Tiered pricing is becoming the simplest way to sell AI-powered SaaS without turning your pricing page into a technical explanation. In my interview with Dan Balcauski, founder and Chief Pricing Officer at Product Tranquility, we talked about why AI is forcing new pricing decisions earlier than ever—and why “good, better, best” packaging often works because it keeps buying decisions clear while helping companies manage real AI costs.
The AI era is making pricing margin-aware again. Tiered pricing helps you protect margins without forcing buyers to learn your cost structure.
About Dan Balcauski
Dan Balcauski is the founder and Chief Pricing Officer at Product Tranquility, where he helps high-volume B2B SaaS CEOs define pricing and packaging for new products. He is a TopTal certified Top 3% Product Management Professional and helps teach Kellogg Executive Education course on Product Strategy. Over the last 15 years, Dan has managed products across the full lifecycle—from concept incubation to launch, platform transitions, maintenance, and end of life—across consumer and B2B companies ranging from startups to publicly traded enterprises. He previously served as Head of Product at LawnStarter and was a Principal Product Strategist at SolarWinds.
Why Tiered Pricing Is Winning in the AI Era
For years, SaaS companies could price mostly around value because marginal costs were relatively stable. AI changes the math. Dan points out that companies are now cutting meaningful monthly checks to model providers, and leadership teams can’t pretend cost-to-serve is irrelevant anymore.
That’s a big reason tiered pricing is showing up everywhere right now. It gives teams a way to:
- Keep the offer simple for buyers
- Put premium capabilities where they belong
- Create a natural upgrade path that aligns with value and cost
Most importantly, tiered pricing keeps you out of the weeds. The customer conversation stays focused on outcomes, not infrastructure.
What Makes Tiered Pricing Actually Work
Dan’s point isn’t “just shove AI into the top tier.” Tiered pricing works when plan differences are easy to understand and tied to value drivers customers already recognize.
Here are three practical patterns from the discussion that hold up well in the AI era.
1) Put AI in higher tiers when it boosts a user’s output
If an AI feature makes a person more effective—faster drafting, better triage, higher quality responses—tiering can be straightforward. The buyer already understands why a “Better” or “Best” plan costs more: it changes the capability of the team.
This is also why seat-based pricing can still make sense for many AI-enhanced tools. If the value driver is still “help my team do better work,” then users/seats remain an intuitive anchor.
If AI increases team productivity, tiered pricing can stay aligned to seats—because seats still map to value.
2) Use add-ons when AI changes the value driver
Sometimes AI doesn’t just “help” the user—it replaces work entirely. When that happens, forcing it into the same tier structure can distort value and create confusion.
Dan points to Intercom as a strong example of handling this well:
- The core support platform stays priced per user (agents), because the value driver is agent effectiveness.
- Their AI agent (“Fin AI”) is priced separately because the agent isn’t involved—the value is the number of issues the AI resolves. That’s why per-resolution pricing makes sense.
3) Don’t make buyers learn token math
Dan’s strongest warning is about token pricing. Customers don’t want to learn what tokens are, and sales teams don’t want to explain them—especially when you’re selling a business outcome like faster support or better customer experience.
Token-based pricing also shifts the conversation away from value and toward your vendor bill. As Dan puts it, customers don’t care about your infrastructure costs, and pushing that complexity into the buying motion adds friction.
If your tiered pricing requires a footnote explaining tokens, you’re adding sand in the gears.
A Tiered Pricing Checklist for AI Features
Here’s a simple way to apply this immediately:
- Good: Core workflow value, minimal AI (or AI where costs are predictable)
- Better: AI that boosts team output (speed, quality, throughput)
- Best: AI that drives outcomes at scale (automation, deflection, resolution)
- Add-on: Use when AI has a different value driver than the base product (example: per-resolution)
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