Support AI cost calculator, explained
A good support AI cost calculator is not about the headline price per resolution. It is about the handful of inputs that actually determine your monthly bill and whether the automation pays off. This page explains those inputs, the mistake most calculators make, and how to build a comparison that reflects your real cost rather than the vendor's best case.
The inputs that drive your bill
- Monthly ticket volume. The base multiplier for everything. Estimate a realistic average, not a quiet month.
- Resolution rate. Use a realistic rate, not the marketed one. Fin, for example, is reported near 45 to 53 percent in production versus a marketed 76 percent.
- Pricing model. Per-resolution meters multiply cost by resolutions. Flat pricing fixes cost per band. This single input often matters more than the per-unit rate.
- Seats. Any tickets the AI does not resolve still need paid human seats, so seat count is part of the true cost.
- Add-ons and overage. AI add-ons, outcome fees, and volume overage are the line items that surprise people.
The mistake most calculators make
Most vendor calculators count deflection as resolution and use the marketed resolution rate. Both inflate the apparent value and understate the cost. A deflected ticket where the customer gave up is not a saved ticket, and the marketed rate is usually well above production. Build your calculator on verified resolution and a realistic rate, and add a reopen-rate haircut for the resolutions that come back.
One caution on any cost-per-resolution math: a "resolution" is only worth paying for if it actually solved the customer. Resolve reports verified resolution (confirmed by survey, confirmation, or no 72-hour recontact) separately from deflection, and treats reopen rate as the lie detector. A per-resolution bill that counts deflections, where the customer simply gave up, charges you for tickets that were never really solved. Grounding the price in verified resolution is the only honest way to compare cost.
A fair comparison at your volume
To compare honestly, run the same volume and the same realistic resolution rate through each model. At 5,000 tickets and 80 percent resolution (4,000 resolutions), per-resolution models land roughly: Fin near $4,009 a month, Zendesk near $6,000 in resolutions alone, Lorikeet around $3,200. Resolve's Scale tier is $2,500 flat for roughly 10,000 inbound and does not move at 90 percent resolution. Put your own numbers in and the shape holds: flat wins as volume and automation grow.
How Resolve prices instead
Resolve decouples price from resolutions entirely. You pay a flat monthly rate for a band of inbound contact volume, with unlimited seats and unlimited AI resolutions inside that band. The published tiers are Launch at $250 a month for roughly 750 inbound contacts, Growth at $900 for roughly 3,000, and Scale at $2,500 for roughly 10,000, with a custom flat Enterprise tier above that. If you go over a band, you bump to the next band, never a per-unit surprise.
The promise is one sentence: your bill never goes up because the AI did its job. It only changes when your business grows. That is the opposite of a per-resolution meter, where the better the automation performs, the more you owe.
Frequently asked
What inputs matter most in a support AI cost calculator?
Ticket volume, a realistic resolution rate, the pricing model, seat count for unresolved tickets, and add-ons or overage. The pricing model often matters more than the per-unit rate, because a per-resolution meter multiplies cost by resolutions while flat pricing fixes it per band.
Why not just use the vendor's calculator?
Vendor calculators often count deflection as resolution and use the marketed resolution rate, both of which flatter the result. Use verified resolution and a realistic rate, closer to the reported 45 to 53 percent for Fin, and add a reopen haircut for resolutions that come back.
How do I compare flat and per-resolution fairly?
Run the same volume and the same realistic resolution rate through both. Flat pricing tends to win as volume and automation grow, because per-resolution cost climbs with the resolution count while a flat band does not move until you outgrow it.