Paano Kalkulahin si AI Chatbot Cost
Ano ang 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.
Pormula
- 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
Step-by-Step na Gabay
- 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
Mga Nalutas na Halimbawa
Mga Karaniwang Mali na Dapat Iwasan
- ✕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
Mga madalas itanong
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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