Companies adopting Intercom's $0.99-per-resolution pricing report billing increases of 100%+ within a year. One founder went from $200/month to $1,400 during a product launch. A Reddit post titled "My Intercom billing shot up by 120%" captured a sentiment that's become widespread.
After 18 months of real-world deployment across Intercom, Zendesk, and Salesforce Agentforce, the pattern is clear: outcome-based pricing transforms billing complexity rather than eliminating it.
Last Updated: January 2026
The Core Problem: Better AI = Higher Costs
Here's what outcome-based pricing data reveals: when your AI gets better, your costs go up.
Traditional cost structures work the opposite way. Better software is usually more efficient. Better employees produce more value per dollar. But with per-resolution pricing:
- Improve your knowledge base → AI resolves more issues → costs increase
- Train the AI on better responses → resolution rate climbs → costs increase
- Reduce friction in the chat widget → more users engage → more resolutions → costs increase
One support team invested time improving their help center content, which made Fin more effective. Their resolution rate jumped from 40% to 65%. Great for customer experience—but costs increased by over 60% with no corresponding increase in support volume.
This creates a counterintuitive dynamic. Do you deliberately keep your AI less effective to control costs? The alignment becomes more complex than the model suggests.
The Definition Problem: What Counts as a "Resolution"?
The word "resolution" seems straightforward. But dig into the definitions, and you find significant variation.
| Provider | Price Point | Resolution Definition | Key Risk |
|---|---|---|---|
| Intercom (Fin) | $0.99/resolution | Hard (customer confirms) OR Soft (no follow-up in 24h) | Pays for abandoned conversations |
| Zendesk | Varies by tier | No human handoff AND AI determines "satisfactorily resolved" | Black-box AI evaluation |
| Salesforce (Agentforce) | $2/conversation | Completed agent conversation | Charges per conversation, not resolution |
Intercom's "soft resolution" is where things get complex. If a customer asks a question, gets an answer they don't find helpful, and leaves? That's still counted as a resolution. You pay $0.99 for an interaction that didn't resolve anything.
One community post highlighted another edge case: Fin counts a resolution even when a human takes over from the AI chatbot, as long as the original AI response happened first.
With token-based billing, you can count tokens yourself. With seat-based billing, you know how many seats you have. But with resolution-based billing, you're trusting the provider's definition and counting methodology. Companies report discrepancies between their internal metrics and provider invoices—with no clear way to reconcile.
The Soft Resolution Loophole
A soft resolution typically means the customer didn't ask for more help within 24 hours. The assumption: silence equals satisfaction.
But silence can mean many things:
- Satisfaction: The answer worked
- Abandonment: The answer didn't help, customer gave up
- Channel switching: Customer called phone support instead
- Delayed discovery: Customer tried the solution, found it didn't work, but it's been 25 hours
All count as billable "resolutions."
One support team ran an experiment: they followed up with customers who had "soft resolution" conversations. Only 62% reported their issue was actually resolved. They were paying full price for a 38% false-positive rate.
The Forecasting Challenge
Traditional SaaS costs are predictable. 50 seats at $100/month = $5,000/month.
Outcome-based AI costs depend on customer behavior, issue complexity, AI effectiveness, seasonality, and product changes. An analysis of 200+ Capterra reviews found a consistent theme: "founders and operators can't predict their customer support bills anymore."
One company budgeted $3,000/month based on historical support volume. During a product launch, the bill came to $8,500—a 183% overrun from a single month.
Resolution-based costs also compound with growth. More customers means more support interactions means more resolutions. Unlike infrastructure costs (which have economies of scale), resolution-based costs scale linearly—or worse—with volume. This mirrors the power user problem that many AI products face.
What This Means for Finance Teams
Outcome-based AI costs require different financial controls:
- Budget ranges, not points: Accept that costs will vary and plan for 20-30% variance
- Rolling forecasts: Update projections monthly based on actual trends
- Independent tracking: Your resolution counts won't match provider invoices without reconciliation
- Definition documentation: Get the exact resolution criteria in writing before signing
For procurement, push for annual buckets (which provide flexibility for spikes), overage caps, and advance notice requirements if the provider changes what counts as a resolution.
The Bottom Line
Outcome-based pricing for AI is not simpler. It's fundamentally different—and requires infrastructure that most finance teams don't have.
The $0.99 resolution sounds cheap. At scale, with compounding growth, in an unpredictable support environment, it becomes the cost structure that demands the most attention.
Bear Lumen tracks resolutions independently and reconciles against provider invoices automatically. Request early access for visibility into outcome-based AI costs.