Chatbase Pricing vs Intercom Fin vs Guzli: A Practical Cost Forecast for 2025
Trying to forecast chatbot costs? This guide shows how Chatbase message credits compare to Intercom’s seat and per-resolution model, and how Guzli’s action workflows can reduce total volume.
Chatbase Pricing vs Intercom Fin vs Guzli: A Practical Cost Forecast for 2025
If you are comparing Chatbase pricing vs Intercom Fin vs Guzli, you are probably trying to answer:
- Which tool will cost less as volume grows?
- Which model is easiest to forecast?
- How do actions (Stripe, Shopify, scheduling) change the math?
This post avoids hand-wavy “it depends” advice and gives you a forecasting approach you can use.
For broader product comparison, start here: Guzli vs Intercom vs Chatbase
The three pricing models you are really choosing between
1) Intercom: seats plus usage-based AI
Intercom often combines:
- A seat model for the platform
- Usage-based AI pricing
This can work well when:
- You have a large support org that needs the full suite
- Each resolved conversation is worth more than the AI usage cost
2) Chatbase: message credits by tier
Chatbase uses message credits. This is often easier to forecast than per-resolution, but it depends on conversation length.
3) Guzli: predictable plans plus action workflows
Guzli focuses on reducing total workload by:
- Deflecting repetitive support
- Completing actions inside chat
- Capturing leads and booking meetings
Actions matter because they reduce back-and-forth messaging and escalations.
A simple forecasting model you can copy
You only need four inputs:
- Monthly conversations (or tickets)
- Deflection rate (percent resolved by AI without human)
- Average bot messages per conversation
- How many conversations turn into actions
Why actions change cost
Actions reduce “chat loops.”
Example:
- Without actions: “Can you cancel me?” becomes 6 to 10 messages, then escalation
- With Stripe actions: confirm intent, run action, summarize, done
This affects cost on message-credit models and affects human workload on all models.
Example scenarios (with clear assumptions)
These are not vendor promises. They are planning examples.
Assumptions:
- AI resolves 40% of conversations
- Average 4 bot messages per conversation when actions exist
- Average 7 bot messages per conversation without actions
Scenario A: 2,000 conversations per month
- 800 resolved by AI (40%)
- 1,200 escalated
If your top intents are billing and order status, actions usually reduce total message count and escalation rate.
Scenario B: 6,000 conversations per month
At this volume, pricing model shape matters a lot.
- If your cost scales per resolved conversation, your bill rises with success
- If your cost scales by messages, your bill rises with volume and chat length
- If actions cut chat loops, you control the bill and reduce human workload
Scenario C: 20,000 conversations per month
At this volume, you should evaluate:
- Automation rate by intent
- Channel mix
- Whether ecommerce workflows reduce “where is my order” tickets
If you want ROI benchmarks across industries, read: AI chatbot ROI guide and use the ROI calculator
What to ask vendors so costs do not surprise you
Use these questions in every sales call:
- What counts as “usage”?
- How do you bill when the bot escalates?
- Do actions reduce message usage or add usage?
- Can I set limits to prevent runaway costs?
- Can I see analytics by intent (billing, order status, returns)?
This checklist approach is also covered in our: AI Chatbot Buyer’s Guide
When each model is best
Intercom model is best when
- You want a full suite
- Your support interactions are higher value
- Your team is already standardized on Intercom
Chatbase model is best when
- You want an AI agent builder quickly
- You can keep conversations short and well-scoped
- You prefer forecasting by message volume
Guzli model is best when
- You want support plus lead capture
- You want Shopify and Stripe outcomes inside chat
- You want scheduling inside chat for faster conversion
- You want fewer escalations and fewer long conversations