Calculate AI API costs for OpenAI GPT-4, Claude, Gemini, and other LLM providers
An AI API cost estimator calculates expenses for using LLM APIs like OpenAI GPT-4, GPT-3.5, Claude 3, Claude 3.5 Sonnet, and Gemini based on requests per day, average input tokens (prompt length), average output tokens (response length), and optional caching. Estimates daily, monthly, and annual costs accounting for different pricing per provider and model, helping developers budget AI features, choose cost-effective models, optimize prompt engineering, and evaluate whether AI implementation is financially viable for their application scale.
Prevent sticker shock from AI API bills that can reach thousands monthly by understanding costs before integrating LLMs into production applications. Choose the right model for your budget—GPT-4 at $30/million output tokens is 20x more expensive than GPT-3.5 at $1.50/million, but may be worth it for quality-sensitive applications. Optimize costs through strategic choices like caching repeated prompts (50-75% savings), using GPT-3.5 for simple tasks and GPT-4 only for complex reasoning, or implementing hybrid approaches. Calculate break-even user counts and pricing for AI-powered features—if API costs $1000/month and you have 100 users, need to charge $10/user minimum to break even. Essential for AI startups, developers adding AI features, and companies evaluating AI implementation ROI.
Select AI provider and model (GPT-4, GPT-3.5 Turbo, Claude 3 Opus, Claude 3.5 Sonnet, Gemini Pro), enter estimated requests per day, average input token count (prompts: 50-500 tokens typical, 1 token ≈ 0.75 words), and average output token count (responses: 50-2000 tokens typical). Enable caching if prompts repeat frequently for 50-75% savings on cached portions. Results show daily, monthly, and annual costs plus cost per request. Optimize by: using shorter prompts, limiting output lengths, caching system prompts, choosing cheaper models for simple tasks, and batch processing when possible. Monitor actual usage in production and adjust estimates—token counts often exceed initial estimates.
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