Most teams discover their API bill after they've built. Calibrait models the true cost of agentic AI workflows — including context accumulation, tool call overhead, and provider differences — before you write a line of code.
Why most teams get this wrong
Each step of an agentic workflow carries context forward. By step 8, you're paying 10× what you paid on step 1 — and most teams don't realise until the bill arrives.
Input tokens, output tokens, caching discounts, batch rates — the real cost difference between providers is rarely what the headline price suggests.
How much context you carry, how many tool calls you make, how big your system prompt is — these choices matter more than which model you pick.
Three steps to understanding your AI infrastructure costs before you commit.
Choose a preset or configure your own — steps per run, tool calls, context carry-over, prompt size. Use plain English, no token expertise required.
See the real cost across Anthropic, OpenAI, Google Gemini, DeepSeek and Grok — including the impact of caching and batch API discounts.
Understand which architecture choices drive cost, which provider fits your workload, and what your monthly spend looks like at scale.
Whether you're scoping a client project or sizing infrastructure, Calibrait gives you the numbers before they matter.
Model client workloads before scoping. Put a cost breakdown in the proposal, not a surprise in the invoice.
Get budget approval with confidence. Show finance what the infrastructure will cost at 1,000 runs/day.
Choose your provider and architecture before you build. Avoid expensive pivots later.
Free to use. No account required. Results in under a minute.
Model your agent costs →Pricing data verified May 2026 · Anthropic · OpenAI · Google Gemini · DeepSeek · Grok