“Your pricing is the exchange rate on the value you’re creating in the world.” — Patrick Campbell, Founder & CEO, ProfitWell
Every AI project starts with dreamy demos and ends with a CFO asking, “Why did the bill triple last quarter?” The raw cost of running large AI models can be punishing: OpenAI’s o3-high model can rack up $3,500 for a single benchmark call. When generative AI tokens flow, your pricing structure must scale revenue in tandem or profits vanish. That’s why choosing a rock-solid pricing model—whether subscription, pay-as-you-go model, or hybrid pricing models—is mission-critical.
Stakeholders also care about value perception. As ProfitWell’s Patrick Campbell reminds us, customers pay for value—not code. If the payment model hides that value, churn follows fast.
In a pay-as-you-go system, customers pay for what they use—no more, no less. AWS set the gold standard decades ago, and now many AI infra players copy the playbook. The upside? Revenue scales with compute; AI costs never get out of control. According to Metronome’s 2025 survey, 85 % of respondents “already had UBP,” spanning every slice of software pricing from dev tools to vertical apps.
Yet there’s a clear disadvantage: buyers dread invoice roulette. One rogue script and the bill can pay based on how much they over-ran last night. Predictability plummets, and a customer might walk. Enterprises often demand a ceiling before signing.
A flat monthly subscription brings simplicity: swipe the card once, and finance can forecast. Seat-based subscription pricing remains dominant because procurement loves line-items they can pencil in under “IT OPEX.” SaaS companies like Adobe ride this wave; customers know they’ll pay the same amount each month regardless of usage patterns.
The flip side? If your usage-based model costs spike, a low, fixed monthly fee bleeds margin. We’ve seen AI startups offer a flat subscription only to yank it back once GPUs start burning through cash. That sudden pricing changes hurts trust—and ARR.
Unable to choose? Bolt.New and Monday.com mix a base subscription tier with AI usage credits. This hybrid pricing lets customers predict spend while you align revenue to compute. OpenView data shows 46 % of SaaS players now run usage-based pricing models alongside seats. Hybrid models balance risk: small users stay profitable, power users fund GPUs.
Ask two CFOs, get two answers. Research from Maxio reveals 73 % of usage-based firms actively forecast variable revenue for predictability. That means the vendor’s finance org wants stable cash flow as badly as customers do. Meanwhile, developers love the freedom to use the service sporadically without a long-term lock-in. Your model allows you to pick sides—or straddle both.
The GPU arms race makes generative AI uniquely expensive. When tokens explode, charge based on consumption or risk a loss leader. Business Insider notes firms “are adopting usage-based pricing based on tokens consumed” to match rising infrastructure bills. AI pricing thus drags the entire industry toward usage meters, even for legacy vendors. Flat-rate models feel quaint when one prompt melts a 24-layer transformer.
Good-Better-Best isn’t dead; it’s just smarter. Offer a free tier for experimentation, a mid-tier with core AI tools, and an enterprise tiered pricing model that bundles white-glove support. Algolia famously uses this tiered pricing plus usage overages. A simple pricing tier helps early adopters start cheap, then scales as customers use more advanced AI features.
Still, beware the disadvantages of the model: freemium plans can swamp servers with tire-kickers. Charge too late, and overage billing models get messy.
No guru can hand you a universal answer. Your use case, margin profile, and buyer persona dictate the best model for your business. Infrastructure vendors often embrace payg pricing because costs mirror usage. Horizontal apps lean on flat-rate pricing for budget approvals. Customer might balk if your tool swings between $200 and $2 000 a month.
When in doubt, run experiments: A/B a payg model vs a fixed subscription, measure churn, expansion, and NPS. As Kyle Poyar warns, models may need adjustment once data rolls in.
Here’s where execution meets reality: invoices. A voice AI assistant such as CallPad listens, transcribes, and routes customer calls, then logs precise token counts per interaction. By surfacing real-time usage, CallPad helps customers to pay only what they owe—no surprises, no card-declines at month-end. The AI phone assistant makes it easier to offer pay-as-you-go without drowning in support tickets.
When it comes to pricing, there’s no silver bullet—only trade-offs. A seat-based subscription business offers calm seas; pay-as-you-go plans ride the revenue roller-coaster but can scale with demand. Most leaders land on a combo: a base subscription for stability plus meters for bursts. Whatever you choose, remember Campbell’s mantra: pricing is simply the value exchange rate. Set it wisely, and both you and your customers get richer.
How does pay-as-you-go vs subscription affect margin?
Usage-based vendors report 4–8 % higher gross margin once infrastructure costs align with revenue, but face up to 29 % longer enterprise sales cycles.
Is AI adoption pushing everyone toward consumption-based pricing?
Yes. Maxio’s 2025 report shows 67 % of SaaS firms now include consumption charges—up from 52 % in 2022—as AI adoption forces them to offset GPU spend.
What is an AI phone assistant, and why should I care?
An AI phone assistant like CallPad uses speech recognition and language models to handle calls automatically, reducing support load while logging every token so customers pay based on real usage, not guesses.
Can tiered pricing coexist with pay-as-you-go?
Absolutely. A platform can sell starter subscription tiers with included credits, then apply consumption-based pricing when customers get close to the cap. This hybrid model is common among infrastructure and analytics vendors today.
