Tools in This Collection
API Cost Calculator
Estimate monthly costs for OpenAI, Anthropic, Stripe, Twilio, AWS, and other API services
AI Agent Cost Estimator
Estimate AI agent development ($25K-$300K+) and monthly operating costs by complexity tier
RAG Chunk Size Calculator
Calculate optimal chunk size and overlap for retrieval-augmented generation pipelines
Context Window Calculator
Calculate how many tokens a document or conversation consumes in an LLM context window
System Prompt Tokenizer
Analyze system prompt token count, cost per call, and daily/monthly LLM usage costs
AI Coding Assistant ROI Calculator
Calculate ROI of AI coding tools like Copilot, Cursor, and Claude Code on developer productivity
AI Image Cost Calculator
Compare per-image and monthly costs across DALL-E, Midjourney, Stable Diffusion, and Flux
Guides & Articles
AI and ML Cost Workflow
AI API costs scale in non-obvious ways. A prototype that costs $10/month can cost $10,000/month in production if the architecture isn't designed for cost efficiency. Understanding token economics, model selection trade-offs, and RAG architecture decisions before shipping is critical for any production AI system.
Step 1: Model Pricing and Token Math
LLM API costs are measured per 1,000 tokens (roughly 750 words). Input tokens (your prompt + context) and output tokens (the model's response) are priced separately, with output tokens typically costing 3-5x more. The API Cost Calculator lets you enter estimated tokens per request, requests per day, and model selection to project monthly costs across OpenAI, Anthropic, and Google models side by side.
Step 2: RAG Architecture Decisions
Retrieval-Augmented Generation systems retrieve document chunks and include them in the prompt context. Larger chunks mean fewer retrievals but fill the context window faster and cost more. Smaller chunks require more retrievals but may fragment context. The RAG Chunk Size Calculator models the token cost at different chunk sizes (256-2048 tokens) and overlap percentages for your expected query volume.
Step 3: Context Window Management
Context window costs are often the hidden driver of AI spending. Systems that blindly inject full conversation history or large documents into every request burn tokens on information the model doesn't need. The Context Window Calculator estimates token usage for different document sizes and conversation lengths, helping identify where context trimming would reduce cost without degrading quality.
Step 4: AI Developer Tooling ROI
AI coding assistants (GitHub Copilot, Cursor, Claude Code) typically save 1-3 hours per developer per week. The AI Coding ROI Calculator compares subscription cost against time saved at your loaded developer rate. At $100/hour all-in, even 1 hour/week saved by a $20/month tool is a 40:1 ROI.
Frequently Asked Questions
How do I estimate OpenAI API costs before going to production?
Multiply your average input tokens per request by the input token price, add average output tokens × output token price, then multiply by estimated daily requests × 30. For GPT-4o (as of early 2025): ~$0.0025/1K input tokens, ~$0.01/1K output tokens. A typical 1,000-token prompt + 500-token response at 100 requests/day costs about $15/month. The API Cost Calculator handles this math across models.
What is the difference between input and output tokens?
Input tokens include everything you send to the model: the system prompt, conversation history, retrieved document chunks, and the user's message. Output tokens are the model's response. Output tokens typically cost 3-5x more than input tokens because they require sequential generation. Optimizing output length (shorter, more focused responses) often reduces costs more than reducing input length.
What chunk size should I use for RAG?
For most document retrieval use cases: 512 tokens (approximately 400 words) with 50-100 token overlap is a good starting point. Larger chunks (1024-2048) provide more context per retrieval but fill the context window faster. Smaller chunks (128-256) are precise but may miss context. Test empirically: retrieve 3-5 chunks per query and evaluate whether answers are complete.
Is GitHub Copilot worth the cost for developers?
At $10-19/month per developer, Copilot typically needs to save roughly 6-12 minutes per day to break even at a $80,000/year fully-loaded developer cost. Studies report average productivity gains of 30-55% on completion-eligible tasks. Real-world benefit varies by developer experience level and codebase. The AI Coding ROI Calculator lets you input your actual time savings estimate for a personalized break-even.
How do I reduce LLM API costs in production?
Key cost reduction strategies: 1) Route simple queries to cheaper models (GPT-4o-mini or Claude Haiku) — often 90%+ cheaper with minimal quality loss. 2) Implement prompt caching for stable system prompts (50% discount on Anthropic). 3) Trim conversation history — only include the last N turns, not full history. 4) Use Batch API for offline workloads (50% cheaper on OpenAI). 5) Optimize RAG retrieval to reduce injected chunk count.