In a recent video by pricing strategist Akhil from Monetizely titled "Most AI Startups Don't Have a Pricing Problem: They Have a Product Management Problem," Akhil explains that AI companies often struggle with pricing because they haven't properly aligned their business model with customer outcomes. Referencing Andrew Ng's insight that product management—not engineering—is the real bottleneck for AI companies, Akhil takes this perspective further into the realm of pricing strategy.
The Root Problem: Misaligned Value Metrics
Most AI startups default to pricing strategies that are convenient for them rather than meaningful to customers. As Akhil pointedly states in the video: "Team meters tokens or charges per seat because it is easy to do. But customers buy outcomes."
This fundamental misalignment creates a cascade of business problems. When your pricing doesn't reflect how customers actually measure value, you experience poor activation rates, customer anxiety about overages, and minimal opportunity for revenue expansion.
The core issue is that AI companies typically focus on what's technically easy to measure (tokens, seats, compute resources) rather than what customers truly value (business outcomes).
Understanding Your Unit of Value
According to Akhil, the solution starts with identifying your true unit of value before setting prices. He recommends a specific process: "Do five jobs to be done interviews to find the moment a buyer says this paid for itself. Then align your model to that moment."
This "jobs to be done" framework helps uncover what customers are actually trying to accomplish with your AI solution. Are they trying to:
- Generate qualified leads?
- Prevent fraud?
- Resolve customer service tickets?
- Save time in meetings?
Once you understand the specific outcome that makes customers feel they've received a return on their investment, you can build your pricing model around that value metric.
Three Effective AI Pricing Models
Akhil outlines three pricing approaches that have proven successful for AI companies:
1. Hybrid Pricing
"Hybrid pricing, a small platform fee plus a value meter tied to outcomes, number of documents processed, incidents resolved, meetings scheduled, etc."
This approach gives you a baseline of predictable revenue through the platform fee while also allowing you to capture additional value as customers derive more benefits. The variable component should be tied directly to customer outcomes—not technical inputs like tokens or compute time.
2. Outcome-Based Tiers
Rather than creating tiers based on features, build them around economic outcomes. Akhil suggests using "a good better best style that ladders on economic outcomes, not features. Use a top tier as an anchor and a decoy to highlight ROI."
This approach helps customers self-select into the tier that delivers the right level of business value for them. The highest tier serves as both an aspirational target and a price anchor that makes the middle tier look like an excellent value.
3. Persona-Based Price Fencing
"Price fences by persona. Builder plan with general exploration. Operator plan with predictability and enterprise plans with outcome SLAs."
Different user types have fundamentally different needs and value drivers. Builders (often developers) need flexibility and exploration. Operators need predictability and reliability. Enterprise customers need guaranteed outcomes and service level agreements.
By creating distinct packages for each persona, you can maximize adoption and revenue across different customer segments.
The Psychology of AI Pricing
Token-based pricing creates significant psychological barriers to adoption. As Akhil notes, "Token-based pricing creates loss aversion and throttles learning."
When customers are constantly worried about exceeding token limits or accruing unexpected costs, they restrict their usage—which limits the value they receive and reduces their likelihood to expand.
To overcome this challenge, Akhil recommends adding "guardrails, soft caps, usage credits, and rollover to reduce risk and increase exploration." These mechanisms give customers the confidence to fully explore your AI solution without fear of sudden cost spikes.
Another powerful psychological tactic is to "anchor prices to an existing budget line. People pay faster when they can reallocate versus when they have to justify net new." This approach reduces friction in the buying process by allowing customers to use existing budget allocations rather than requesting new funds.
The One-Line Value Equation
Perhaps the most important takeaway from Akhil's presentation is this: "If you can't name your unit of value, you can't scale PLG [product-led growth]."
He recommends creating a simple value equation: "We deliver X outcome per Y unit for Z price" and testing it with actual customers.
This straightforward formula forces you to articulate precisely what customers are buying, how they measure it, and what they're paying. If you can't express your value proposition in these terms, you likely haven't done the product management work necessary to support effective pricing.
Conclusion: Pricing Is Product Management With a Dollar Sign
As Akhil succinctly puts it, "Pricing is product management with a dollar sign." Your pricing strategy is inseparable from your product strategy—they must be aligned around the same understanding of customer value.
For AI startups, this means moving beyond technically convenient pricing metrics like tokens and seats. Instead, structure your pricing around the business outcomes your customers actually care about. Do the hard work of product management first—understanding your customers' jobs to be done and defining your unit of value—and effective pricing will follow.
The most successful AI companies don't just have better models or code; they have a deeper understanding of their value proposition and price accordingly.