Mike HeapMike is an experienced Product Manager who focuses on all the “non-development” areas of My AskAI, from finance and customer success to product design, copywriting, testing and more.
Table of Contents
- The number everyone compares tells you almost nothing
- The meter, the definition, and the stack
- The meter: what unit you are billed on
- The survey: how 20 vendors charge
- Vendors with a public per-unit rate
- Vendors with no public rate
- The definition: what counts as one unit
- The stack: what's bolted on top
- What the meter does as you get better
- How to decode any pricing page this week
- When a success-metered model is the right call
- The takeaway
- FAQs
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Every AI vendor quotes a per-resolution rate. It's the least useful number on the page. Here's how 20 customer service vendors actually meter your bill, and what each one counts as billable.
Every AI customer service vendor wants you to compare their per-resolution rate. I'll save you some time: it's the least useful number on the page. Two things decide your bill instead: which meter they put you on, and what they count as one tick of it.
Here's the spot I watch CX leaders end up in. You ask two vendors for a quote, and both come back with "$0.99 a resolution". You drop the two numbers into a spreadsheet, they match, and you assume the products cost the same.
They don't. One counts a workflow handoff to a human as a billable resolution and the other doesn't, so the same month of tickets produces two very different invoices (I'll show you exactly where that happens).
The deeper problem is that you usually can't work out what either vendor will cost you before you start. Some price on replies, but you don't know how many replies your team sends in a month. Some price on resolution, but you've no idea what resolution rate you'll get on day one, let alone in a year.
A few price on a "credit" that isn't obviously tied to anything, and a brave couple price on tokens (an abstract unit that only makes sense if you already live in AI). You often only find the real number out once you're inside the product.
I'm Mike, co-founder of My AskAI. We run AI customer service for 200+ ecommerce and SaaS businesses inside Zendesk, Intercom, Freshdesk, HubSpot and Gorgias, and our agents have now resolved over a million tickets.
That means we've read most of these pricing pages, either as the incumbent a team is leaving or the tool they're switching to. So this is the survey I wish existed when we started: how 20 AI customer service vendors actually charge, what each one counts as billable, and a simple way to read any pricing page so you can compare them honestly.
The number everyone compares tells you almost nothing
TL;DR: The headline per-resolution rate barely predicts your bill. What a vendor counts as a billable unit, and what they stack on top, moves the cost far more.
The per-resolution rate is the most-compared and least-predictive number in AI pricing. Two vendors at the same headline rate routinely land bills that differ by two or three times (I've seen enough invoices to promise you that).
I get why the rate gets all the attention. A procurement spreadsheet wants one cell per vendor, and "$0.99/resolution" fits in a cell.
The published guides don't help either. The articles that rank for "AI pricing models" are written for the people building and selling AI. A head of support barely gets a look-in.
Orb's guide lists seven monetisation models and tells you to experiment to find your mix. Zuora describes four kinds of agentic AI pricing using examples from code agents and cloud-cost tools. Both are genuinely useful if you sell AI, but neither tells a head of support what they'll pay.
The failure case is boringly predictable, and I've watched it happen plenty. A team picks the lower headline rate, signs, and then the bill behaves nothing like the spreadsheet, because the two vendors define a billable unit differently and stack different fees on top.
You hear the same thing from the operators we talk to. In one r/B2BSaaS thread on outcome-based pricing, a founder put it plainly:
"Customers love the idea in theory: pay for value delivered, not usage. But in practice? Budget unpredictability, lack of control over outcomes and all the complications when customers don't fully follow through on their side."
Budget unpredictability is the real complaint here. The rate barely comes into it. And I'd go further: the unit behind the rate, and whether you can forecast your own usage of it, is what bites you later.
The meter, the definition, and the stack
TL;DR: Read every pricing page as three layers: the meter (what unit you're billed on), the definition (what counts as one unit), and the stack (seats, minimums, overage, onboarding). The survey below classifies 20 vendors by meter.
Every AI customer service price I've read is three layers stacked on top of each other. Get all three and you can compare any two vendors honestly; miss one and the comparison is fiction.
The first layer is the meter: the unit you're billed on. The second is the definition: what the vendor counts as one unit of that meter. The third is the stack: the seats, platform minimums, overage charges and onboarding fees bolted on top of the per-unit rate.
I'll take them one at a time, then drop the whole market into one table.
The meter: what unit you are billed on
There are really eight meters in this market. I've ordered them by how easily you can forecast your own usage of the unit before you sign, because (as the founder above found out) that's what decides whether a quote means anything.
- Per-ticket, usage-based. You pay when the AI works, whether or not it lands a resolution. Most forecastable, because most teams already know their monthly ticket count.
- Per-seat or per-agent. You pay per human agent. Forecastable from your headcount, but the bill has nothing to do with how much work the AI actually does (we mostly see this on copilot-style tools).
- Per-resolution or per-outcome. You pay when the AI succeeds, so the bill tracks success and rises as you get better. Hard to forecast, because you don't know your resolution rate on day one or where it lands in a year.
- Per-conversation. You pay for each conversation the AI engages, resolved or not (Salesforce prices this way, as you'll see below).
- Per-interaction, per-message, per-reply or per-session. You pay per back-and-forth. Hard to forecast, because you don't know how many replies a month of tickets will need.
- Per-credit. An opaque bundle unit, often multiplied by which model you choose. I've yet to meet a buyer who could turn it into a monthly number on the first read.
- Per-token. The most abstract meter of the lot. Useful to people who work with AI all day, close to meaningless to a CX leader trying to budget.
- Enterprise or custom. No public meter at all (you find out on a sales call).
The further down this list a vendor sits, the harder it is to answer the only question that matters in procurement: what will this cost me?
The survey: how 20 vendors charge
Here's the whole market, grouped by meter. The "what counts" column is where same-rate vendors quietly diverge, and the "catch" column is where the stack hides.
These are list prices on the publish date, so verify against the live page before you sign (pricing moves, and it rarely moves down).
Vendors with a public per-unit rate
Vendor | Meter | Headline rate | What counts as one billable unit | The catch / what's stacked on top |
My AskAI | Per-ticket (usage-based) | ~$0.10–$0.15 per ticket | Every AI reply you can count in your own helpdesk export. Chat: 2 AI replies = 1 credit. Email: first reply = 1 credit, follow-ups 0.5 | Bill stays flat as resolution improves; no vendor-defined "doesn't count" rule; 30-day trial shows your real cost |
Intercom Fin | Per-resolution, renamed per-outcome in late 2025 | $0.99 per outcome | End-to-end AI resolution, plus workflow-driven handoffs added with the rename | Seat fees of $29–$139/mo unless run standalone; 50-outcome monthly minimum standalone; bill rises as you succeed |
HubSpot Breeze | Per-resolution | $0.50 per resolved conversation | A resolved conversation per plan tier | Service Hub Pro at $90/seat (10-seat Enterprise minimum) plus $1,500–$3,500 onboarding; credits don't roll over; HubSpot only |
Fini | Per-resolution | $0.69 per resolution | A resolution by the Sophie agent | $1,799/mo Growth minimum; the free Starter is a sandbox, not a usable trial |
Help Scout | Per-resolution | $0.75 per AI resolution | An AI Answers resolution (~73% average) | A 2025 move to per-contact base billing produced documented 4–5x cost increases for some teams |
Gorgias AI Agent | Per-resolution (automation) | $0.90–$1.00 per resolved automation | An automation: intent recognised, action taken, customer didn't reply | A May 2025 change billed resolutions as both a helpdesk ticket and an AI fee; $1.50 overage; the AI agent is Shopify only |
CoSupport AI | Per-resolution | $0.19–$0.59 per resolution | A resolution | $99–$190/mo base plus a $500–$5,000 setup fee; no Gorgias or HubSpot |
Yuma AI | Per-resolution (tiered) | ~$0.60–$0.70 per resolution ($350–$900/mo tiers) | A resolution | Sales-gated; Shopify and ecommerce only |
Zendesk | Per-automated-resolution plus per-seat | $1.50–$2.00 per AR, plus $50/agent Copilot and ~$50/seat Advanced | An AI-only resolution that stays closed for 72 hours; reopens inside that window don't count | The more efficient your team, the more you pay. Pro-services often run $5,000–$50,000 |
Salesforce Agentforce | Per-conversation | $2 per conversation | A conversation the AI agent actively engages | A broader unit than per-resolution |
Alhena AI | Per-conversation (tiered) | $239/mo Pro for 200 conversations, then $0.83–$1.20 overage | A conversation | Ecommerce-first |
Aissist | Per-interaction, with a per-resolution cap | $0.09 per interaction or $0.60 per resolution, whichever is lower | One automation cycle from user input to AI response. You can't always audit where one cycle ends and the next begins | Per-interaction billing multiplies: a four-reply conversation is roughly four units. A 3,000-interaction free tier softens it |
eesel AI | Per-interaction | ~$0.15 per interaction, plus $239–$639/mo | An interaction | Requires an existing helpdesk, so you pay twice; hard interaction caps |
Freshdesk Freddy | Per-session | $0.49 per session, plus $55/agent Pro and $29/agent Copilot | A session | Sessions don't roll over and previewing consumes paid sessions; Freshworks only |
Wonderchat | Per-credit (per-message) | $29–$299/mo plus message credits | A message credit, multiplied 1–20x depending on the model you pick | The model multiplier makes the headline price misleading |
Vendors with no public rate
These vendors price by sales call. The meter is usually per-resolution or per-outcome, but you can't forecast a number without talking to them, which is its own answer about predictability.
Word-on-the-street puts Decagon and Ada nearer $2 to $3 a resolution, but I'd take that with a grain of salt: none of them publish a rate, so the figures here are just what buyers tell us they were quoted. Treat them as ballpark.
Vendor | Meter | Indicative cost | Notes |
Ada | Per-resolution (enterprise) | $1.00–$3.50 per resolution plus a ~$30,000/yr floor | Sales only, no trial; entry typically $100,000+/yr |
Decagon | Per-resolution (enterprise) | ~$2–$3 per resolution, anecdotal and non-public | Median ACV around $386,000; no public pricing page |
Sierra | Per-outcome (enterprise) | ~$150,000+/yr, outcome-priced | You pay only for customer-agreed outcomes; escalations are free; Sierra itself notes routing is better priced on consumption |
Forethought | Enterprise / custom | ~$59,500 median ACV | No self-serve, no trial, no published pricing |
DigitalGenius | Enterprise / custom | $1,000/mo floor plus $2,500 implementation | Managed service; ecommerce only |
For full disclosure, we're the per-ticket row in that first table. I've put us in the same survey as everyone else on purpose: the meter is the thing to read here, far more than the brand on it.
The definition: what counts as one unit
This is where two vendors at the same rate stop being comparable (it's the layer I'd watch most closely). The clearest example is the word "resolution", which vendors define in ways that quietly change the bill.
Intercom Fin renamed resolutions to outcomes in late 2025, and the new definition counts workflow-driven handoffs to a human as billable, on top of end-to-end fixes. Zendesk only bills an automated resolution that stays closed for 72 hours, so a customer who reopens inside that window doesn't count.
Sierra bills only on outcomes the customer explicitly agrees to and doesn't charge for escalations. Gorgias counts an automation only when intent is recognized, an action is taken, and the customer doesn't reply. Aissist defines an interaction as "one automation cycle from user input to AI response, including all automation steps within that flow" (precise on paper, hard to audit in practice).
Our own definition is, on purpose, the dullest one in the table. A credit is tied to AI replies you can count yourself in your helpdesk export, so there's no vendor-controlled rule about what does or doesn't count.
The stack: what's bolted on top
The per-unit rate is rarely the whole bill. Seat fees show up at Intercom ($29 to $139/mo) and as Copilot add-ons at Zendesk and Freshdesk.
Platform minimums gate the cheaper-looking rates: Fini's $1,799/mo, Ada's ~$30,000/yr floor. Onboarding fees appear at HubSpot ($1,500 to $3,500) and DigitalGenius ($2,500).
Overage rates ($1.50 at Gorgias and Zendesk) bite above your allotment, and several vendors run credits or sessions that don't roll over from month to month. None of that is in the headline rate, and all of it is in your invoice.
What the meter does as you get better
TL;DR: On a success-metered model your bill rises as your AI improves. Across our rollouts (TravelJoy 24% to 80%, Edel Optics to ~79%), a flat per-ticket bill means the cost per resolved ticket falls instead.
The meter you choose decides what happens to your bill as your resolution rate climbs. On a success-metered model, getting better costs more. We've watched this play out across a lot of rollouts.
TravelJoy, a platform for travel advisors running on Zendesk, went from 24% AI resolution with Zendesk's own AI to 80% with us, saving 193 hours a month. Their head of customer service put it like this:
"Our experience with My AskAI has been nothing short of transformative. In comparison to Zendesk's AI agent, we're now achieving an impressive 76% AI resolution rate, versus just 24% before." Alan Pugh, Head of Customer Service at TravelJoy.
Edel Optics, a European eyewear retailer also on Zendesk, climbed from 20–30% to around 79% AI resolution after their own team connected the User Data API, with roughly 3,000 tickets a month now resolved by AI. Kriptomat, a crypto exchange on Intercom, reached 62% AI resolution, up from about 50% at go-live, and chose usage-based pricing after finding per-resolution billing uneconomical for their volume.
Now run those numbers against a success-metered model. At a $1.50 per-resolution rate, Edel Optics' ~3,000 AI-resolved tickets a month is roughly $4,500 a month, and that figure rises as resolution climbs.
A flat per-ticket bill stays put, so the cost per resolved ticket falls as the AI improves. (For the full worked comparison of how each model behaves at scale, the per-conversation versus per-resolution breakdown does the math model by model.)
There's a reason this matters beyond arithmetic. Most of what makes a resolution rate climb is work your own team does: updating knowledge, connecting tools and APIs, tuning guidance, setting up triage, running a weekly review of what the AI couldn't answer. The vendor didn't lift those numbers; you did.
A success-metered model charges you more for improvements your own team created. A usage-based model leaves that upside with you. Usage-based is the boring-but-effective option here, and the headline rate hides exactly why.
How to decode any pricing page this week
TL;DR: Decode any pricing page in four questions: what's the meter, can you forecast your usage of it, what counts as one unit, and what's stacked on top? Model it at your real volume and a higher future resolution rate.
You don't need a finance background to compare these vendors honestly. You need to answer the same four questions for each one, which takes about half an hour per vendor.
- Find the meter. Read past the headline rate to the unit underneath it. Is it a resolution, a conversation, an interaction, a seat, a credit, a token, or a ticket?
- Ask whether you can forecast your own usage of that unit before you sign. You know your monthly ticket count; you don't know your replies per month, your day-one resolution rate, or how many credits a model multiplier will burn. A meter you can't forecast means you only learn your real cost after you've committed, so weight it accordingly.
- Pin the definition in writing. Ask the vendor what counts as one billable unit and what doesn't (reopens? workflow handoffs? escalations? a four-message back-and-forth?). Then ask the procurement question worth more than any rate: what will resolution look like on day one, and how much will it realistically change over a year? On a success-metered model, that answer is the difference between a flat bill and one that doubles.
- Add the stack and re-run the math at your real volume and a higher future resolution rate. Build the blended cost per resolved ticket; the headline rate won't get you there. Model both today and the improved version of today a year out.
If a vendor can't give you a number without a sales call, treat a real free trial that shows your actual cost as the only honest way to see it. That's the reasoning behind our own trial: 30 days free, an in-product cost estimate at the 7-day mark, and the exact go-forward cost by the end. You see the real number while you can still walk away.
When a success-metered model is the right call
TL;DR: Success-metered pricing is the right call when you're already at a high resolution rate on day one, when you want the vendor carrying delivery risk, or for a small copilot-only team.
Per-resolution and per-outcome pricing aren't a trap for everyone, and it'd be dishonest of me to pretend otherwise. There are buyers for whom success-metering is the better, and sometimes cheaper, choice.
If you're already at a high, near-capped resolution rate on day one, there's little headroom for the bill to balloon, and paying per resolution can work out fine. If you genuinely want the vendor carrying delivery risk, an outcome-based model aligns incentives in a way usage-based doesn't.
If your team is small and using AI as a copilot rather than an autonomous agent, a per-seat model can be the simplest thing to reason about. Even per-reply pricing (Aissist's $0.09 interaction with a $0.60 resolution cap) can be the honest model for low-touch, one-and-done conversations, though it turns into the worst of both worlds the moment your conversations run long.
What you should know is which meter you're on, and why. No single one wins for every team.
The takeaway
TL;DR: Read the meter and the definition before the rate, and favor a meter you can forecast (your ticket count) over one you can't (your future resolution rate or token burn). Run the four-question decode on your shortlist before you sign.
Read the meter and the definition first. The headline rate is the easiest thing to compare and the least predictive, because what a vendor counts as a billable unit (and what they stack on top) decides your bill far more than the rate does.
And the meter you can actually forecast, your ticket count, beats the meter you can't, your future resolution rate or your token burn, for anyone who needs a real number before signing.
The framework is three layers: the meter, the definition, and the stack. The action is to run the four-question decode on your shortlist before you commit, at your real volume and a realistic future resolution rate.
If you want help picking between specific models rather than reading specific pages, the per-conversation versus per-resolution comparison is the decision tool that sits alongside this survey. Our own pricing page shows what per-ticket looks like in practice, and you can start a free trial to see your real number.
FAQs
How much does AI customer service software cost?
It runs from about $0.09 per interaction at the low end to $2 to $3.50 per resolution at the high end, plus enterprise contracts from roughly $30,000 to $386,000 a year. The spread is that wide because vendors meter different units. I'd push past the rate to the meter and what it counts, since two vendors at the same rate can bill very differently.
What is the difference between per-resolution and per-conversation pricing?
Per-resolution bills only when the AI successfully resolves an issue, so your bill rises as the AI gets better. Per-conversation bills for every conversation the AI engages, resolved or not. They behave very differently as your resolution rate changes, which is exactly what we compare model by model in the per-conversation versus per-resolution post linked above.
What counts as a "resolution" in AI customer service pricing?
It depends entirely on the vendor, which is the whole problem. Intercom Fin counts end-to-end resolutions plus workflow handoffs; Zendesk only counts an automated resolution that stays closed for 72 hours; Sierra counts only customer-agreed outcomes and doesn't charge for escalations; Gorgias counts an automation when intent is recognized, an action is taken, and the customer doesn't reply. Always ask a vendor for their exact definition in writing.
Why do two vendors with the same per-resolution rate cost different amounts?
Because the rate is only one of three layers. The definition of a billable unit differs (one vendor counts handoffs or reopens, another doesn't), and the stack differs (seat fees, platform minimums, onboarding, overage). Two vendors at "$0.99 a resolution" can land invoices that differ by two or three times once you account for both.
Which AI support tool gives the best value per conversation?
It depends on your resolution rate and what each vendor counts as billable, so a single "best value" number is misleading (it's the question I get asked most). The way to find your answer is to take your real ticket volume and a realistic resolution rate, then build the blended cost per resolved ticket for each shortlisted vendor. Compare those numbers; the headline rate hides the real cost.
Is per-ticket or per-resolution pricing cheaper?
Per-resolution can look cheaper at low resolution rates, but it rises as your AI improves, because you pay more each time it succeeds (we're biased here, but the math is the math). Per-ticket stays flat, so your cost per resolved ticket falls as resolution climbs. For most teams who keep improving their AI over time, per-ticket ends up cheaper on a cost-per-resolved-ticket basis, and far more predictable along the way.
How much can AI reduce customer support costs?
In our rollouts the saving shows up as hours rather than headcount cuts: TravelJoy saved 193 hours a month at 80% resolution, and Edel Optics saved 150 hours a month at around 79%. How much of that turns into a lower bill depends on your pricing model, since a success-metered model claws some of the gain back as your resolution rate climbs while a usage-based one doesn't.
What is outcome-based pricing?
Outcome-based pricing charges you only when the AI achieves a defined result, like a resolved ticket or a saved cancellation, instead of charging for usage. It aligns the vendor's revenue with results, but it also means your bill rises as your results improve, and the vendor controls how an "outcome" is defined. It's one of the success-metered meters in the survey above.
