Know what your AI feature costs before the invoice does.
Free planning calculators for model APIs, AI agents, RAG, chatbots and cost per user. Current pricing inputs, visible assumptions, no signup.
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Start from the business question you actually have: monthly bill, cost per user, cost per task, or whether a workload fits.
LLM API Cost Calculator
Estimate monthly API spend from input tokens, output tokens and request volume across current OpenAI, Anthropic and Gemini models.
Unit economicsAI Cost per User Calculator
Calculate LLM API cost per active user and compare it with your SaaS subscription price or product margin.
Agent costAI Agent Cost Calculator
Estimate AI agent running cost from task volume, multi-step model calls, retries and paid tool usage.
RAG costRAG Cost Calculator
Estimate retrieval-augmented generation cost including document embedding, retrieved context, generation and vector database spend.
Chatbot costAI Chatbot Cost Calculator
Estimate monthly chatbot API cost using conversation length, history, system prompts and reply size.
CachingPrompt Caching Savings Calculator
Estimate how much prompt caching can reduce repeated input-token costs for supported AI models.
Batch processingBatch API Savings Calculator
Compare standard LLM processing cost with a configurable batch-processing discount for asynchronous workloads.
Context planningLLM Context Window Calculator
Estimate prompt tokens and check whether text plus reserved output fits current model context windows.
Traffic planningAI Cost per 1,000 Requests Calculator
Calculate what 1,000 AI API calls cost for a chosen model and average input/output size.
Model comparisonOpenAI vs Claude vs Gemini Cost Calculator
Compare modeled monthly API cost for the same workload across current OpenAI, Anthropic Claude and Google Gemini models.
Token pricing is easy. Product economics are not.
A provider's dollars-per-million-tokens table tells you almost nothing about what a feature will cost at production traffic. A real estimate needs a workload shape: how many requests happen, how much context is repeated, how much output is generated, whether an agent loops, and whether retrieval or paid tools add another bill.
TokenCOGS converts those moving parts into units product teams can reason about: cost per request, per user, per conversation, per task, per month and per year.
Pricing is reviewed against primary provider pages where possible. Every result is an estimate and should be rechecked before procurement or production budgeting.