As AI revolutionizes how software works, it’s also reshaping how software is sold. Pricing strategy isn’t just a monetization lever anymore—it's a core part of the product experience and a key competitive advantage.
Sam Lee, VP of Pricing Strategy at HubSpot, spoke with Metronome CEO Scott Woody to explore SaaS’s sea change toward usage-based pricing (UBP). Drawing from their experience at places like Snowflake, ServiceNow, and Dropbox, they shared how AI is upending pricing norms—and what SaaS leaders should do next.
Here are the conversation’s major insights, why they matter, and what you can do about them.
AI breaks the seat-based pricing model
AI-enabled software has moved well past helping humans do things—it does things for them. This shift fundamentally changes how value is created. In traditional seat-based models, pricing is tied to the number of users. But when AI agents can autonomously generate reports, process support tickets, or analyze datasets, value stops being tied to user seats—it’s tied to actions, outcomes, or usage.
If your product uses AI, continuing to price by seat may dramatically misalign with how value is delivered. This can make your product seem overpriced or underperforming, even when it’s delivering tremendous value.
Action item: Start identifying if and where AI is creating value independent from the user. If you can locate examples in your product, it’s probably time to rethink what your pricing is anchored to.
UBP isn’t just for infrastructure companies anymore
Companies like Snowflake and AWS have used UBP for a long time, and now it’s spreading fast to application-layer SaaS—especially where AI is involved. This is because UBP is the only model flexible enough to monetize AI in real time. Trying to sell AI features through static packaging and procurement cycles just can’t move fast enough to keep companies competitive.
Speed and agility are becoming competitive differentiators. If you’re stuck in a packaging or SKU-approval cycle, you’re likely giving ground to faster, nimbler competitors.
Action item: Ask yourself, “How quickly can we launch and monetize a new AI capability today?” If the answer is weeks or months, your billing system could be the answer to unlocking speed and flexibility for your product roadmap.
Value metrics and usage metrics aren’t the same
Great pricing design starts with value, but value isn’t always measurable. Value metrics describe how customers experience desired outcomes. Usage metrics are what you can track internally and charge for. Here’s a quick look at the differences:

- Value metric: Defines what drives customer value. This is the foundation of your pricing model and should be carefully chosen and rarely changed.
- Usage metric: Tracks measurable activities tied to that value. These are more flexible and can evolve over time.
- Billable metric: Determines how customers are charged. This is the most adjustable, as long as it remains aligned with the value metric.
Misaligning your value metrics and your usage metrics causes friction. For instance, if you’re a customer support company, it might seem logical to price based on “number of messages sent.” But if your customers don’t perceive “number of messages sent” as valuable, there’s a mismatch in value. This can erode trust or block adoption. In this case, customers don’t care how many messages were sent—they care about what the messages resulted in: support tickets resolved is a much more accurate way to measure value. Your pricing needs to map as closely as possible to the value delivered.
One way to address this is with an abstraction layer—think credit-based usage models. Here, you have a middle layer you can adjust as you fine tune your usage and billing metrics, all without touching your core value metric. If you charge per successful outcome, each outcome might cost X credits, and the total credits per outcome might change as customers realize more value.
Action item: Map out the value your product delivers (your value metric) and what your systems measure (your usage metric). If they’re too far apart, you may need an abstraction layer (e.g., credits or tokens) or better instrumentation.
You’re probably moving to a hybrid pricing model—great!
Most SaaS companies won’t flip overnight from seat-based to usage-based pricing. We wouldn’t even advise that. Instead, they’re layering UBP on top of existing seat models. This reflects the reality that some features are still user-driven while others are automated or scalable.
This hybrid model adds complexity, but also opportunity. You can use UBP to unlock new revenue streams while keeping existing models stable around core features.
Action item: Start exploring which parts of your product are better suited to UBP. Can you introduce usage pricing for AI-powered features while retaining seat licenses for core functionality?
Speed and simplicity beat optimization
In fast-moving markets, pricing precision is less important than velocity. Over-engineering your pricing model to perfectly map cost-to-value often leads to confusion and delays.
A clear, understandable, good-enough metric usually outperforms a complex-but-precise one. In AI products, where usage can spike unpredictably, clarity is key.
Action item: Choose pricing metrics that are easy to explain, understandable to the customer, and resilient to abuse. Use early feedback and telemetry to adjust over time.
Your billing experience is part of your product
Unlike seat-based models, where billing is static and predictable, UBP introduces variability. Customers now need visibility, alerts, and guardrails, or they’ll feel blindsided by any unexpected changes.
Poor billing user experience is a top reason UBP fails. To actually succeed, you need to invest in real-time usage dashboards, budget controls, and proactive communication.
Action item: Build billing surfaces that empower users to monitor and control their spend. Review some great examples for inspiration, like OpenAI’s usage dashboard or the AWS Cost Explorer, both of which clearly lay out current costs and usage in an easy-to-understand way.
UBP is a company-wide transformation
Switching to UBP affects every department—from finance and sales to product, engineering, and customer success. Without alignment across the organization, efforts will stall or backfire.
Successful UBP transitions require strong executive sponsorship and clear articulation of why the company is making the change. Successful companies can make the switch in months if their CEO or sponsoring team make it a strategic priority.
Action item: If you’re leading the charge on this pricing change, start by aligning leadership and educating the rest of the organization. If you’re not at the top, build a business case that focuses on agility, monetization speed, and better customer alignment.
tl;dr: Your pricing strategy needs to evolve with your product
AI is changing what software is, which means it must also change how software is priced. Static, seat-based pricing won’t cut it in a world of autonomous agents, elastic compute, and fast-moving innovation.
Here’s what to remember as you transform your pricing:
- In AI, value is dynamic. That means pricing must be elastic.
- Usage doesn’t directly equal value, but it’s the closest measurable proxy.
- Hybrid models are the future. Plan for complexity.
- Billing is not a back office task—it’s customer experience.
- UBP transitions require C-level commitment and cross-org execution.
Watch the full session on transitioning to usage-based pricing with Sam and Scott.
If you're interested in seeing how Metronome can help you transition to usage-based pricing, get in touch! We're here to help you navigate the transition with confidence.
Company | Industry | Outcome-Based Pricing Model | Key Metrics for Pricing | Notable Features |
---|---|---|---|---|
Salesforce (Agentforce) | CRM / AI Customer Service |
$2 per conversation handled by Agentforce (AI agent) A conversation is defined as when a customer sends at least one message or selects at least one menu option or choice other than the End Chat button within a 24-hour period. |
Number of support conversations handled by the AI agent |
First major CRM to adopt a "semi"outcome-based pricing for AI; aligns cost with actual support volumes (clear ROI) Addresses inefficiencies of idle licenses by charging only when value (a handled conversation) is delivered |
Intercom (Fin AI) | Customer Support Software |
$0.99 per successful resolution by "Fin" AI chatbot - clients pay only when the bot successfully resolves a customer query Fees accrue based on AI-solved issues |
Count of support conversations resolved by the AI agent |
Early adopter of AI outcome-based pricing in 2023 Lowers adoption risk by charging for resolved queries instead of a flat rate; combines usage- and value-based pricing to tie cost directly to support effectiveness. |
Zendesk (AI Answer Bot) | Customer Support |
Per successful AI chatbot-handled resolution No charge if the bot fails and a human must step in |
Number of customer issues or tickets auto-resolved by the bot |
Aimed at cost-conscious customers wary of paying for unproven AI Aligns price with realized automation benefit; part of a broader industry shift from per-agent pricing to value-delivered pricing in support |
Chargeflow | Fintech (Chargeback Management) |
Charges a fraction of recovered funds on chargebacks Example: ~25% fee per successful chargeback recovery No fees for chargebacks lost Alert service charges $39 per prevented chargeback |
Value/count of chargebacks recovered (disputes won) and chargebacks prevented (for prevention alerts) |
4× ROI guarantee on recoveries No contracts or monthly fees Revenue comes only from successful outcomes; pricing directly aligns with merchant's regained revenue, meaning Chargeflow only profits when the client does (win-win model) |
Riskified* (source: https://www.chargeflow.io/blog/riskified-vs-forter) |
E-commerce Fraud Prevention |
remain fraud-free Only charges for transactions it approves that |
Number or value of approved transactions without fraud (i.e. successfully processed legitimate sales). |
Provider shares financial risk of fraud with clients; pricing tied to outcome of increased safe sales Incentivizes vendor to maintain high accuracy (they only profit when fraud is stopped) Foster continuous improvement in their fraud-detection algorithms |