bedrock pricing, compare configurations side-by-side, and export CSV reports for stakeholder review.
Cost modeling is product strategy. Understanding the per-invocation cost of each AI component in your stack — and knowing which levers (caching, model mixing, context windowing) move the needle — is the difference between a sustainable AI product and one that burns through runway.