# How I Got a $340 AWS Bill from a Side Project (And What I Built to Prevent It) > Source: > Published: 2026-06-19 15:37:59+00:00 The invoice arrived on a Tuesday morning. $340. For a side project I'd built in a weekend. A small LLM-powered summarization tool — users paste text, model returns a summary. I'd done the math before launching: roughly $0.002 per request, ~500 requests/day, around $30/month. Totally fine. What I hadn't accounted for: system_prompt_tokens = 800 requests_per_day = 2000 # not 500 — it went viral in a group chat input_price_per_1M = 2.50 # GPT-4o daily_cost = (800 * 2000 / 1_000_000) * 2.50 Plus the actual user input tokens. Plus output tokens. $340 later, I had learned my lesson. The Real Problem: API Pricing Is Designed to Be Hard to Compare Every provider uses different units: OpenAI → per million tokens (input vs output, different rates) Pinecone → read units + write units + storage GB/month Stripe → % of transaction + fixed fee + monthly platform fee AWS Lambda → per GB-second + per request + data transfer None of it is comparable at a glance. You end up either building a spreadsheet from scratch every time or just guessing — and guessing gets expensive. What I Built After the invoice incident I started keeping a cost estimation spreadsheet. It grew. Eventually I turned it into APICalculators.com — 16 free, browser-based calculators covering the infrastructure decisions most AI/SaaS developers face: LLM APIs GPT-4o, Claude Sonnet, Gemini Flash, Llama — cost by model, context length, daily volume Side-by-side comparison at your exact usage Vector Databases Pinecone vs Qdrant vs Supabase vs Weaviate Enter index size + queries/day → monthly cost Serverless AWS Lambda vs Cloudflare Workers vs Vercel Functions Cost at your invocation volume and memory config Auth Providers Clerk vs Auth0 vs Supabase Auth vs Cognito Monthly cost by MAU tier Payment Processors Stripe vs Paddle vs Lemon Squeezy Real fee comparison on your transaction volume The System Prompt Problem, Solved in 30 Seconds Here's what the LLM cost calculator would have shown me before I shipped: Model: GPT-4o System prompt: 800 tokens Avg user input: 200 tokens Avg output: 150 tokens Requests/day: 2,000 → Input cost: (800+200) × 2,000 / 1M × $2.50 = $5.00/day → Output cost: 150 × 2,000 / 1M × $10.00 = $3.00/day → Monthly: $240 vs my estimate of $30. 8x off. The fix was obvious once I saw it: cache the system prompt, shorten it, switch to a cheaper model for summarization. Cut the cost by 70%. Everything Runs in Your Browser No signup. No data sent anywhere. All calculations happen client-side — your usage numbers never leave your machine. If you're building anything that touches LLM APIs, vector databases, or cloud infrastructure, check your numbers before you ship. Surprise invoices are optional. What's the most unexpected cloud bill you've received? Drop it in the comments.