LLM cost archetype
Simple chatbot — LLM cost calculator & pricing model
Your app takes a user message, sends it to an LLM with some instructions, and returns a response. No memory between sessions. Every conversation starts fresh.
Does this sound like your app?
- □Users ask one-off questions and get answers
- □Each conversation is independent — no history carried over
- □Your system prompt is fixed and doesn't change per user
- □Response time matters — users are waiting in real time
Real-world example
A customer support widget on an e-commerce site. User types 'where is my order?' — the LLM responds using a fixed system prompt about the company's policies. Every session is identical in structure.
Default cost profile
- Calls per request
- 1
- Batch-eligible
- no
- Avg input tokens
- 800
- Avg output tokens
- 400
Assumes 1 LLM call per user message with short prompts (~800 input tokens) and brief responses (~400 output tokens). No conversation history carried between turns. Prompt caching provides modest savings on repeated system prompts. Not batch-eligible — responses are real-time.
Rough cost
$1–30/mo at 100–1,000 users/day depending on model.
Red flag
If you're including the last 5 messages in every prompt, this is actually a Chatbot with history.