# Credits Are the New Seats (And They'll Die the Same Way)

> A framework for choosing the AI pricing model that actually fits your product.

- Author: Rishikesh Ranjan · Published: Mar 23, 2026 · Updated: Aug 9, 2026
- Type: Essay
- Tags: Resources, AI, Pricing
- Growth levers: Revenue (primary)
- ~2277 words

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In 2025, the top 500 SaaS and AI companies made over 1,800 pricing changes. That’s 3.6 changes per company in a single year.

Lovable changed its pricing roughly once a month. Launched a Team plan in March, killed it in June, added rollover credits in August, tweaked limits again in September. Salesforce launched Agentforce at $2 per conversation, faced immediate backlash, and overhauled the entire model with “flex credits.” Figma introduced 3,000 AI credits per seat per month. Users quickly figured out that’s roughly 50 prompts. The forums lit up.

![Card titled "The 2025 AI Pricing Chaos", drawn from the top 500 SaaS and AI companies. Four stats: 1,800+ pricing changes; 126% credit adoption year on year; seat pricing declining from 21% to 15%; and hybrid pricing surging from 27% to 41%. Key moments of 2025: Lovable changed pricing roughly once a month, launching a team plan in March, killing it in June, adding rollover credits in August and tweaking again in September, tagged constant churn. Salesforce launched Agentforce at $2 a conversation and backlash forced an overhaul to flex credits, tagged pivot. Figma introduced 3,000 AI credits per seat per month, users worked out that is around 50 prompts and forums lit up, tagged user backlash. Across the industry there were 3.6 changes per company in a single year, credits surged but anxiety followed, tagged panic mode.](https://www.productgrowth.blog/media/posts/ai-pricing-credits-vs-seats-vs-outcomes/01-image.webp)

The industry is in pricing panic mode. And credits, the model everyone’s rushing toward, are the panic response.

Here’s the uncomfortable truth: credits are just seats wearing a different costume. They charge for access (actions taken) instead of value delivered. And just like seats dropped from 21% to 15% adoption in 12 months, credits will face the same reckoning.

The real shift? Outcome-based pricing. But almost nobody can pull it off. Yet.

Let me break down the three AI pricing models, why two of them are broken, and a framework for picking the one that fits your product.

## Seats: Not Dead, But Definitely Dying

Per-seat pricing dominated SaaS for 15 years, and for good reason. Traditional software has near-zero marginal cost. Adding one more user costs basically nothing. So charging per user is clean, simple, and wildly profitable at 80-90% gross margins.

AI broke that math.

Every AI query costs real compute. Every inference burns GPU cycles. AI products see 50-60% gross margins compared to 80-90% for traditional SaaS. When your cost structure changes that dramatically, your pricing model has to follow.

The numbers tell the story. Companies sticking with per-seat pricing for AI products see 40% lower gross margins and 2.3x higher churn than those using usage or outcome-based models. Seat-based pricing as the primary model dropped from 21% to 15% of companies in just 12 months. Hybrid pricing surged from 27% to 41% in the same period.

But here’s where the pricing pundits get it wrong: seats aren’t dead. They’re evolving.

![Screenshot of Notion's pricing page, headed "One tool to run your company" and listing OpenAI, Figma, Volvo, Ramp and Cursor as customers. On yearly billing, saving up to 20%: Free at $0 per member per month for individuals; Plus at $10 per member per month for small teams, adding custom forms and sites, unlimited charts, collaborative blocks and file uploads; Business at $20 per member per month, recommended, adding Notion Agent, AI Meeting Notes, Enterprise Search, SAML SSO, granular database permissions and premium integrations; and Enterprise at custom pricing, adding zero data retention with LLM providers, SCIM user provisioning, audit log and a customer success manager. A panel at the bottom introduces Custom Agents, free to try then $10 per 1,000 credits.](https://www.productgrowth.blog/media/posts/ai-pricing-credits-vs-seats-vs-outcomes/02-image.webp)

Look at what actually happened in 2025. Notion launched AI as an $8/user add-on, then bundled it into Business at $20/user. Slack had a $10/user AI add-on, bundled it into all plans, raised Business pricing. Loom created a dedicated Business+AI tier priced higher than the original plan plus the add-on combined. Atlassian bundled Rovo (originally $20/month) into Jira and Confluence with credit limits, but isn’t even enforcing those limits yet.

These companies didn’t abandon seats. They used AI as the excuse to raise seat prices. Smart? Absolutely. Sustainable? Only if AI remains a feature, not the product.

That distinction matters. When AI is a feature inside a larger product (Notion’s AI writing assist, Slack’s AI channel summaries), seats still work because the seat captures value from the whole product. When AI IS the product, seats collapse under their own weight.

## Credits: The Bridge That’s Being Sold as the Destination

Credits are the hottest thing in SaaS pricing right now. 126% year-over-year growth. 79 of the top 500 SaaS companies now use them, up from 35 at the end of 2024. Figma, HubSpot, Salesforce, Lovable, Cursor, Replit. Everybody’s doing it.

The pitch is appealing: credits give customers the predictability of a license while giving vendors a usage component to protect margins. You buy a bucket of credits, consume them at different rates for different features. A basic API call might use 1 credit. An AI-powered analysis might burn 10. It feels fair.

It isn’t.

Credits have a fatal flaw: they create anxiety instead of value.

Lovable is the cautionary tale. Their pricing changed roughly once a month throughout 2025. They launched a Team plan, killed it three months later, added rollover credits, tweaked limits. Through it all, one theme dominated their Reddit: credit anxiety.

![Screenshot of a Reddit post in r/lovable by yourloverboy66, 7 months old, titled "Is Lovable's pricing model sustainable?", tagged Discussion, with 7 upvotes and 18 comments. It reads that the poster has been building with Lovable for a month and the product is genuinely impressive, but they cannot stop thinking about credits: every modification or bug fix drains the balance, and one day they spent what felt like $5 trying to get a simple API connection working because the agent would say it was fixed, the test would fail, and explaining the issue again cost more credits. They say the constant credit anxiety is killing their creativity and they avoid experimenting because each attempt has a real cost, so they now use Lovable only for initial prototyping before switching to Cursor or MetaGPT X for longer sessions where pricing feels more predictable, and ask how others manage their usage without breaking the bank.](https://www.productgrowth.blog/media/posts/ai-pricing-credits-vs-seats-vs-outcomes/03-image.webp)

“This constant credit anxiety is killing my creativity,” one user wrote. “I find myself avoiding experimentation because I know each attempt has a real cost.” Another: “Lovable purposely keeps their pricing ambiguous so they can tweak what a credit is worth under the hood without facing backlash.”

Figma is heading down the same path. Professional plan users get 3,000 AI credits per month, which sounds generous until you realize that’s roughly 50-70 prompts. For a tool designers live in all day. The Figma Forum is already filling with complaints.

Cursor charges credits at API rates bundled into the seat price. Sounds developer-friendly, but they don’t allow credit rollovers, which has been a consistent source of frustration.

See the pattern? Credits charge for actions, not outcomes. They measure how much you used the tool, not how much value you got. That’s the same fundamental problem as seats, just with extra accounting overhead.

As [Kyle Poyar](https://www.growthunhinged.com/?utm_source=productgrowth.blog) of Growth Unhinged put it: “The more credit models flood the marketplace, the more customers will want to return to simplicity. In 2025 the pendulum swung toward credits. In 2026 it’ll likely swing back toward simplicity and predictability.”

Credits are a bridge. A necessary one, maybe. But the industry is selling the bridge as the destination. And the bridge has termites.

## Outcome-Based Pricing: The Model That Actually Fits AI

If credits are seats in disguise, outcome-based pricing is the logical endpoint of what AI actually does: deliver results.

Instead of charging for access (seats) or activity (credits), you charge for outcomes. A resolved support ticket. A qualified lead. A completed contract analysis. The customer pays for the result. If the AI fails, they don’t pay.

This sounds obvious. So why isn’t everyone doing it?

Because it’s terrifyingly hard.

Intercom is the proof point everyone cites, and for good reason. Their AI agent Fin charges $0.99 per resolution. Not per message. Not per token. Per problem solved. Fin grew from $1M to over $100M ARR, now resolves over 1 million customer issues per week, and Intercom backs it with up to a $1M performance guarantee if resolution targets aren’t met.

Read that again. They’re so confident in their AI that they’ll pay you a million dollars if it underperforms.

![Diagram titled "The AI Pricing Spectrum", subtitled "From consumption-based to outcome-based, where companies land and where they're headed", running from more cost risk on the vendor to more value alignment. Consumption, per token or API call: OpenAI and Cursor, token-based or credit-based API pricing. Workflow, per task or action: Salesforce Agentforce at $2 a conversation, pivoted to flex credits. Outcome, per resolved result: Zendesk at $1.50 to $2.00 a resolution, the first CX vendor to adopt it. Outcome plus, guaranteed performance: Intercom at $0.99 a resolution with up to a $1M performance guarantee. The footer reads that the spectrum runs from charging for access and activity to charging for value delivered.](https://www.productgrowth.blog/media/posts/ai-pricing-credits-vs-seats-vs-outcomes/04-image.webp)

Zendesk followed with $1.50-$2.00 per automated resolution. They were the first in the CX industry to announce outcome-based pricing (August 2024), positioning it as a strategic bet on AI agent quality.

Then there’s what happens when you try outcome pricing without the confidence to back it up. Salesforce launched Agentforce at $2 per conversation. Customers were confused. What counts as a “conversation”? What if the agent fails? The backlash forced Salesforce into a complex overhaul: flex credits, enterprise license agreements, multiple pricing tiers. They went from simple-but-wrong to complex-and-still-confusing.

Leena AI learned a similar lesson from the other direction. They started with consumption-based pricing for their AI employee support agents. Customers became wary of usage, adoption slowed. When they switched to outcome-based pricing, customers could finally see clear ROI, and the business accelerated.

The pattern is clear: outcome pricing works when three conditions are met:

1. The outcome is unambiguous. “Resolved ticket” is measurable. “Better writing” is not.
2. Your AI is reliable enough. Intercom’s Fin handles 80%+ of support volume. If your AI fails half the time, you’ll bankrupt yourself.
3. You can absorb cost variance. Some resolutions take 3 messages, some take 30. At $0.99 per resolution regardless, your margins swing wildly. You need the volume to smooth it out.

Most AI products today can’t meet all three conditions. That’s why credits exist as the middle ground. But make no mistake: the direction of travel is toward outcomes. Every credit system is a temporary answer to the question: “How do we charge for this until we’re good enough to charge for results?”

## The Decision Framework: Which AI Pricing Model Fits Your Product?

Enough theory. Here’s how to actually choose.

![Decision tree titled "Which AI Pricing Model Fits Your Product?" The first question is whether AI is a feature or the entire product. If it is a feature, bundle it into seats: AI enhances the core product, so raise seat prices and add credit limits to cap compute, as Notion, Slack and Atlassian do. If AI is the product, the next question is whether it delivers a measurable, discrete outcome. Yes, at 80% or more success, points to outcome-based pricing, charging per result so the customer pays only when AI delivers value, as Intercom and Zendesk do. No or not yet points to either credits, usage-based with rollover and transparency, as Replit and Cursor do, or hybrid, a base subscription plus usage tiers for AI features, which suits early-stage startups.](https://www.productgrowth.blog/media/posts/ai-pricing-credits-vs-seats-vs-outcomes/05-image.webp)

Your AI is a feature inside a larger product? Bundle it into seats with credit limits. This is the Notion, Atlassian, Slack playbook. AI enhances the core product, seats capture the whole value, credits prevent runaway compute costs. Raise seat prices to absorb the AI cost. Your customers are already paying per seat. Don’t overcomplicate it.

Your AI IS the product, but output quality varies? Credits, but do them right. Learn from Lovable’s mistakes: offer rollover credits, be transparent about what a credit buys, and don’t change pricing every month. Replit moved to usage-based plans and grew from $2M to $144M ARR. It works when customers can see the value scaling with their usage.

Your AI delivers a measurable, discrete outcome? Go outcome-based. But only if your resolution/completion rate is 80%+. If it’s lower, you’ll bleed money on failed attempts. Intercom didn’t launch at $0.99/resolution on day one. They got Fin’s quality right first, then priced for outcomes.

You’re genuinely not sure? Hybrid. Base subscription for access, usage or outcome tiers for AI features. Bessemer recommends this as the effective middle ground for early-stage startups: it provides customer predictability while capturing upside as they scale.

The one rule regardless of where you start: your pricing should move closer to outcomes over time. Credits to workflow-based to outcome-based. That’s the direction. The companies that get there first will have a structural advantage, because outcome pricing creates the tightest feedback loop between product quality and revenue.

As Bessemer puts it: “The charge metric you pick isn’t just a billing decision. It’s a statement about what you believe your AI is worth.”

If you found this framework useful, you might also like my teardown of [how ElevenLabs built a PLG voice platform](https://www.productgrowth.blog/p/how-elevenlabs-hit-330m-arr) that conquered 41% of the Fortune 500. Their pricing evolution is a masterclass in moving from usage to value.

## The Bottom Line

The SaaS industry made 1,800 pricing changes in 2025, and most of them were lateral moves. Swapping seats for credits feels like progress, but it’s rearranging deck chairs. Both models charge for access, not value.

Outcome-based pricing is the only model that truly aligns with what AI does: deliver results. But it requires a level of product confidence that most companies haven’t earned yet. Intercom earned it. Zendesk is betting on it. Salesforce tried and stumbled.

Credits are the bridge. Use them if you need to. But build your product with the goal of crossing that bridge, not living on it.

What pricing model is your product using? I’m genuinely curious. Hit reply and tell me.

## FAQ: AI pricing models compared

#### What are the three AI pricing models?

The three models are seats, credits and outcomes. Seats charge per user for access. Credits charge for actions taken, with different features consuming credits at different rates, so a basic API call might cost one credit and an AI-powered analysis ten. Outcome-based pricing charges for the result delivered, such as a resolved support ticket or a qualified lead, which means the customer pays nothing when the AI fails. The case for the third is that the first two both charge for access rather than value, which makes credits far closer to seats than they look.

#### Why is per-seat pricing breaking down for AI products?

The cost structure underneath it changed. Traditional software has near-zero marginal cost, so one more user costs almost nothing and per-seat pricing works at 80 to 90% gross margins. Every AI query burns real compute, and AI products run at 50 to 60% gross margins instead. Companies that kept per-seat pricing for AI products see 40% lower gross margins and 2.3 times higher churn than those on usage or outcome-based models, and seats as a primary model fell from 21% to 15% of companies in twelve months. Seats still work where AI is a feature inside a larger product, because the seat captures the value of the whole product rather than the AI alone.

#### What is wrong with credit-based pricing?

Credits create anxiety instead of value, because they meter activity rather than results. Lovable is the cautionary case: its pricing changed roughly once a month through 2025, and credit anxiety became the dominant theme on its Reddit, with one user describing how they avoid experimenting because every attempt carries a real cost. Figma gives Professional users 3,000 AI credits a month, which works out to roughly 50 to 70 prompts for a tool designers sit in all day. Cursor bundles credits at API rates into the seat price but does not allow rollovers.

#### When does outcome-based pricing work?

It works when three conditions hold at once. The outcome has to be unambiguous, so a resolved ticket qualifies and better writing does not. The AI has to be reliable enough, which for the Intercom agent Fin means handling more than 80% of support volume. And the company has to absorb the cost variance between a resolution that takes three messages and one that takes thirty, since the price is the same either way. Intercom charges $0.99 per resolution, Fin grew from $1M to over $100M ARR and now resolves more than a million customer issues a week, and Intercom backs the model with a performance guarantee of up to $1M if resolution targets are missed.

#### Which AI pricing model should you choose?

Match the model to what your AI actually is. If AI is a feature inside a larger product, bundle it into seats with credit limits, which is the approach Notion, Atlassian and Slack took. If AI is the product but output quality varies, use credits and do them properly, with rollovers and transparency about what a credit buys. If your AI delivers a discrete, measurable outcome and your completion rate is above 80%, price on outcomes. If you genuinely cannot tell, run a hybrid: a base subscription for access with usage or outcome tiers on top. Whichever you start with, the direction of travel is toward outcomes.

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