Within the previous world, “pricing” was a factor you may level at.
A cellphone system value this a lot. A contact heart seat value that a lot. A migration value no matter your endurance may tolerate, multiplied by an hourly price and a obscure apology.
Now we’re pricing work carried out by programs that don’t clock in, don’t ask for coaching, and don’t politely underperform till the third quarter. So we fall again to what procurement understands: seats, tokens, minutes, credit. Items which might be straightforward to bill, even once they’re arduous to imagine in.
That’s the quiet shift Mark Vange, Founder and CTO of Autom8ly, is describing: consumers don’t buy “AI.” They buy the second the work is completed.
What Autom8ly Is Difficult About AI Pricing (and Why It Issues)
Autom8ly’s Mark Vange says token-based AI pricing breaks down in actual shopping for cycles as a result of it’s arduous to see, arduous to forecast, and troublesome to bundle for patrons.
He argues that as AI strikes from “options” to accomplished duties, pricing will shift towards resolution-based items that prospects can predict, approve, and finances—particularly in service supplier channels.
“The ultimate shopper pays for it by end result… it doesn’t matter what number of tokens it takes.”
Why Autom8ly Thinks Final result-Based mostly AI Pricing Beats Tokens for MSPs
Token pricing is rational when you’re constructing an inner mannequin. It’s even rational when you’re an AI group making an attempt to measure prices exactly.
It turns into irrational the second you must promote.
As a result of the unit you’re promoting shouldn’t be “inference.” It’s not “GPU time.” It’s not “tokens.” The client shouldn’t be strolling right into a finances assembly asking for approval to buy an unknown amount of invisible math.
They’re asking for approval to get rid of an issue.
Vange’s framing is blunt: prospects purchase completed work, and the rest is vendor-centric accounting dressed up as transparency.
“If I are available and measure it in tokens… it’s nothing to you.”
What’s taking place here’s a collision between two worlds:
The AI vendor world, the place value is granular, variable, and measured in micro-units.
The service supplier world, the place worth have to be packaged, repeatable, and defensible to a buyer who doesn’t need shock invoices.
That mismatch is why outcome-based pricing is engaging to MSPs and UC suppliers. It permits them to productize AI into one thing sellable. It turns “AI functionality” right into a line merchandise.
And crucially, it makes the dialog boring once more—which is strictly what procurement desires.
10DLC Onboarding as a Priced Final result
Autom8ly’s instance is 10DLC marketing campaign registration within the US—one thing that’s obligatory for A2P SMS, advanced for small companies, and labor-heavy for service suppliers.
The declare is that MSP onboarding for 10DLC can take 4–6 hours of hand-holding, whereas their AI strategy reduces that to a ~5-minute evaluation by the MSP, with ~95% first-time submission success.
As a substitute of charging “for AI,” Autom8ly prices per profitable marketing campaign utility. A hard and fast unit. A predictable SKU.
The deeper level isn’t simply that they automated a kind. It’s that they discovered a unit a buyer can comply with while not having to grasp how AI works.
“The worth is predicated on this way getting submitted and handed.”
Associated Story on CX Immediately
Why Token-Based mostly AI Pricing Fails on the Shopping for Desk (Even When It’s “Truthful”)
Token pricing has a seductive pitch: you pay for what you utilize. It’s measurable. It maps to value.
That’s additionally its downside.
In most real-world shopping for committees, “truthful” shouldn’t be the highest requirement. Controllable is.
Finance groups need:
Forecasting accuracy
Spend governance
Contract readability
The power to check distributors with out studying a brand new unit of physics
Token pricing struggles on all 4.
It’s additionally psychologically backwards. Tokens ask the client to care concerning the vendor’s inner mechanics. However consumers don’t wish to sponsor your infrastructure selections. They wish to pay for their very own outcomes.
Even when distributors try to repair this with dashboards, the dashboards turn into a part of the tax: one other system somebody has to observe to forestall finances surprises.
Final result pricing shouldn’t be excellent, nevertheless it has one huge benefit: it attaches spend to one thing legible. One thing a line-of-business proprietor can level at.
In case your AI can’t be pointed at, it is going to be handled like a variable threat.
“Having the ability to relate that operational value… to outcomes… makes it that a lot simpler so that you can undertake this.”
The UC Angle: Why Final result Pricing Matches Service Suppliers Higher Than Consumption
UCaaS channels stay and die by packaging.
They’re not simply promoting capabilities. They’re promoting deployment, change administration, compliance, adoption, and assist. That’s why the “unit” issues extra for them than it’d for a hyperscaler.
The minute a UC supplier tries to resell token-based AI, three uncomfortable questions seem:
Who carries the overage threat?If utilization spikes, does the supplier eat the price, or does the shopper get the invoice shock?
Who can clarify the invoice?Not “what prompted it technically,” however who can clarify it in a method that doesn’t sound like a shrug.
Who owns the end result?If the AI fails, do you refund tokens, credit, minutes, or one thing else?
Final result-based pricing simplifies all three. It doesn’t get rid of complexity underneath the hood—nevertheless it prevents that complexity from leaking into the shopper relationship.
It additionally aligns with how UC suppliers already promote: bundles, per-site charges, per-location pricing, per-activation providers, onboarding packages, managed providers tiers.
Final result pricing is solely that mindset utilized to AI execution.
“Understanding the use case… contains understanding the economics and the financial levers of that use case.”
What Final result-Based mostly AI Pricing Actually Forces Distributors to Admit
Final result pricing appears like a pricing tactic. It’s truly a product maturity check.
As a result of when you cost for outcomes, you’re not charging for effort. You’re charging for reliability. That forces uncomfortable self-discipline:
You want clearer definitions of “finished.”
You want tighter guardrails on edge instances.
You want operational workflows that scale back exception dealing with.
You want a failure coverage that doesn’t turn into a margin crater.
Token pricing lets distributors cover behind variability. If the shopper complains, you’ll be able to say: utilization went up.
Final result pricing removes that excuse. If the shopper complains, the implied query is: why didn’t the system full the work as anticipated?
That’s why end result pricing, when it really works, is so compelling for channel companions. It turns AI right into a sellable product as an alternative of an experimental functionality with a variable meter operating within the background.
It additionally explains why the market retains circling again to the identical stress: consumers need predictable spend, distributors have variable compute, and neither facet desires to carry the danger alone.
A latest CX Immediately piece framed the parallel downside involved facilities: pricing “seats” for brokers that don’t exist breaks down as autonomous brokers do extra work, forcing pricing groups to rethink the unit of worth. The unit is drifting away from individuals, and towards utilization and outcomes—as a result of that’s the place the work is transferring. Supply.
“It doesn’t matter what number of tokens it takes… the value is predicated on this way getting submitted and handed.”
The Believable Future Drift: “Decision Items” Change into the New SKU
Right here’s the seemingly drift, if Vange is true, and if consumers hold behaving like consumers.
First, the shopper stops asking “what number of tokens” and begins asking “what number of resolutions.” Not as a result of they’ve turn into extra refined, however as a result of they’ve turn into extra drained.
Then the supplier packages AI the best way they bundle the whole lot else: commonplace items, predictable tiers, tight boundaries.
A decision unit turns into a SKU:
One submitted and accepted 10DLC marketing campaign.
One compliant collections name accomplished with required disclosures.
One assist case resolved with out escalation.
One onboarding workflow accomplished and verified.
This gained’t be marketed as “decision items,” not less than not at first. It’ll be marketed as managed automation, or AI-enabled providers, or “brokers.”
However the business logic would be the similar: end result pricing quietly replaces compute pricing, as a result of compute pricing makes the client really feel like they’re paying to your uncertainty.
The darker drift isn’t dystopian; it’s administrative.
We’ll find yourself with contracts that look much less like software program licenses and extra like manufacturing agreements. Outlined outputs, acceptance standards, service credit, and infinite negotiation over what counts as “success.”
And as soon as that occurs, the query gained’t be “how good is the mannequin?” It’ll be “how defensible is the bill?”
Not as a result of consumers dislike AI.
As a result of consumers dislike ambiguity with a fee schedule.
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In the event you’re seeing this pricing shift in shopping for cycles proper now, share what questions your CFO or procurement group is asking—and what solutions they’ll truly settle for.
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FAQs
1) What’s outcome-based pricing for AI?
Final result-based pricing prices for a accomplished job or verified end result fairly than for compute metrics like tokens, minutes, or GPU hours. The objective is to align pricing with what consumers truly worth: completed work. It’s typically packaged as a set price per decision, submission, or accomplished workflow.
2) Why do consumers wrestle to approve token-based AI pricing?
Tokens are arduous to forecast and troublesome to map to enterprise worth in finances conversations. Even when token pricing is “truthful” from a technical standpoint, it might probably really feel uncontrollable to finance groups. That creates friction in procurement and slows adoption.
3) How does end result pricing assist MSPs and UC suppliers promote AI?
Service suppliers want repeatable, packaged affords they will quote, ship, and assist. Final result pricing turns AI right into a predictable SKU that may be bundled into managed providers. It additionally reduces bill-shock threat and simplifies buyer explanations.
4) What’s an instance of outcome-based AI pricing in follow?
Autom8ly costs its 10DLC onboarding automation by the marketing campaign utility end result—successfully a set price per efficiently submitted and accepted registration. That’s simpler for an MSP to resell than variable token consumption. The client pays for completion, not mannequin effort.
5) Is outcome-based AI pricing all the time higher than usage-based pricing?
Not all the time. Final result pricing requires clear definitions of “success,” robust reliability, and settlement on edge instances and exceptions. Utilization-based fashions could be less complicated to implement for distributors, however they typically create forecasting and governance points for consumers.
6) How far may outcome-based pricing realistically go if left unchecked?
If consumers standardize on end result items, AI contracts could begin resembling operational service agreements with acceptance standards, credit, and audited definitions of “finished.” That might scale back ambiguity, nevertheless it may additionally create a brand new layer of contract complexity round measurement and attribution. The “seat” could survive as a legacy wrapper, whereas actual worth shifts into priced outcomes.
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