Do I need to track token cost per request in my chatbot?

Usually yes—if you’re running a chatbot that uses an LLM API, it’s a good idea to track token cost per request.

Why track it?

• Cost control: Token usage directly affects your bill.
• Debugging: Helps identify unusually expensive prompts or conversations.
• Optimization: Lets you compare prompt designs and models.
• Product analytics: You can estimate average cost per user, session, or feature.
• Alerts: You can detect spikes before they become expensive.

What to track

At minimum, log per request:

• Input tokens
• Output tokens
• Total tokens
• Model used
• Estimated cost
• Request ID / session ID
• Optional: latency, prompt version, user ID, error status

When it matters most

Tracking is especially important if you:

• have many users,
• allow long conversations,
• use expensive models,
• stream outputs,
• or want to set usage limits / quotas.

When you might not need it

You could skip detailed tracking if:

• it’s just a small prototype,
• usage is tiny and fixed,
• or you’re okay with rough monthly billing only.

But even then, basic logging is still helpful.

Practical recommendation

A common approach is:

1. Log token counts per request
2. Aggregate by user/session/day
3. Set budget alerts
4. Add rate limits or caps if needed

If you want, I can also show you a simple schema for logging token usage or a code example for doing it in your stack.