How I Calculate My LLM API Costs Before They Surprise Me

Every developer building with LLMs has been there: you prototype something cool, ship it, and then the AWS/OpenAI bill arrives.

I've been burned by this twice. So I started being obsessive about cost estimation before writing a single line of production code.

Here's my actual workflow:

Step 1: Estimate token usage realistically

Don't guess. Take your average prompt + expected output, multiply by your expected daily requests.

Example: A customer support bot

Input: ~500 tokens (system prompt + user message)

Output: ~200 tokens

Requests/day: 1,000

That's 500K input + 200K output tokens per day.

Step 2: Compare models — the difference is massive

For that same workload:

Model Daily Cost

GPT-4o ~$7.70/day

GPT-4o mini ~$0.42/day

Claude 3.5 Haiku ~$0.35/day

Gemini 1.5 Flash ~$0.26/day

That's a 30x difference between the most and least expensive option for identical functionality in many cases.

I use APICalculators.com to run these numbers — it has a free LLM cost calculator that lets you punch in your token estimates and compare OpenAI, Anthropic, Google side by side instantly.

Step 3: Don't forget the infrastructure tax

LLM cost is rarely your only cost. A real production app also pays for:

Vector DB (if you're doing RAG) — Pinecone vs Qdrant vs Weaviate pricing differs wildly (vector DB calculator)

Auth — Clerk vs Supabase Auth vs Auth0 (auth cost calculator)

Serverless functions — Lambda vs Vercel Functions (serverless calculator)

I've seen teams optimize their LLM costs and ignore that their Pinecone bill is 3x higher.

Step 4: Prompt caching changes everything

If you're using Anthropic or OpenAI, prompt caching can cut costs by 60-90% on repeated system prompts.

For a 2,000-token system prompt called 1,000 times/day:

Without caching: ~$6/day

With caching: ~$0.60/day

There's a prompt caching calculator that shows the exact savings before you implement it.

Step 5: Set a budget alert before you deploy

This sounds obvious but most people skip it. In OpenAI dashboard: Usage → Limits → set a hard monthly cap. Same for Anthropic.

My rule of thumb

Never deploy an AI feature without running the numbers first. 10 minutes of cost estimation saves you from a $500 surprise bill.

What's your approach to LLM cost estimation? Do you have a spreadsheet, a script, or just hope for the best? 👇