Hub Spot Switching AI Pricing From Per Use to Per Resolution. But Does It Really Matter? | Saa Str

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Hub Spot Switching AI Pricing From Per Use to Per Resolution. But Does It Really Matter?

by Jason Lemkin | Artificial Intelligence (AI), Blog Posts, Saa Str. Ai

So Hub Spot just made a big pricing change to its Breeze AI agents. Starting April 14, its Customer Agent moves from

On its face, this seems like a pretty big deal. You only pay when the AI actually does its job. No more paying for failed attempts, dead-end conversations, or leads that go nowhere. Hub Spot’s Chief Customer Officer Jon Dick put it well: “You pay when it works, full stop.”

And the numbers back it up so far. Hub Spot says Breeze Customer Agent already resolves 65% of conversations across 8,000+ customers, and cuts resolution time by 39%. Prospecting Agent activations are up 57% quarter-over-quarter. These aren’t bad numbers.

But here’s the thing. Is this actually going to matter in 12 months?

Let’s give credit where it’s due. Hub Spot isn’t pioneering outcome-based pricing for AI agents. Sierra was.

Bret Taylor and Clay Bavor launched Sierra in early 2024 with outcome-based pricing baked in from day one. Their model: you pay a pre-negotiated rate when the AI agent resolves an issue autonomously. If it has to escalate to a human, it’s free. As Taylor put it at Sequoia’s AI Ascent: “If you’re selling software that completes a job, what is the secular business model for that? Let’s pay for a job well done.”

And the results have been staggering. Sierra hit

Taylor has been vocal about why this matters. As recently as his March 2026 conversation with John Collison on Cheeky Pint, he laid it out clearly: the whole market is going to go towards agents and towards outcomes-based pricing. It’s just so obviously the correct way to build and sell software.

And Taylor says something even more provocative that’s worth sitting with: outcome-based pricing gives startups a structural advantage over incumbents. Legacy CX providers face a dilemma because their revenue models depend on seat-based pricing. The more effective their AI becomes, the fewer seats their clients need. That undermines the provider’s own revenue. Sierra, with no reliance on seat-based pricing, has no conflicting incentives. The AI works better, Sierra makes more money.

Intercom Did It, Too. And Grew Fin From

$1M to$

100M+ ARR on It.

Intercom’s Fin agent launched with

The pricing is straightforward:

And just last week, Intercom launched the Fin API Platform, opening Fin up to third-party developers for the first time. It’s available to customers spending at least $250K on Intercom. They’ve built their own in-house LLM called Fin Apex, which they claim outperforms GPT-5.4 and Claude Sonnet 4.6 on resolution rate and hallucinations. CEO Eoghan Mc Cabe isn’t being subtle about the stakes: “If you can’t become an agent company, your CRUD app business has a diminishing future.”

Fin’s resolution rates tell the story of the broader AI improvement trajectory. Fin launched at roughly 27% resolution rates. The average across Intercom’s customer base is now 66-67%. That climb from 27% to 67% in under two years is the whole game.

Salesforce Went a Different Way. Three Pricing Models at Once.

Meanwhile, Salesforce has been on its own wild ride with Agentforce pricing. We wrote about this recently. They shipped three different pricing models in roughly 18 months:

October 2024:

May 2025: Flex Credits at $0.10 per action. More granular. More aligned with actual work done. But still fundamentally consumption-based, still unpredictable for enterprise procurement.

Late 2025: Per-user licenses at $125+/month. Seats become the primary wrapper again. CFOs get a number they can budget. CIOs get a contract shape they understand.

All three run simultaneously now. Many people look at this and see chaos. But I’d argue it might be the smartest thing Salesforce has done with Agentforce. When the market hasn’t converged on how to buy something, you don’t force one model. You let customers self-select.

The result? Agentforce hit $540M ARR by Q3 FY2026, growing 330% year-over-year. 18,500 total deals, 9,500 paid. Only about 8% of Salesforce’s 150,000+ customer base has adopted so far. Having three on-ramps instead of one turned out to be the right call.

And here’s the uncomfortable part for Salesforce: they handled 380,000+ customer support interactions with their own Agentforce agents internally, with 84% fully resolved without human intervention. Across 90 enterprise accounts, they’re already seeing a 10% reduction in seat counts because AI is making service agents more efficient. That’s real revenue compression. Today. Not hypothetically.

Salesforce has to run three pricing models because they can’t fully commit to outcomes-based pricing without cannibalizing their own seat-based revenue. Sierra doesn’t have that problem. Intercom is navigating it. And now Hub Spot is joining that conversation.

Salesforce Now Has 3+ Pricing Models for Agentforce. And Maybe Right Now, That’s The Way to Do It.

Salesforce Now Has 3+ Pricing Models for Agentforce. And Maybe Right Now, That’s The Way to Do It.

Zendesk has gone all-in on outcome-based pricing through what they call “Automated Resolutions.” The model launched at their 2025 Relate Conference as part of the Zendesk Resolution Platform, and as of April 2026 it works like this: every Suite and Support plan includes a baseline number of free automated resolutions per agent per month (varying by tier, up to 10,000 allocated per year). Beyond that, you pay

On top of that, the real AI muscle requires the Advanced AI add-on at

Zendesk also introduced an “AI Dynamic Pricing Plan” for enterprises that lets you shift committed budget between human agent seats and AI-powered automated resolutions as your strategy evolves, without renegotiating contracts. It’s the closest thing in the market to acknowledging the seat cannibalization problem head-on.

One Zendesk customer reportedly burned through a year’s worth of automated resolutions in just a few weeks. And Trustpilot reviews aren’t kind about the layered cost structure: seat fees plus AI add-on fees plus per-resolution fees. As one customer put it, providing only 15 resolution credits as a baseline and then charging on top of a fixed license cost plus a fixed AI add-on cost is “too much.” That’s the double-edged sword of outcome-based pricing layered on top of legacy seat pricing rather than replacing it.

So Here’s the Pricing Landscape as of April 2026

Hub Spot at $0.50/resolution is genuinely the cheapest option in the market right now. That’s a real competitive wedge for SMBs.

But Here’s the Thing. The Math Changes Fast When Resolution Rates Hit 90%

Right now, AI resolution rates in customer support are all over the map. Hub Spot is at 65%. Intercom’s Fin averages 66-67% across its 8,000 customers. Sierra’s best deployments hit 90%. Many early AI agents in 2024-2025 were stuck in the low 20s. At those early rates, per-resolution pricing was a genuine gift to customers. You were essentially saying: “We know this thing fails a lot. We’ll eat the cost of the failures.”

But AI is getting better fast. Gartner projects AI agents will autonomously resolve 80% of common customer service issues by 2029. But it may be faster. Look at the trajectory — and the rate at which LLMs are improving. Top performers are already pushing toward 90% today. Intercom’s own Fin went from 27% to 67% in under two years. Resolution rates grew 52% in just the year between Fin 1 and Fin 2. That trajectory doesn’t stop at 67%.

When resolution rates were 25%, paying per resolution meant you paid for 1 in 4 attempts. That’s a real discount. When resolution rates hit 90%, you’re paying for 9 out of 10 attempts. At that point, per-resolution pricing and per-conversation pricing are almost the same thing.

The delta between “per use” and “per resolution” shrinks to near zero as models improve. It starts to become form over substance.

The Variable Bill Problem Hasn’t Gone Away Either

Per-resolution pricing sounds customer-friendly until the bills show up. Intercom customers running 5,000 AI-resolved tickets monthly pay

Hub Spot’s rate nominally is the best in market at

This is the same tension we saw with usage-based pricing in Saa S broadly. Snowflake, Twilio, the whole consumption-based wave. Wall Street loved it on the way up, punished it on the way down. Customers liked the idea of paying for what they use until they realized they couldn’t predict what they’d use.

Per-resolution AI pricing has the same DNA. It’s aligned. Until it’s not.

All that said, I think Hub Spot is making the right move. Here’s why:

1. It’s a great wedge for new buyers. At

   $0.50/resolution vs. Intercom at$
   0.99, Zendesk at
   $1.50-$
   2.00 plus a $50/agent AI add-on, and Salesforce’s zigzag, Hub Spot is the cheapest option and it’s baked into the platform rather than stacked as an add-on. For an SMB making their first AI bet, that matters a lot. Zendesk’s triple-layer cost structure in particular makes Hub Spot look elegant by comparison.

2. It reduces the “trust gap” for AI skeptics. A lot of potential AI buyers are still scared. They’ve been burned by chatbots that don’t work. Per-resolution pricing gives them a safety net. If it doesn’t work, you don’t pay. Hub Spot is even throwing in a free 28-day trial. For SMBs who haven’t tried AI agents yet, the trial is probably more important than the pricing model itself. Get them in the door. Let them see 65% resolution rates. The pricing is secondary to the habit formation.

3. Sierra proved the model. Now it’s table stakes. Sierra showed that outcome-based pricing can power one of the fastest-growing enterprise software companies ever. Intercom showed it can work layered on top of an existing seat model. Hub Spot is smart to follow. But they’re following, not leading. This would have been a much bolder move 18 months ago.

4. Hub Spot has a data moat argument. Their pitch is that Breeze agents work better because they’re built into the CRM with full customer context, relationship history, the whole deal. Outcome-based pricing is a way of saying “we’re so confident in our context advantage that we’ll bet our revenue on it.” Smart positioning, even if it’s the same argument Sierra, Intercom, and everyone else is also making.

In 2025, This Would Have Been a Much Bigger Deal

If Hub Spot had made this move 18 months ago, when AI resolution rates were 20-30% and everyone was still figuring out if these things even worked, per-resolution pricing would have been a seismic shift. A pure bet-on-ourselves move when nobody else was willing to.

In April 2026, with Sierra at

And here’s the thing Taylor gets right but that cuts both ways: as AI agents get better and better, almost every resolved conversation is a conversation that would have been a successful interaction anyway. The distinction between “per use” and “per resolution” disappears.

The real question isn’t per-use vs. per-resolution anymore. The real question is what happens when resolution rates are so high that the AI is handling almost everything.

Do you go back to flat-rate pricing? Do you charge per seat because the “agent” is now the AI, not the human? Do you just raise the platform subscription and bundle it all in?

Taylor says outcome-based pricing is the end state. He may be right in the long run. But in the medium term, Salesforce’s messy-but-pragmatic three-model approach might be the most honest response to a market that hasn’t figured out how it wants to buy yet.

My bnet: in 2-3 years, per-resolution pricing will be a footnote for most categories. The models will be so good that charging per resolution will be like charging per successful Google search. The success rate will be so high that the distinction between “attempt” and “resolution” won’t carry economic weight.

The Market Has Moved to Outcome Based Pricing in B2B, But It May Not Be as Disruptive As It Sounds

Hub Spot’s move to per-resolution pricing is smart, well-timed for today’s market, and genuinely customer-friendly at $0.50/resolution. It’s the best deal in the market right now for AI agents in the CRM space.

But it’s not the revolution it sounds like. Sierra pioneered this two years ago and built a

And the fundamental tension of variable AI billing remains. As resolution rates converge toward 90%+, the gap between “per use” and “per resolution” disappears. The bills can still be large and unpredictable. And the strategic question of whether outcome-based pricing is genuinely the end state or just a transitional model for a market that hasn’t figured out the right answer yet is still very much open.

Good move. Smart pricing. But probably more form than substance by 2027.

Dear Saa Str: Should Saa S Pricing Be Adjusted for Different Geographical Markets?

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    e Book:  The First $1m ARR
  

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