AI agents break every traditional pricing logic because they also break the fundamentals of how a product behaves. Conventional SaaS products execute a defined task/workflow. AI agents autonomously understand context to identify and execute a series of steps/workflows, tap into external databases for context, and evaluate and amend the output. And just like human operators, no two commands create the same amount of work for an AI Agent.

For example, Intercom’s AI agent performs several tasks, from vector database search to contextual identification to output generation and even output revalidation based on preconfigured business rules. At every step, it pulls in context from several sources, performs LLM functions, and then validates the execution before the final output is delivered to the user.

How, then, do you price a product that changes its function based on the input?

• Per action? Punitive for multi-threaded use-cases
• Per seat? Irrelevant, because agentic AI is designed to replace seats
• A flat fee for unlimited access? Inefficient; individual instances of heavy usage can nuke your margins

Funnily enough, some of the more seasoned AI players are also yet to figure out how pricing works for them. And while the answer will always be subjective to your industry and buyers, this article is expected to provide frameworks to help you select a model that works for you with conviction.

TL;DR: The AI Agent Pricing Decision Tree

(Feel free to use this to support your pricing rationale; attribution appreciated)

But first…

What are AI Agents?

An AI agent is a software program that uses artificial intelligence to perform tasks and achieve goals autonomously, often with minimal human intervention. These agents can reason, plan, and act independently, making decisions and using tools to complete tasks, and can even learn and adapt over time.

An AI agent will autonomously decompose a goal into multi‑step workflows, pull in external context, execute those steps through tools or APIs, validate the result, and iterate until success.

Why is Pricing AI Agents Difficult? (Lessons From Replit and Cursor)

When trying to optimize the pricing model for their AI agents, Replit and Cursor uncovered a few key lessons that detail why pricing AI agents is extremely complicated.

1. Agent Workload Scope Shifts with Context

The scope of every AI agent differs based on its embedded context: industry, task, database, and customers. Intercom’s Fin AI agent operates on clearly defined datasets (the company’s documentation) and solves customer queries within that limited scope (only resolves queries about the associated product). On the other hand, a Replit agent builds complete applications based on the vibe-coder’s whims and context sourced from the entirety of the internet.

The limited scope of Fin AI agent makes support resolutions standardized and measurable in volume and value. Replit Agent’s actions are multi-step, creative, and highly contextualized to each ask.

2. Usage-based Pricing Scales Asymmetrically for Each User

Like AI agents, no two users are the same. And the personal choice of how they interact with AI agents largely determines the resources utilized.

In some cases, users will offer significant context upfront, instigating an agent into a course of action with more direction. In others, users may implement prompt chaining—iterative instructions and layered context in subsequent messages—to receive their desired output.

In both these cases, the costs incurred by the AI agent in solving for the customer vary by a significant margin. Herein, the message determines the medium of charge.

When this user asked the Replit agent to change the color of a specific button of an app it created, the agent tapped into all the chained context of the chat preceding the request and operated on this as an entirely new task, incurring a ~$1 charge for what looked like a simple request.

3. Value Interpretation is Sometimes Inconsistent with Customer WTP (Willingness-to-pay)

When introducing usage limits to what was previously an “unlimited” plan, Cursor wanted to rationalize (pass on) the growing cost of advanced model usage to their customers. In the words of founder/CEO of Ping Chat and self-admitted Cursor investor, Theo Browne, “we’re moving away from loss leaders into a more realistic pricing. And that’s going to screw a lot of people.”

This is also the tremendous reality of AI agents. Getting it to work incurs multiple costs:

• LLM API usage
• Tooling and RAG infrastructure
• Vector DBs, memory, state tracking
• Orchestration, security, and compliance layers

Most agentic AI businesses can’t cleanly wrap it all into one transparent pricing model. Sure, an agent does many things at once and at inhuman speed.

But just because an agent behaves like a high-leverage product, doesn’t mean the buyer is ready to value it that way. Because Cursor is in a market with a lot of competitors, Reddit threads and YouTube videos are “replete” (had to do this, sorry) with discussions on who to switch over to.

How to Choose the Right Pricing Model for Your AI Agent?

The end-state of all pricing is a fine balance between the value your company provides and the value your customers expect you to provide. Emergence Capital and my favorite pricing expert, Madhavan Ramanujan, have a beautiful 2×2 matrix to determine how AI businesses must price themselves.

But today, we are at the very early stages of agentic AI transformation. Much of what your AI agent does and what it is expected to do is still being defined as we go. And honestly, most businesses are pricing their AI agents based on the elasticity of demand and their variable expenses.

“

About eight in ten companies report using gen AI—yet just as many report no significant bottom-line impact… At the heart of this paradox is an imbalance between “horizontal” (enterprise-wide) copilots and chatbots—which have scaled quickly but deliver diffuse, hard-to-measure gains—and more transformative “vertical” (function-specific) use cases—about 90 percent of which remain stuck in pilot mode.

McKinsey & Co June 13, 2025

Therefore, instead of evaluating based only on attribution (how easily customer value can be attributed to your business) or autonomy (the complexity of functions handled by your business), businesses are considering:

• Cost visibility: Can customers intuit your marginal cost?
• Value attribution: How easily can customers tie the agent’s outputs to their outcomes (e.g., revenue earned, tasks performed, or costs saved)?
• COGS margin: How does your LLM and compute cost scale with tasks? (E.g., context-rich and iterative agents cost more than agents with predefined workflows)
• Usage volatility: How spiky and unpredictable is consumption across users and time? (e.g., better estimations of monthly support tickets for businesses vs. app creation requests by retail users)

What are the Most Popular Pricing Models for AI Agents?

AI agents are doing to SaaS what SaaS did to license-based software: changing the value perception. Instead of paying for access, customers expect to use agents to autonomously complete end-to-end workflows. This evolution shifts focus from licensing to product usage, task automation, and measurable outcomes, introducing new possibilities and challenges for monetizing value efficiently.

Over the last few years, four pricing model archetypes have crystallized:

• Usage-based pricing: maps revenue deeply to scaling customer engagement)
• Credit-based pricing: simplifies billing for AI agents with complex usage instances in a single request
• Agent-based pricing: uses full-time-employee (FTE) pricing to rationalize a large, case-sensitive workload into a simple model
• Outcome-based pricing: aligns revenue output to agentic efficiency in delivering real and measurable, customer impact
• Hybrid pricing: Utilizes a combination of different pricing models to help you maintain a predictable revenue axis while retaining usage-pricing orbits

Naturally, while each pricing model has its own benefits, your design choices will also dictate your trade-offs.

With that, let’s dissect every individual pricing model for specifics:

1. Usage-based pricing: Mapping Revenue to Scaling Customer Engagement

When it was first introduced, usage-based pricing generated a ripple effect. In principle, it seemed a sound concept: users only pay for as much as they consume. It threatened shelfware off their shelves and reduced the barriers to entry for price-sensitive buyers.

But usage meters cut both ways. Unless you frame and cap the metric, volatility can spook both Finance and the end‑buyer.
For example, N8n’s value proposition is built on the idea that users should only pay for workflows run instead of the number of tasks the agent may run in the background. This makes ‘usage’ easier to define and the charge against each usage event, incredibly simple to understand for users.

On the other hand, ‘10k workflows’ and other thresholds serve as soft ceilings. It offers users the flexibility to grow product usage without being straitjacketed, and n8n the opportunity to monetize that growth. At the same time, the month-end bill leaves room for ‘sticker shocks’ for customers if usage and real-time expense tracking is not visualized at the point of usage (in-app).

Correspondingly, users can contest the veracity of individual usage events (every active workflow initiated) if they don’t find workflows contributing to direct and quantifiable business outcomes.

Pros of usage-based pricing:

• Built in fairness for customers; buyers see a one‑to‑one link between consumption and cost, minimising shelf‑ware
• Revenue closely tracks COGS since pricing is mostly on a “cost + margin” basis
• Greater usage automatically brings in more revenue without the need for complex upsell motions

Tradeoffs of usage-based pricing:

• Leaves room for high financial unpredictability since ARR cannot be measured
• Without strong thresholds, buyers might face “sticker shocks” when workloads spike
• Low outcome attribution, since buyers have to translate usage to return on investment (ROI) themselves
• Usage metric is rooted in business context; technical metrics require constant customer explanation, while broader metrics might evaporate margins

2. Credit-based pricing: Simplifying Usage-based Pricing for Expansive Agents

Usage-based works when there’s a clean, customer-intuitive unit: tokens, API calls, “activities.” But agentic workloads rarely stay that tidy. A single user intent can fan out into several model calls, tool invocations, RAG lookups, function executions, and follow-ups, each with different cost curves.

Metering every micro‑action and presenting a composite invoice to the user becomes UX torture. On the other hand, exposing complex calculations across several line items opens up room for billing friction.

This is when companies attempt to build an abstraction layer that aggregates heterogeneous costs into a single burnable currency that buyers can buy, track, and refill.

How credits (usually) work:

Customers can buy a block of credits (credit bucket/wallet) from a plan/SKU. Each action performed by the AI agent consumes a separate quota from the ‘credit bucket/wallet.’ The variable amount at which every action consumes credit is controlled by you through a ‘burn table’: the logic applied to every action that determines how many credits (fractional/whole) every action demands.

Credits are usually offered for a pre-defined time frame. Upon the lapse of the time frame, some businesses choose to roll over credits to the next billing period (in case of renewals). Some companies may also choose to expire unused credits. In credit-based pricing, you own the rate card–i.e. how individual costs (i.e., increased LLM costs, advanced model usage, etc.) accrued by your agent for an action/workflow gets translated into a final line item on the bill. This allows you to account for sudden price movements (i.e., increase/decrease in LLM costs) and pass those charges/benefits over to your user.

Devin does this by abstracting compute or AI workload burned in every session initiated by the user into ACUs (Agent Compute Units). While 1 ACU (ideally) ≈ 15 minutes of active agent work, the charge scales with task complexity. Multi-step processes require more credits than a quick bug fix. This sliding burn table lets Devin smooth over cost volatility while still offering enterprises a clear budget ceiling through plan SKUs.

This simplification of pricing, however, comes with a degree of buyer obscurity. Users cannot pre-quantify ACU consumption for every session and must rely on Devin’s backend calculation. In rogue cases of high workload sessions, buyers may contest ACU consumption for individual cases, leading to the need to over-explain individual sessions.

Devin also allows ACU carry-forwards. Essentially, unused ACU credits roll over into the next month and subsequent months, potentially leading to bloated ACU accumulation. This can open situations where buyers have accumulated enough credits to prefer downgrading or abstaining from ACU purchases for the next billing cycle.

Pros of credit‑based pricing:

• Preloaded credit wallets lock in revenue commitments, ensuring no downsides of usage drop-off
• Buyers are insured against bill shocks since they deplete a visible balance instead of getting a surprise bill
• Upgrading becomes a one‑click motion with credit top-ups

Tradeoffs of credit‑based pricing:

• Calculating actual credit usage is complex and layers actual usage across different components (LLM, infra, compute costs, and more)
• Credits obscure actual usage from customers; high-credit usage instances become a point of friction
• Credit rollover management becomes a key consideration; rollovers create accumulation, and unused credit expiry may feel like a tax 

3. Agent-based Pricing: Opening up Untapped HR Budgets

By resolving entire workflows autonomously, AI agents solve the ‘too many cooks’ problem and help them offload a lot of work that needed human intervention. This means businesses can now do the same volume of work (or more) with fewer people. Therefore, it makes sense for agent-based pricing to attack the human resources (FTE) budget instead of the IT procurement wallet.

Agent‑based pricing treats each autonomous agent as a digital employee: you pay a flat subscription for every agent you spin up, no matter how many humans trigger its workflows. At the same time, agent-based pricing may be extremely difficult for the vendor to map when agent actions change with requests and context.

Positioning agents as a rationalization for SDR and AE headcount helps 11x solve the problem of bloated sales teams.

At the same time, no two SDR/AE workflows are similar. Intense workload sessions and low-lift workload sessions may end up being charged the same.

Pros of agent-based pricing:

• Offers cost predictability by charging per agent
• Opens up a new procurement route by targeting the human resource budget instead of a highly competitive IT procurement budget
• Product value scales with usage without penalizing customers

Tradeoffs of agent-based pricing:

• Compute cost may rise with task volume while revenue stays flat; requires strong negotiation upfront to prevent revenue leakage later
• Lightly used agents can feel overpriced, potentially affecting renewals/upsells
• Comes with an expansion ceiling wherein, once core jobs are covered, growth depends on launching net‑new agents

4. Outcome-based Pricing: Charging Customers for Value Delivered, not Derived

Outcome‑based models tie your revenue to metrics your customer already tracks to define success (meetings booked, invoices collected, fraud prevented, automated tickets resolved). Here, instead of metering inputs (tokens, minutes), you meter the result the buyer actually wants.

This shifts the perspective of an AI agent from a tool to a solution. This is exactly the positioning Intercom targets with its Fin AI agent. Intercom enables customers to buy its Fin AI Agent without the native Intercom platform, and simply charges $0.99 each time Fin fully resolves a customer issue.

For products with a quantifiable outcome, this is a great way to attune product value to customer wins. However, identifying customer wins is never straightforward in customer success.

There is no linear path to defining a successful event. In Intercom’s case, customers can indicate that a solution provided by an agent resolves their query either as a positive response or through the lack of a follow-up. It then becomes the vendor’s job to determine the right logic that defines a metric (which in turn defines a billable event).

At the same time, any AI workload consumed by the AI agent in the attempt to initiate a solution goes under-monetized, because charges are directly attributed to outcomes rather than the effort required to get there. 

Pros of outcome-based pricing:

• Month-end bill becomes easy to interpret for the user since it does not surface complex billing math
• Since customers pay for outcome, not usage, product value becomes stickier
• You are able to qualify software value based on end-of-period resolutions delivered

Tradeoffs of outcome-based pricing:

• Constant product proofing is required; non-performance may lead to no revenue
• Potential errors may skew real results (i.e., an agent may close tickets without customer satisfaction, unless guardrails exist)
• Outcome needs to be aligned with the user’s perspective of value (potential questions are raised when solutions are semi-delivered; e.g., do you bill for meetings booked or meetings successfully completed)

5. Hybrid Pricing: Blending fixed-fee Predictability with Usage-based Scalability

A hybrid pricing model typically operates as an expansion of other pricing models mentioned earlier. It involves setting informed usage limits in your public plans or packaging. Once a heavy user zooms past the limit, they are charged at a marginal per-unit charge, thereby maintaining business continuity.

Additional usage (i.e., overages) is usually charged in arrears on contract renewal. At the same time, a fixed-fee entry (either through minimum credit purchases, flat subscription fees, or minimum committed contract value) helps buyers rationalize costs within their procurement budget, instead of having to predict outgo.

Pros of hybrid pricing:

• Fixed fee entry points avoid ‘blank cheque’ fears that pure usage-based pricing may invoke
• Revenue climbs automatically when a user exceeds plan thresholds without the need for human upsell
• Overage tracking acts as proof-of-concept and a negotiation lever to move a customer up the subscription tier
• Risks to revenue predictability are capped through fixed-fee entry points

Tradeoffs of hybrid pricing:

• If the packaging is wrong, users may frequently find themselves under/over limit, leading to a perception of ‘loss’
• Buyers who ignore usage alerts can wake up to a double‑sized bill

Gorgias uses a point-and-click mechanism for customers to build their own plans along with the pre-commissioned units of usage and automation. Once a customer burns past that threshold, they are charged variable usage rates for AI-automated and non-automated support tickets.

How to Select the Right “PRICE POINT” for Your AI Agent?

If pricing models are the rules that govern how value flows between you and the customer, price points are the exchange rate at which that value converts to revenue. Selecting the right number is therefore less art than architecture: it must rest on customer willingness to pay, clear-eyed cost fundamentals, and an adaptive process that keeps both signals current.

Before the astronomical rise of AI agents and remodeled pricing frameworks (usage, outcome, effort-based, and whatnot), AI pricing was an experiment in finding the right dollar value at which large-scale adoption could be delivered.

Here are a few key processes you should consider:

1. Lead with Customer Feedback

Pricing friction is the single clearest signal your market can send about product–market fit. When you surface price sentiment early, before engineering locks in expensive architectural decisions, you risk ending up outside your customers’ willingness to pay spectrum.

• Too low, and you get locked into a potential revenue-leaking ship as usage scales
• Too high, and you end up ceding ground to cheaper alternatives

Ultimately, your pricing also becomes your positioning, and therefore, needs a thorough ICP study and feedback loop.

2. Determine Cost Fundamentals Before Your Price Point Goes GA

No amount of customer enthusiasm can rescue a price that sits below your fully‑loaded cost floor or above a threshold that the market perceives unfair. A robust price point pins down the non‑negotiable minimum, sets a guardrail for margin, and guides how aggressively you can use price as a competitive moat.

Determine your expected costs based on:

1. Baseline: What costs do you expect to incur on a regular basis
2. Spike: Define what spike scenarios you can provision and model the price point based on the expected cost
3. Supplier price hike: Maintain a margin for potential price hikes from your AI suppliers (LLMs, models, etc.)

Remember: entry price anchors perception. A too‑low sticker can trap you in the “cheap automation” bucket, making future step‑ups look like gouging. A too‑high launch price invites low‑cost disruptors before you’ve scaled.

3. Build a Cross-functional Pricing Committee

AI agents are exposed to dynamic shifts in economic policies and technological improvements (model licensing tariffs drop, context windows grow, enterprise buyers demand fresh ROI proof). A siloed PM or CFO can’t keep pace. A permanent, cross‑functional price council ensures every viewpoint—whether cost, value, competition, or customer sentiment—feeds one coherent pricing stance.

In early-stage AI agents, a price committee may operate on a simple model in which decision-makers from individual functions (Product, Engineering, Finance) build an active cost, usage-pattern tracker. For larger organizations, this is what a price committee may look like:

4. Treat Pricing as Dynamic and Iterative

In AI, yesterday’s economics rarely survives the long run. Model inference costs fall, context windows expand, and rivals invent new abstractions (tokens → credits → “intelligence units”).

Locking price points in stone traps you between eroding margins and “surprise” churn when buyers realize better value elsewhere. A living pricing cycle helps you continue to monitor your revenue metrics and keeps your monetization aligned with both value delivered and cost‑to‑serve.

Augmentation, Automation, Disruption: How AI is Changing Pricing Paradigms

To be very honest, AI agents have gone from augmentation to automation pretty fast.

The first generation of AI (generative) answered questions, generated content, and researched on your behalf. The second generation of AI (copilots) contextualized and synthesized these capabilities to solve fragments of a complex workflow. The third generation (agentic AI) marries contextual synthesis of multiple capabilities with a significant amount of autonomy, powerful enough to reframe our understanding of work and outcomes.

In the background, LLM, compute, and AI infrastructure are also changing. Businesses need to constantly be on their toes, ready to change their pricing metrics or models when the market shifts.

Ultimately, ‘work’ becomes less about doing and more about framing the right problem and potential solution; ‘value’ too, swings from process improvements to business outcomes.

As Madhuri Narayana K noted, AI is creating “harder-to-please, incredibly demanding, and yet … uncertain” buyers, and, I would argue, incredibly volatile pricing models. Models like Salesforce and Intercom’s outcome-based pricing, to Replit or Lovable’s effort-based pricing have all happened very recently.

All of them, so far, have found their unsure, uncomfortable seats at the table. And until buyers build a concrete idea of what value means to them, pricing will continue to remain a living, breathing creature that changes with time.

Freepik, Zapier, DeepL, Quillbot and several others have built successful businesses on the back of their ability to shift both product and pricing with the changes in the market.

Tried a wild pricing pivot? Let’s chat! It might fuel the next update.

The post Selling Intelligence: The 2025 Playbook for Pricing AI Agents appeared first on Chargebee Blog.

Krish and Tracy explore how second-time founders tackle pricing strategy, organizational design, and the messy middle of the mid-market—with sharper instincts and fewer illusions.

Prefer to listen? → Catch the full episode here

Why second-time SaaS founders make smarter bets

PlanGrid is a vertical SaaS success story. Founded in 2011, it scaled past $100M ARR and was sold to Autodesk for $875M in 2018.

But when it came time to build TigerEye, Tracy didn’t rush.

Her new company, TigerEye, is horizontal, AI-native, and designed with those lessons in mind—from GTM motion to org structures.

“There are still at least 10 good startup ideas in every enterprise software category,” Tracy says. “And the only people who would have the audacity and experience to go after them are second-time founders.”

Why evolving your SaaS pricing model is harder—but more critical—than changing your price

At PlanGrid, seat- and storage-based pricing delivered 130–140% NRR year after year.

“We had a beautiful land-and-expand strategy,” Tracy explains. “But at the same time, one of our competitors was charging a single price per project with unlimited seats. It was incredibly attractive to buyers.”

PlanGrid’s user-first, best-in-class pricing strategy was surpassed by a competitor’s more top-down approach. As Tracy notes, changing pricing models is much harder than changing price points.

“…the reason it’s hard is because you have to look at what percentage of your current revenue that might not renew because of the new pricing. And you always want to protect every dollar of revenue.”   

Even if your metrics look great, your pricing model might still be leaking deals—because it’s too complex, too demanding for buyers, even when loved by users, or misaligned with how the product spreads in your category. 

Takeaway for RevOps teams: ARPA and NRR tell you what you earned—but not how buyers feel. Go deeper by tracking:

• Where deals slow down due to pricing structure (e.g. license thresholds, approval layers)
• How usage spreads inside accounts—or stalls out as shelfware
• What pricing model lowers perceived risk for your target buyers

Strong monetization metrics can still mask hidden friction. Look for signals of buyer confidence and scalable adoption.

The three distinct flavors of the mid-market segment

Who actually sits in your mid-market segment?

“When we say mid-market—who are you servicing? Enterprise? SMB? Because in the middle are usually a lot of people who are not open to change,” Tracy says.

Tracy breaks the segment down further: mid-market enterprise, mid-market SMB, and what she calls “no man’s land”—a space where companies are too small for enterprise sales but too change-resistant for agile SaaS adoption.

This sharper segmentation helps TigerEye navigate go-to-market tradeoffs and avoid getting stuck chasing deals that won’t move.

How TigerEye does unscaleable things (fast and cheap) to drive customer adoption

TigerEye is rethinking more than just its product. It’s rethinking how a company operates.

By building its own tailored AI models, TigerEye enables fast, personalized answers for its customers—without the overhead of manual research or consulting bandwidth.

Pricing, segmentation, and building AI-native insights 

Hear the full conversation with Tracy Young on:

• Why Tracy founded TigerEye
• How TigerEye’s pricing is centered on simplicity 
• Why the classical definition of mid-market fails startups
• How AI economics will change in the next couple of years
• And more! 

The post TigerEye’s Tracy Young on Enterprise AI, Top-Down Pricing, Mid-Market Mirages, and More appeared first on Chargebee Blog.

Build on Existing Technology, Not From Scratch

Mohsen Ghasempour, Group AI Director at Kingfisher, emphasized that businesses “don’t need to build from scratch” and “don’t need to re-invent the wheel” when implementing AI and other advanced technologies.

This principle applies directly to subscription businesses evaluating new tools. Focus on maximizing your existing technology stack and integrating proven solutions that address specific operational gaps.

Listen to the full Kingfisher conversation.

Why Data Infrastructure Matters 

Before any AI implementation or advanced personalization, subscription businesses must prioritize their product usage data infrastructure. Companies that scale their subscription models invest heavily in data collection, cleaning, and analysis capabilities. This foundation enables accurate churn prediction and effective customer segmentation.

Successful subscription businesses use data to understand customer behavior patterns, identify at-risk subscribers early, and optimize pricing strategies based on actual usage and value perception.

How to Balance Acquisition and Retention

Mark Scott, who scaled Bella & Duke, his pet food subscription, from zero to £50 million, highlighted the balance between acquiring new subscribers and retaining existing ones. This balance becomes more complex as businesses scale, requiring sophisticated approaches to customer lifetime value (LTV) calculation and tracking.

Two key metrics emerged for subscription success:

The R Number: Scott emphasized achieving an R number greater than 1, where retention rates outpace churn rates. This mathematical relationship determines whether a subscription business will grow or face the “Death Curve” of declining subscriber bases.

Lifetime Value Optimization: Accurate LTV tracking enables better acquisition spending decisions and helps identify which customer segments drive the most long-term value.

Listen to the full Bella & Duke conversation. 

Why Traditional CRM Fails for Subscriptions

Ammar Qureshi from Babbel provided insights into managing subscriptions with highly diverse user needs. Language learning subscribers have different learning styles, schedules, and goals, making personalization critical for retention.

The key lesson for all subscription businesses: Traditional CRM strategies often fall short for subscription models. Subscription businesses need systems that can handle complex customer journeys, multiple touchpoints, and ongoing engagement rather than one-time transaction relationships.

Listen to the full Babbel conversation.

When to Use Tiered vs Usage-Based Pricing

Marie Goddard from the Financial Times addressed the complexity of subscription pricing models. Her insights from publishing translate directly to other subscription verticals:

Tiered vs. Usage-Based Models: Understanding when each pricing structure works best depends on customer behavior patterns and value perception. Some subscribers prefer predictable monthly costs, while others want to pay based on actual consumption.

Multiple Pricing Structures: Advanced subscription businesses often manage multiple pricing models simultaneously, requiring billing systems that can handle this complexity without creating operational overhead.

Listen to the full Financial Times conversation.

Excellent resource: Mastering Hybrid Pricing: Usage-Based Billing + Subscriptions = The Future

AI-Driven Personalization Across Channels

Subscription businesses operating across multiple channels need personalization strategies that work seamlessly everywhere customers interact with their brand. Ghasempour’s work at Kingfisher shows how subscription models can strengthen traditional retail by creating personalized experiences that span digital and physical environments.

This strategy proves particularly valuable for subscription businesses in beauty, fitness, and home improvement, where ongoing digital relationships can enhance and inform in-store experiences. Customers receive consistent, tailored interactions whether they’re browsing online, visiting a store, or engaging through their subscription portal.

What’s Next for Subscription Growth

These foundational principles remain constant: prioritize data infrastructure, balance acquisition with retention, implement proven technologies rather than building from scratch, and maintain focus on customer value creation.

Subscription businesses that master these fundamentals position themselves for sustainable growth, regardless of market conditions or technological changes.

The post How to Scale Subscription Revenue: Proven Insights from SubscriptionX 2025 appeared first on Chargebee Blog.

In this special episode of the Second Acts podcast, Chargebee CEO Krish Subramanian sits down with CMO Guy Marion to explore what separates companies that stall out from those that scale up. The conversation covers everything from AI pricing strategy and org design debt to Zapier’s bold monetization experiments and the risk of treating pricing like a yearly planning exercise.

This blog breaks down the highlights. Prefer to listen? Catch the full episode here.

What defines a second act

Your first growth curve won’t last forever. Whether it’s building a product-led activation experience, an outbound sales team, or a flagship product line, eventually, the momentum fades.

Second acts are about layering on new growth motions: new products, new markets, or new operating models. But sustaining that growth requires both a beginner’s mindset and organizational speed.

Instead of siloing innovation, Krish emphasizes embedding experimentation across the company and rethinking incentives to match.

Why pricing is now a product

AI is reshaping both the value customers get and the costs companies must incur to deliver that value. That makes pricing a strategic product in and of itself, not a financial exercise.

Krish is direct: If your pricing strategy still relies on annual reviews and gut instinct, you’re already behind. Leading companies treat pricing like software—tested, iterated, and measured.

Instrumentation is your new moat

You can’t price what you can’t measure.

As AI adds variable delivery costs, companies must track:

• Which features customers use
• What those features cost to support
• How usage maps to value perception

According to Chargebee’s 2025 State of Subscriptions & Revenue Growth report, companies that implement pricing changes within a month see 2x the revenue impact. 

What leading companies are doing

Leading companies are already embracing rapid monetization experiments:

• Zapier: Shifted from flat-rate pricing to output-based pricing for AI agents
• Salesforce: Now charges per AI-resolved support case
• Personio: Modular product rollout to accelerate land-and-expand in SMB

Culture beats strategy

When growth slows, old playbooks lose effectiveness. The ability to evolve isn’t just strategic—it’s cultural.

Krish emphasized that execution accelerates when teams aren’t bogged down by hierarchy but empowered to solve what he calls inspired problems.

He highlights three enablers of that kind of environment:

• Creating space for experimentation
  
• Framing work as inspired problems, not tasks
  
• Scaling org design to support faster iterations

The role of luck in scale

Krish credits a mix of deliberate design and serendipity. From UberPool encounters with future execs to a move to Europe driven by visa constraints, momentum came from staying in motion.

Listen to the full episode

Hear the full conversation with Krish and Guy on pricing in the age of AI, scaling org design, and building growth engines that last.

The post How Today’s Winners Are Building Their Second Acts appeared first on Chargebee Blog.

• On July 8, 2025, the Eighth Circuit vacated the FTC’s Negative Option Rule, which included the click-to-cancel requirement.
• This eliminated the July 14, 2025, compliance deadline due to procedural errors in the rulemaking process.
• State automatic renewal laws remain in effect, and subscription businesses should maintain customer-friendly cancellation processes as a competitive advantage rather than a regulatory requirement.

The Eighth Circuit eliminated the July 14, 2025,  compliance deadline for the FTC’s Negative Option Rule. On July 8, 2025, the U.S. Court of Appeals for the Eighth Circuit vacated the Federal Trade Commission’s “click-to-cancel” rule just days before enforcement began.

The ruling provides immediate relief from federal compliance requirements, but subscription businesses still face state law obligations and subscriber expectations around easy cancellation.

Why Did the Eighth Circuit Vacate the FTC Negative Option Rule?

The Eighth Circuit invalidated the rule on procedural grounds. The court found that the FTC failed to follow the required procedures under Section 22(b)(1) of the FTC Act, 15 U.S.C. § 57b-3(b)(1), which mandates a preliminary regulatory analysis when a proposed rule amendment has an expected annual economic impact of at least $100 million.

The FTC initially estimated the rule’s impact below $100 million. The court found that the agency should have issued the required analysis when the rulemaking process revealed that the actual implications exceeded this threshold and that the failure to do so deprived stakeholders of the opportunity to address the Commission’s cost-benefit analysis.

This procedural error, not disagreement with the rule’s content, caused the change.

What Subscription Laws Remain in Effect After the FTC Ruling?

The ruling does not affect existing laws and requirements.  

State Automatic Renewal Laws Continue Operating: State automatic renewal laws remain unchanged and often impose requirements similar to the vacated federal rule. California’s updated automatic renewal law took effect July 1, 2025, requiring easy cancellation processes and restricting retention offers that impede cancellation. Colorado’s recently amended law extends automatic renewal regulations to business-to-business transactions.

These state requirements create ongoing compliance obligations across different jurisdictions.

Federal Consumer Protection Authorities Remain Available: The FTC retains enforcement capabilities under existing consumer protection laws, though the agency’s approach may shift under current leadership.

What Is the FTC’s Next Move on Subscription Cancellation Rules?

Chairman Andrew Ferguson, who opposed the rule’s adoption in November 2024, has indicated a preference for enforcement over rulemaking. While the FTC could seek rehearing, appeal the decision, or restart rulemaking with proper procedures, current leadership appears focused on using existing authorities rather than developing new comprehensive rules.

Why Should Subscription Businesses Still Make Cancellation Easy?

Regulatory requirements aside, straightforward cancellation processes deliver measurable business benefits. Companies that implement transparent, flexible cancellation options often see higher customer satisfaction and increased likelihood of subscriber returns.

The fundamental principle holds: customers appreciate transparency and control over their subscriptions. This approach builds trust and often results in longer customer relationships.

How Should Subscription Businesses Respond to the FTC Ruling?

• Maintain Compliance Readiness: State law requirements vary by jurisdiction. Maintaining practices that meet higher standards simplifies operations and reduces compliance complexity as you scale.
• Address Customer Expectations: Consumers increasingly expect easy cancellation options regardless of legal requirements. Meeting these expectations reduces friction and negative customer experiences. See how Condé Nast does it. 
• Prepare for Future Regulatory Changes: The FTC could restart rulemaking with proper procedures, or new legislation could emerge. Businesses with established customer-friendly practices adapt more easily to regulatory changes.

What Steps Should Subscription Companies Take Now?

• Review Current Cancellation Processes: Evaluate your cancellation workflow against state law requirements in your operating jurisdictions. Identify any friction points that could create customer frustration.
• Assess Technology Capabilities: Ensure your billing systems can process immediate cancellations and provide explicit confirmation to customers. Integration capabilities with your customer communication systems become essential for managing the post-cancellation relationship.
• Develop Retention Strategies: Focus on creating value that makes customers want to stay rather than barriers that prevent them from leaving. Consider pause options, plan modifications, and other flexibility features that address common cancellation triggers.

What Does the Future Hold for Subscription Cancellation Requirements?

The Eighth Circuit ruling removes immediate federal pressure but doesn’t change the business case for customer-centric cancellation policies. State laws require many of the same practices, and customer expectations around cancellation ease continue evolving.

Subscription businesses can use this period to implement thoughtful cancellation processes that prioritize customer experience and regulatory compliance. This approach builds competitive advantages through superior customer relationships rather than necessity-driven changes.

The most effective retention strategy focuses on delivering consistent value rather than creating exit barriers. That principle drives sustainable growth, whether federal rules require it or not.

Turn Compliance Into Competitive Advantage

While regulatory uncertainty settles, focus on what you can control: creating cancellation experiences that build trust and gather actionable insights. Chargebee Retention helps you implement compliant, customer-friendly cancellation workflows that capture valuable feedback and identify retention opportunities before customers leave.

Our platform integrates with your existing billing system to process immediate cancellations, present thoughtful pause options, and gather churn insights without creating friction or compliance risk.

The post Eighth Circuit Vacates FTC’s Negative Option Rule: What Subscription Businesses Need to Know appeared first on Chargebee Blog.

• AI has gone mainstream. 52% of businesses in the 2025 State of Recurring Revenue & Monetization report are using AI capabilities, and 41% are implementing them within 12 months, making it their top 2025 investment priority.
• But for the subset of companies building and selling AI-enabled products—what we call “AI Builders”—monetization remains fragmented: 29% bundle AI free, 24% charge a premium, and 11% are still figuring it out.
• These AI Builders face unique revenue challenges that internal AI users don’t encounter. The core challenge: 41% struggle with cost-effective scaling, and technical barriers prevent value-based pricing.
• Despite 70% of companies raising prices and 77% changing pricing models in 2024, 40% of these changes failed to improve customer alignment.

Companies restructuring pricing to capture AI value are winning; those unwilling or unable to evolve their pricing are falling behind.

How Companies Are Monetizing AI in 2025: Upsells, Premium Add-Ons, and More

The numbers reveal AI’s mainstream status: 52% of businesses have deployed AI capabilities, and 41% will add AI features within 12 months. Companies are backing this adoption with budget dollars, making AI their dominant investment priority for 2025.

Here’s how companies are currently approaching monetizing their AI offerings:

• Incorporating AI within existing packages at no extra charge (29%)
• Offering AI as a premium feature with additional cost (24%)
• Offering AI as paid add-ons (20%)
• Creating separate AI-focused product lines (14%)
• Still evaluating monetization strategies (11%)

These strategies show how companies are balancing quick revenue wins with widespread user adoption—decisions that directly impact long-term growth and market position.

AI vs. Traditional SaaS Economics: What Makes Pricing So Hard?

It’s no surprise that AI disrupts traditional software economics. Unlike standard SaaS with 90% margins and near-zero marginal costs, AI requires real computing resources for each customer interaction, creating typically lower gross margins.

The Two Biggest Obstacles to Monetizing AI Profitably

The monetization divide is amplified by a critical communication challenge: 41% of companies struggle to balance development costs with pricing strategy, while 22% struggle to quantify AI feature benefits. Communicating competitive differentiation is next at 21%.

Technical obstacles worsen these communication barriers. Billing accuracy issues (37%), metering limitations (38%), packaging inflexibility (35%), and technical dependencies (28%) all prevent companies from implementing value-based pricing effectively.

Despite 70% of companies raising prices and 77% modifying pricing models in 2024, 40% of these changes failed to improve customer relationships. Companies adopting usage or outcome-based models face the highest risk when they can’t clearly demonstrate how AI capabilities drive business results.

How Outcome-Based Pricing Drives AI Monetization Success

High-performing organizations address this by focusing on customer outcomes rather than features. As one leader states, “We measure ourselves by how much time we save their teams, how many errors we eliminate, how much they can do with fewer people.”

This creates win-win partnerships: vendors succeed when customers achieve results. Pricing becomes about value delivered, not costs justified.

AI Pricing Strategy Checklist: 3 Questions Every Company Must Answer

Our data reveals that the ability to adapt pricing flexibly — which requires executive alignment, solid business strategy, and proper tooling — has become a core competitive advantage. Companies that can quickly restructure pricing to capture AI value are pulling ahead, while those maintaining (or stuck with) static pricing approaches risk being left behind.

Ready to see where your company stands? Get the 2025 State of Recurring Revenue & Monetization report to get the full research findings and data from 473 companies reshaping revenue growth.

The post The Monetization Reality appeared first on Chargebee Blog.

The First Three Months Determine Subscription Success

Cafeyn is a pioneer and leader of digital information streaming in Europe. Recently, Cafeyn discovered something crucial in its subscription data: churn behavior fundamentally changes after three months. “Our data shows that the first three months after the trial are when we really focus on building loyalty,” Steijns explained. “After that three-month mark, churn stabilizes at a very healthy level.”

This insight led to a strategic shift. Rather than spreading retention efforts across the entire customer lifecycle, Cafeyn concentrated resources on those critical first 90 days when user behavior patterns solidify.

Defining Subscriber “Aha Moments” With Precision

Many subscription businesses discuss “aha moments,” but Cafeyn quantified theirs with specific metrics. Data analysis revealed that users who record sessions of 15 minutes or longer within their first three months churn far less.

Cafeyn’s data team analyzed user behavior patterns to understand when their digital magazine platform truly “clicked” for subscribers. The 15-minute threshold represents the point at which users move from casual browsing to engaged reading, transforming from trial users to committed subscribers.

Moving Beyond Generic Nudges to Personalized Retention Interventions

Once Cafeyn identified their 15-minute “aha moment”, the challenge became guiding users who missed it during their trial period. Working with their CRM and data teams, they developed personalized communication strategies based on individual usage patterns.

The approach considered multiple data points:

“We’re working with our CRM and data team to figure out how we can use each user’s usage data to nudge them towards that 15-minute session,” Steijns noted. This personalized approach moves far beyond generic “we miss you” emails to behavior-based interventions.

Voluntary vs. Involuntary Churn: What The Payment Data Revealed

Cafeyn also uncovered an important insight about payment failures that many subscription businesses overlook. In the Netherlands, where direct debit is common, they noticed users deliberately declining payments through their banking apps—a behavior that initially appeared as involuntary churn.

“These users are expressing that they don’t want to continue doing this payment for some reason,” Steijns explained. “In many cases, it’s users who want to cancel the subscription but think just declining the payment is an easier way for them to cancel.”

This revelation created a retention opportunity. What looked like payment failures were actually voluntary cancellation attempts, giving Cafeyn a chance to engage with targeted retention offers before losing these subscribers entirely.

Proactive Payment Failure Prevention Strategies

For genuine involuntary churn, Cafeyn implemented proactive strategies based on failure trend analysis:

Expired Credit Cards: Automated reminders are sent one month before expiration, allowing users to update payment information seamlessly.

Insufficient Funds: Flexible billing date options, particularly valuable for lower-priced subscriptions where timing matters for monthly budgets.

Payment Method Changes: Quick resolution paths for users experiencing temporary payment issues.

These strategies work because they address root causes rather than just symptoms. By analyzing failure patterns over time, Cafeyn could anticipate problems and prevent churn before it occurred.

AI-Powered Churn Prediction and Customer Retention

Looking ahead, Cafeyn is exploring AI applications that combine disparate data sources—usage analytics, customer service interactions, and acquisition channel data—into comprehensive customer profiles with churn risk scores.

“With all the things happening with AI and in the many data sources we have, this is a huge opportunity,” Steijns shared. The goal is to create retention offers tailored to individual user contexts, moving beyond one-size-fits-all approaches.

However, Steijns emphasized the experimental nature of this work: “We are at the very beginning of tying those data sources together.”

Key Takeaways for Subscription Businesses

• Focus on Critical Windows: Identify when churn patterns stabilize in your business model and concentrate retention efforts on the periods that matter most.
• Quantify Success Moments: Define your “aha moment” with specific, measurable criteria rather than abstract concepts.
• Personalize Based on Behavior: Use actual usage data to create targeted interventions for users who haven’t reached engagement thresholds.
• Investigate Payment Failures: Analyze involuntary churn trends to distinguish between technical failures and intentional cancellation attempts.
• Experiment With AI Applications: Start small with AI-powered retention strategies, focusing on combining existing data sources for better customer insights.

Building Retention Into Your Growth Strategy

Cafeyn’s approach demonstrates that effective churn reduction requires strategic focus and operational precision. By concentrating on their three-month window, defining clear engagement metrics, and personalizing retention efforts, they created a systematic framework for building subscriber loyalty.

The key insight is that effective retention requires understanding user behavior patterns and creating interventions that guide subscribers toward long-term engagement.

For subscription businesses looking to improve retention, Cafeyn’s strategy offers a clear framework: focus resources on critical periods, define success metrics precisely, and use data to create personalized experiences that turn trial users into loyal subscribers.

To learn more, read Cafeyn’s case study.

The post The Critical Three-Month Window: Cafeyn’s Strategic Approach to Early Churn Prevention appeared first on Chargebee Blog.

• AI has become the dominant investment priority, with 77% of companies that participated in the 2025 State of Recurring Revenue and Monetization report ranking it #1 (up 67% from 2024) – more than double the combined total of CRM, business intelligence, and billing solutions.
• While 25% of AI adopters forecast 50%+ growth, only those aligning AI implementation with core business objectives and monetization strategy are actually hitting their numbers.

The Innovation Gap: Why AI Investment Doesn’t Always Drive Revenue

This insight explains the disconnect we’re seeing in AI investment patterns and revenue realization across the market. Companies are investing in AI, but many of those who are embedding AI within their products or selling AI-enabled solutions are struggling to make real money.

The challenge isn’t building AI capabilities—it’s translating those capabilities into sustainable revenue streams that customers will actually pay for.

AI Tops 2025 Tech Investment Priorities 

AI has officially won the investment priority battle for 2025. 77% of companies identify AI as their top investment priority, more than double the combined total of those selecting CRM, business intelligence, and billing solutions (30.5%).

This focus represents a 67% increase from 2024, signaling that AI capabilities are now considered critical for competitive advantage.

Four AI Investment Strategies: Which One Matches Your Revenue Goals?

Our research identified four main ways AI building companies are investing differently:

1. Operational Efficiency (32%): Targeting internal process improvements and cost reduction to free up resources for growth initiatives and improve profitability.

2. Market Analysis (21%): Supporting growth strategies through enhanced market insights, enabling better targeting, product development, and competitive responses.

3. Product Feature Upgrades (19%): Enhancing existing customer-facing capabilities and value propositions, potentially justifying premium pricing or increased adoption.

4. AI-First Offerings (16%): Developing entirely new AI-centric products and services, opening new revenue streams, and potentially establishing market leadership.

The monetization disconnect appears most clearly here: companies investing in operational efficiency show the lowest revenue growth correlation, while those developing AI-first offerings demonstrate the highest growth potential but often struggle with pricing strategy alignment.

What Leading AI Product Companies Prioritize to Drive Growth

AI building companies (companies that are building and selling AI-enabled products, not just using AI for internal purposes) are aligning implementation with core business priorities, recognizing that AI’s value lies in driving measurable outcomes rather than just technological advancement:

Enhancing Customer Experience (53%): Strategic AI deployment to improve interactions, build loyalty, and drive customer lifetime value.

Improving Customer Retention (51%): Utilizing AI to understand customer behavior, predict churn, and proactively strengthen relationships.

Driving Customer Acquisition (50%): Leveraging AI to identify new opportunities, personalize outreach, and increase conversion rates.

How AI Is Reshaping Go-to-Market Strategy and Market Access

AI’s greatest potential may be opening up new paths to market that didn’t exist before. Venture capitalist Tomasz Tunguz notes, “AI enables sales development-led experiences in market segments that historically have not been economically viable.”

This creates both opportunity and challenge. Companies can now address market segments previously too expensive to serve, but they need pricing models designed for these new realities.

AI Monetization Strategy Checklist:

If you’re among the 77% of companies prioritizing AI investment, assess these critical questions:

Companies that align AI investment patterns with appropriate monetization strategies have the best opportunity to capture the expected returns. Those who don’t will risk joining those who invest heavily but fail to realize proportional revenue growth.

Ready to see where your company stands?

Get the 2025 State of Recurring Revenue & Monetization Report to receive the full research findings and data from 473 companies reshaping revenue growth.

The post The AI Effect: Why Tech Spending Alone Won’t Drive Revenue appeared first on Chargebee Blog.

This scenario plays out daily at businesses with a recurring revenue model worldwide. It’s why G2’s Summer 2025 rankings matter—they reflect how businesses solve real revenue challenges under real pressure.

The Numbers Tell Our Story

Chargebee maintained its #1 position across every core subscription category in G2’s Summer 2025 reports:

Category Leadership

• Subscription Billing: #1 ranking
• Subscription Management: #1 ranking
• Subscription Revenue Management: #1 ranking
• Subscription Analytics: #1 ranking

Market Recognition

• Featured in 134 reports
• Earned 63 badges
• Ranked #1 in 40 reports

What Enterprise Support Actually Means

Our Best Support Enterprise Badge reflects more than quick response times. It measures three critical factors: ease of doing business, quality of support, and likelihood to recommend.

When enterprise customers rate us #1 for renewals and user satisfaction, they’re measuring our ability to scale with their business complexity. They’re evaluating whether we solve problems before they cascade into revenue issues.

Performance That Drives Business Results

G2’s methodology captures what matters most to subscription businesses:

Beyond the Badges: Real Customer Validation

G2 rankings aggregate thousands of verified user reviews, where our customers describe specific challenges we’ve consistently solved:

• Complex multi-entity billing across global subsidiaries
• Revenue recognition compliance for public companies
• Pricing experimentation without technical debt
• Automated dunning that recovers revenue without damaging relationships

Read the latest customer reviews on G2 to see how businesses describe their experience.

The Platform That Grows With You

Six years of consecutive G2 leadership reflect our commitment to evolving with recurring revenue and subscription business needs. While other vendors focus on basic billing, we’ve built a platform that anticipates the challenges of scaling subscription revenue at every stage of growth. Whether you’re an AI startup figuring out how to monetize your offering,  preparing for Series B funding, planning international expansion, or navigating compliance requirements, your billing infrastructure should accelerate growth, not constrain it.

The post Chargebee Dominates G2’s Summer 2025 Rankings: #1 Across All Core Subscription Categories appeared first on Chargebee Blog.

Then there’s billing, often treated like a back-office function. Something to “just wire up” with a gateway or push off to finance.

But billing doesn’t sit quietly in the background anymore. It shapes how fast you can launch pricing changes, how quickly your GTM scales, how accurately you report revenue, and how much engineering you burn every time something shifts. Especially in AI, where monetization models evolve faster than most stacks can keep up, billing has moved from admin to infrastructure.

Why Billing Can’t Be an Afterthought for AI Startups

Most early teams either hack an in-house solution or bolt thin logic onto a payment gateway. Both work, until they don’t:

• Weeks to launch new pricing because engineering owns the billing code
• Finance loses trust in revenue data, delaying forecasts and audits
• Sales cycles stall when CPQ and billing don’t align
• Support team firefights payment failures that automation should handle
• Product team babysits entitlements instead of shipping features

These cracks appear precisely when you’re scaling: new products, new regions, new funding. Rebuilding billing mid-flight drags growth at the worst moment. Savvy AI teams treat billing as infrastructure from day one.

What You Get Right When You Start with Chargebee

1. Support for Complex Billing Without Slowing You Down

In AI businesses, billing complexity builds up faster than expected. You start with a simple subscription, then layer in feature-gated access, usage metering, entitlements, and usage tracking, and custom contracts. Before long, finance is buried in spreadsheets, support is swamped with payment issues, and engineering is fielding tickets that don’t belong on their roadmap.

This complexity isn’t a sign of failure; it’s a sign of growth. But unless you have the right infrastructure, it turns into operational drag.

How Chargebee helps

Chargebee gives you the tooling to handle complex, evolving billing operations without spinning up a billing team or patching brittle workarounds. You can:

• Track usage and entitlements natively and map them to plan structures
• Automate invoicing, proration, and billing cycles across different contract types
• Manage provisioning, upgrades/downgrades, and discounts without custom code
• Align revenue recognition automatically, even across hybrid models and multi-entity setups
• Build workflows on top of the billing platform via webhooks, rules, and APIs

This results in fewer manual reconciliations, minimal to zero support escalations, and more time spent shipping products rather than fixing billing.

How Phrase scaled global billing with a lean 2-person team

Phrase, an AI-driven localization platform, manages billing across multiple geographies and product lines with just two FTEs in Billing Ops and Chargebee.

“Chargebee allowed us to unify product provisioning without engineering effort. It’s been a game-changer operationally.” 

• Martin Konop, CFO, Phrase

2. Pricing Agility That Matches AI’s Tempo

AI companies constantly evolve their pricing to reflect value, match competition, or drive adoption. You might start with per-seat pricing, then shift to usage-based or experiment with outcome-based or hybrid pricing for enterprises. But every pricing change, if not handled right, creates a domino effect: checkout flows break, invoices misalign, and legacy customers get impacted.

How Chargebee helps

Chargebee gives AI teams the ability to iterate on pricing without rewriting infrastructure or waiting on backend queues. You can:

• Price by API calls, actions, workflows, agent usage, or outcomes
• Roll out new pricing plans, trials, or bundles without touching code
• Grandfather existing customers to avoid revenue disruption
• Run A/B tests or market-specific pricing in parallel
• Sync updated pricing logic instantly to your website pricing page
• Align CPQ logic and downstream systems with a few clicks, not engineering sprints

How Orca AI supports a non-linear, account-based pricing model

“We price based on the number of accounts our customers manage—it’s neither per-seat nor usage-based. If their accounts grow, we grow with them; if they don’t, they don’t pay us huge amounts. Chargebee lets us plug that philosophy straight into our billing, with complex invoicing, gating in one system.”

• Tony Tom, Co-founder & CEO, Orca AI

3. PLG to Sales-Led, Without Creating Silos

The GTM playbook for AI companies isn’t linear. You might launch with self-serve trials, layer on sales for high-value deals, and adopt a full hybrid motion, all within one year. But these motions create tension when they live in disconnected tools: RevOps ends up juggling two billing systems, finance struggles to reconcile revenue, and GTM teams can’t get a unified view of the customer.

How Chargebee helps

Chargebee is purpose-built for GTM complexity. It lets you:

• Launch gated trials and convert to paid plans without building a separate workflow for PLG
• Equip sales teams to manage custom quotes, multi-year contracts, and future-dated amendments, all with native CPQ
• Automate approval workflows and sync contracts directly into your CRM for faster deal closure and cleaner downstream billing
• Unify product-led and sales-led revenue into a single system, so finance, GTM, and ops stay aligned, no matter the path to revenue

How T2D2 streamlined its hybrid sales motion during hypergrowth

T2D2, an AI platform for predictive building maintenance, launched a self-serve purchasing flow alongside its enterprise sales channel, automating billing end-to-end with Chargebee to serve both segments efficiently while reducing sales overhead.

4. Finance-Grade Rigor for a Fast-Moving Business

AI companies experiment like consumer startups but face the financial scrutiny of enterprise software. That creates a fundamental tension: How do you stay flexible on the front end (pricing, GTM, packaging) while keeping clean, auditable books at the back end?

If your billing system can’t handle complexity without breaking compliance, finance ends up stuck in spreadsheets, reconciling usage by hand, and slowing down forecasts, audits, and investor reporting.

How Chargebee helps

Chargebee brings financial discipline to your evolving business without slowing you down:

• Native revenue recognition for usage, subscriptions, and hybrid models
• Tracks mid-contract changes, deferrals, and amortization without manual effort
• ASC 606 / IFRS 15 compliance out of the box
• Deferred revenue and recognition schedules that match your contracts
• Provides audit-ready reports, including unearned revenue roll forwards and sales summaries

How Codacy automated 90% of revenue recognition processes

Codacy, an AI-driven code quality platform, automated nearly 90% of its revenue recognition tasks using Chargebee RevRec. The team eliminated 12+ hours of manual work every month and gained accurate, audit-ready reporting, freeing up finance to focus on strategic growth.

5. Native Ecosystem Integration, Not Another Duct-Tape Solution

AI companies depend on fast feedback loops: product usage needs to inform sales triggers, finance needs clean data to close books, and support teams need context to respond in real-time. But when billing lives in a silo, critical signals don’t reach the systems where decisions are made. 

While many billing tools advertise “deep integrations,” they often mean APIs or third-party connectors you still have to wire up, maintain, and troubleshoot. 

How Chargebee helps

At Chargebee, interoperability is part of our core ethos. That’s why we have built native integrations with 60+ systems you use already, including Salesforce, HubSpot, Xero, QuickBooks, NetSuite, Avalara, Vertex, Anorak, and more in our marketplace.

• Sales teams can generate quotes, trigger subscriptions, and view real-time billing status directly within Salesforce or HubSpot, no context switching
• Finance teams get accurate, clean, and compliant data flowing into NetSuite or Xero without nightly exports or spreadsheet patchwork
• RevOps and product teams can build workflows around usage, entitlements, and upgrades, with full visibility into monetization triggers

How MacStadium made billing more efficient and month-end closes 60% faster

MacStadium, a leading provider of Mac cloud infrastructure for iOS and macOS development, switched from Maxio to Chargebee, driven in large part by Chargebee’s in-depth integrations with Salesforce and NetSuite.

“Chargebee’s Salesforce integration has been of great help to our sales teams. It provides them with live access to customers’ subscription information, from plan and add-on details to MRR, as these can quickly get outdated without a real-time sync.”

• Mark Gagen, Director of Finance, MacStadium

6. A Partner That’s Battle-Tested, and Always Within Reach

AI companies move fast. But their underlying billing challenges—pricing changes, rev rec complexity, GTM shifts—aren’t new. What’s different now is the pace. You don’t have time for your billing vendor to figure it out as you scale.

How Chargebee helps

Chargebee has spent over a decade helping 6,500+ high-growth companies like Calendly, Typeform, Freshworks, and Linktree navigate freemium pivots, global expansion, and multi-product monetization. That means we’re not just built for scale; we’ve already lived it.

Behind the scenes, our product and engineering teams continue to push the bar: investing deeply in advanced usage-based billing, native CPQ, entitlements, revenue recognition, and more. It’s why Chargebee was named a Leader in the August 2024 Gartner® Magic Quadrant for Recurring Billing Applications, recognized for both Completeness of Vision and Ability to Execute.

So when you need to roll out a new pricing model overnight, clean up a migration, or prep for a raise, you don’t just get a platform. You get a seasoned team, real SLAs, and support that actually shows up.

7. Ready for Global Scale from Day One

AI is inherently global. Your first 1,000 users might come from five countries. But expanding into new markets often hits unexpected walls: local tax laws, regional payment preferences, FX volatility, and regulatory requirements. And if your billing system isn’t global-ready, you spend months untangling edge cases just to launch in a new geography.

How Chargebee helps

With Chargebee, global scale is built-in, not bolted on:

• Multi-currency pricing and tax handling for global compliance
• Support for regional gateways and payment methods, including SEPA, PayPal, Apple Pay, Boleto, iDEAL, etc.
• Geo-specific checkout logic that maximizes conversions by adapting to local norms
• Multi-entity management to separate business lines or regional teams cleanly

Freepik scaled from Europe to North America, LATAM, and Asia in just over two years while transforming its business model and launching new AI products. Chargebee handled the monetization infrastructure throughout, allowing internal teams to stay focused on building, not billing.

Chargebee: Billing and Revenue Infrastructure That Grows With You

When your AI product evolves in real time, your billing and revenue systems should evolve with it. Chargebee powers monetization for the next wave of AI leaders like Quillbot, LimeChat AI, AdCreative, DeepL, Messari, Writesonic, and Nextbillion AI, as well as established SaaS businesses like Freshworks and Zapier that are expanding their offerings with AI.

Whether you’re rolling out a new pricing model, entering a new market, or blending PLG with enterprise sales, Chargebee helps you do it with confidence, without compromising finance, ops, or the customer experience.

The post Why AI Companies Choose Chargebee for Billing appeared first on Chargebee Blog.