AI and automation
Price a chatbot, agent workflow, or batch job from its token shape and real request volume before it reaches users.
When to use this
Use request samples, include background jobs and retries, and compare low, expected, and high scenarios.
Default result
The starter scenario is a 200-request-per-day chat helper. Pricing fields are editable and intentionally not tied to a provider.
A support chatbot that averages 2,500 input tokens and 800 output tokens per request, handles 200 requests per day, and runs 30 days per month would process 15 million input tokens and 4.8 million output tokens per month.
At $0.50 per 1 million input tokens and $2.00 per 1 million output tokens, that scenario is about $7.50 for input plus $9.60 for output, or roughly $17 per month before retries, tool calls, longer conversations, and background agent work.
Use the monthly result as a planning number, not a contract. Add room for retries, tests, scheduled jobs, prompt changes, longer user conversations, and background automations.
Token usage can grow quickly when agents call tools, summarize long documents, or retry failed steps. Keep API keys out of frontend code, set spending alerts where your provider supports them, and test with realistic examples before opening a workflow to users.
Estimate a low, expected, and high scenario. Then compare the API path with a local model path, especially when privacy, predictable volume, or self-hosting control matters.
Provider prices, cache discounts, batch discounts, rate limits, and model names can change. Use the pricing page for the exact provider and model you plan to use, then rerun the estimate before turning on production traffic.
Keep API keys on the server side, avoid sending unnecessary personal or confidential data to a model provider, and set billing alerts before agents, scheduled jobs, or background retries can run unattended.
AI pricing changes often and varies by model. Enter the current input and output prices from the provider pricing page so the estimate matches the model you are actually considering.
Yes. Many models price input and output tokens differently, and agent workflows can produce more output tokens than a short chat answer.
Include background automations, retries, tests, scheduled jobs, and internal users. Those quiet calls are often what turns a small prototype into a real monthly bill.
Yes. Use the estimate as one side of the comparison, then check whether local hardware can handle the same workload before buying hardware or committing to a provider.
The page keeps prices editable because provider pricing, cache discounts, model names, and batch discounts can change. Enter the current prices for the exact model and billing mode you plan to use.
No. The calculator focuses on model usage charges. Add hosting, monitoring, storage, support, engineering time, and safety review separately when planning a production workflow.
