Как да изчислим AI Chatbot Cost
Какво е AI Chatbot Cost?
The AI Chatbot Cost Per Conversation calculator estimates the total API expense for each customer conversation handled by an AI chatbot. It accounts for multi-turn dialogue, system prompts, conversation history management, and escalation rates to human agents.
Формула
- T
- Average Turns (turns/conversation) — Mean number of back-and-forth exchanges per conversation
- S
- System Prompt Tokens (tokens) — Token count of the system prompt sent with every API call
- H
- History Tokens (tokens/turn) — Cumulative conversation history tokens (grows each turn)
- V
- Monthly Chat Volume (conversations/month) — Total customer conversations per month
Ръководство стъпка по стъпка
- 1Enter the average number of conversation turns and message length
- 2Specify the system prompt size and LLM model used
- 3Set the conversation history strategy (full history, sliding window, or summarization)
- 4View cost per conversation, cost per turn, and monthly total at your chat volume
Worked Examples
Common Mistakes to Avoid
- ✕Not accounting for conversation history growth — by turn 10, you are sending all previous turns as input, quadrupling token usage
- ✕Forgetting the system prompt is sent with every API call, not just the first one
- ✕Not factoring in escalation costs when the chatbot fails and a human agent takes over
Frequently Asked Questions
How much does an AI chatbot cost per conversation vs. a human agent?
An AI chatbot conversation costs $0.002-$0.25 depending on the model and conversation length. A human customer support agent costs $5-$15 per conversation (based on $15-25/hr salary handling 2-4 conversations/hr). AI chatbots are 50-5,000x cheaper per conversation, though complex issues still require human escalation.
How can I reduce chatbot API costs?
Use conversation summarization instead of full history to cap context growth, implement a sliding window of the last 4-6 turns, use a smaller model (GPT-4o-mini, Haiku) for initial triage and escalate to a larger model only when needed, and cache common Q&A responses to avoid repeated LLM calls.
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