👋 Hi, it’s Kyle Poyar and welcome to Growth Unhinged, my weekly newsletter exploring the hidden playbooks behind the fastest-growing startups.

2025 will be remembered as the year when seemingly everybody lost confidence in their pricing. Among the top 500 players in SaaS and AI with transparent pricing, there were more than 1,800 pricing changes in 2025 alone. That’s a staggering 3.6 per company.

I enlisted help from my friend Rob Litterst, co-founder of pricing intelligence platform PricingSaaS, to give readers a tactical rundown of what happened and what (if anything) seems to be working in SaaS pricing. You can go deeper via their new pricing change log, and stay tuned for a full 2025 retrospective later this month.

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Pricing changes used to be big milestones. Every few years, SaaS companies would pony up six-figures on outside consultants, run willingness-to-pay studies, collect usability feedback, and build complicated financial models before rolling out new pricing. 

And the big pricing change? Probably a slight tweak to their Good-Better-Best model with a 10% price increase.

And now: pricing changes have begun to feel like everyday occurrences. 

At PricingSaaS we closely monitor pricing and packaging changes across 500 of the top players in SaaS, and have observed more than 1,800 changes in 2025 alone. 

Take Lovable, the vibecoding darling which recently hit $200M ARR (four months after reaching $100M ARR). They made roughly one meaningful pricing update per month. Among the changes: they launched a Team plan (March), killed the team plan (June), added rollover credits (August), and tweaked starting price and credit limits for Pro and Business (August and September). Even a SaaS behemoth like Salesforce launched AI credits (May), flexible payment options (August), a new user license option (September), and now an agentic ELA (October). 

Of those 1,800 pricing and packaging changes:

Across these changes, we’ve got our eyes on four trends that defined SaaS in 2025, and will continue to shape how AI business models evolve in 2026. For each trend I’ll share real-life examples and guidance to help you evolve your own pricing.

If there's a single pricing trend that defines 2025, it's credits. Out of 500 companies in the PricingSaaS 500 Index, 79 now offer a credit model, up from 35 at the end of 2024 (this is up 126% YoY). Among the new additions are household names like Figma, HubSpot, and Salesforce.

Credits help vendors and customers manage AI economics. They give customers the predictability of a license, while giving vendors a usage component to ensure margins stay intact at scale.

Credits sit in the middle of the spectrum between charging for access and charging for outcomes. They’re more transparent than legacy per-seat licenses (you can see what actions are being taken), but easier to implement and measure than pure outcomes. 

All that said, credits can get complicated quickly. Before implementing a credit model, there are four core questions to think through:

1. What is a credit worth and how does that change at scale?

The simplest approach is dollar-credit parity. Lindy does this cleanly: $50/month gets you 5,000 credits at roughly $1 per 100 credits. It's dead simple for customers to do the math.

From there, most companies introduce volume discounts. Clay offers 2,000 credits for $134 ($.06/credit) versus 50,000 credits for $720 ($.01/credit).

2. What can a credit buy?

Some credit systems are beautifully simple. Take Audible: one credit equals one audiobook. Most SaaS products are more complex.

Clay credits can purchase company revenue lookups, LinkedIn scrapes, email enrichments, and dozens of other actions, each at different credit costs. Lovable credits can make simple design tweaks, add authentication, and build a full landing page, each at different costs as well.

This complexity is a good thing for SaaS vendors, giving you more flexibility and pricing leverage. On the other hand, this can get complex (and annoying) for customers.

3. Where does the credit fall on the value chain?

Credits can map to different points between inputs and outcomes.

Fireworks AI sits at the beginning of the value chain, you're essentially buying LLM infrastructure (tokens). While a company like Reply sits further down. You're paying for contacts actively being nurtured, which sits closer to the business outcome.

Where your credits land on this spectrum affects perceived value and how customers evaluate ROI. The closer to outcomes, the easier the value case, but the more you're on the hook for results. 

4. Is your credit system customer-friendly or vendor-friendly?

This helps shape how your customers use credits. Whether credits rollover, if they pool at the user or org level, and how additional credit purchases are handled can determine how customer-friendly your credit model is. 

A framework for implementing credits

Across the PricingSaaS database, we’re observing a range of credit implementations although most fall into one of the following four buckets. The buckets are based on two variables: (1) how flexible the credit policy is for customers (e.g., rollovers, account-level pooling, top-ups) and (2) whether credits are cost-based (to protect margins) or value-based (to monetize the work delivered by AI). The key driver of cost-based vs value-based is whether the company explicitly charges different prices for different providers, or abstracts the LLM layer and focuses on actions and outcomes. 

Top-left: AI credits as product feature (Value Based, Vendor-Friendly) 

Companies in this quadrant price AI around outcomes and tightly control usage to protect margins. Credits are abstracted away from models, reset frequently, and don’t rollover. This is common among legacy SaaS workflow tools bundling AI into core plans (e.g. Adobe, HubSpot). The goal is to increase product stickiness while avoiding runaway costs. 

HubSpot uses a credit-model to charge for AI features. Core plans come with a bank of credits that reset every month, and more credits can be purchased as needed. HubSpot credit values cost between 10 and 100 credits depending on how much work is being done on the user’s behalf. This gives HubSpot a growth lever outside of seats and contacts without disrupting its existing model.

Adobe falls in this quadrant for Firefly, its in-house AI offering, but they also allow customers to access third-party providers. In that scenario, they act as a marketplace, and pass along the costs from each provider. 

Top-right: AI credits as a creative sandbox (Value-Based, Customer-Friendly)

These companies aggressively abstract cost and maximize flexibility to drive usage, experimentation, and habit formation. Credits are pooled, loosely governed, and framed around what users can build, not what it costs.

This quadrant is dominated by AI-native or prosumer platforms (e.g. Lovable, Replit) optimizing for growth over margin discipline. The bet: higher engagement and retention will outweigh cost volatility. This works best when AI is the primary product value, not a supporting feature.

Bottom-left: AI credits as enterprise infra (Cost-Based, Vendor-Friendly)

Here, credits closely track underlying costs and are tightly governed through caps, add-ons, and admin controls. These companies generally pass through costs from the LLMs, and utilize governance to protect margins and control usage. This quadrant is a fit for developer tools and Enterprise platforms that face high variance usage. 

Cursor credits are charged at API rates as part of the monthly seat price. The seat-based model is easy to digest, and the aggressive pricing on credits means Cursor is removing friction to drive usage. While Cursor allows users to enable PAYG billing if they exceed their usage limits, they don’t allow credit rollovers, which has been a source of frustration for customers. 

Bottom-right: AI credits as a pass-through utility (Cost-Based, Customer-Friendly)

In this quadrant, credits often map to vendors, actions, or data sources, making pricing transparent but unforgiving. These companies accept usage variability, but push cost awareness onto customers. 

PostHog uses credits for PostHog AI, and offers $20 of free credits every month for customers to explore PostHog’s AI features, then offer both PAYG and pre-paid plans for larger credit commitments. They’re transparent about charging a 20% markup on LLM costs, and allow customers with a pre-paid plan to rollover half of their remaining credits to the next contract.

Clay’s entire pricing model is based around credits, with credit value tightly correlated with costs from the underlying data provider. They offer credit rollovers, account-level pooling, and top-ups when customers need more usage.

The Bottom Line

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.

Credit-based pricing was the main headline of last year, but it also helped propel a quieter AI pricing trend: the great re-bundling of AI. 

Some of the biggest names in SaaS launched their first AI product as an add-on, and have since bundled that functionality into their core pricing model. We’re observing companies execute this on two tracks. 

Track A: Bundle AI into existing plans as a feature, often with a price increase. 

Track B: Bundle AI into existing plans using credit limits.

On the first track are Notion, Slack, and Loom. Each had charged between $4 to $10 per user for an AI add-on. Now they're bundling AI and raising prices by between $2.50 and $5 per user.