free_tool
AI Agent Cost Calculator
Estimate what an LLM agent really costs per month across Claude, GPT-4o and Gemini. It models the part most calculators miss: every tool-call adds a turn and re-sends the context, so cost grows faster than you'd think.
Estimated cost
$5,870/mo
$0.10 / request · $196 / day
- Input tokens20.6k/req · agentic context
- $3,708
- Output tokens2.4k/req
- $2,160
- Embeddings (RAG)query embedding per request
- $1.80
Numbers look scary, or too good to be true? I'll pressure-test your real workload and find the savings.
Want this validated? Book an auditList prices per 1M tokens, captured 2026-06. Estimates only; your real cost depends on caching, batching, context reuse, and provider deals. share the link to compare scenarios.
how_it_works
How the estimate is built
Each request runs 1 + tool-calls LLM turns. Every turn re-sends the conversation so far, so input tokens grow roughly quadratically with tool-calls. That's the "agentic tax." We bill input and output tokens at each model's list price, add a query embedding per request when RAG is on, and an optional flat GCP serving line.
It's a planning estimate, not a quote. Real bills move with prompt caching, batching, context reuse, and committed-use discounts, which are exactly the levers an audit pulls.
faq
Questions & answers
- How does the AI Agent Cost Calculator estimate monthly cost?
- It models the agentic context tax: every tool call adds an LLM turn, and each turn re-sends the whole conversation, so input tokens grow with the square of tool calls. It then prices those tokens against each model's public per-million rates and multiplies by your daily request volume over a 30-day month.
- Why does adding more tool calls raise the cost so much?
- Each tool call is another round trip where the model re-reads everything before it, so input tokens scale by roughly tool calls times tool calls plus one, over two. Going from 3 to 6 tool calls can multiply input tokens several times over, not just double them.
- Which models and prices does it compare?
- It compares Claude Haiku, Sonnet and Opus, GPT-4o and GPT-4o mini, and Gemini Flash and Pro, using public list prices. You can add a flat infrastructure line if you want to fold in serving cost.
- Is my scenario data sent anywhere?
- No. All the math runs in your browser and nothing is sent to a server. Your inputs are only encoded into the URL if you choose to copy a shareable link.
- Does the estimate account for prompt caching or batch discounts?
- No. It is a planning estimate at list price, so real bills usually come in lower once you apply prompt caching, batching, context reuse, or committed-use discounts.
Want these numbers pressure-tested on your stack?
I'll review your inputs and tell you where the real cost and risk are. Book a call, or leave your email and I'll reach out.
Prefer proof first? See how this plays out in real case studies →