Pricing & Packaging Pillars For (AI) Startups: Five Models to Consider
Pricing and packaging is, IMO, the most underutilized, highest leverage tactic available to founders to make an immediate impact on sales. Unfortunately, there are not pragmatic teachings out there on the right way to approach pricing / packaging. Further, AI-based software, especially AI agents & AI teammates, are disrupting the old models. Specifically, the usual “per-seat” SaaS pricing model is no longer quite relevant when the software itself is doing a lot of the work that humans used to do.
Note, at Gigya (where I was the CEO for a decade), we increased average contract values (ACVs) from $10K → $40K → $75K → $125K → $250K, each subsequent year, with largely the same core product offering. The gains we achieved were largely due to improving the way we priced and packaged the product.
In this post, I’ll share the five core pricing models (Platform, Seat-based, Consumption-based, Add-on, and Outcome-based) and specific recommendations on how to package these models based on your stage and what your (AI) product actually does.
Let’s dive in.
The Big Picture
You can think of pricing for AI software as a balancing act between (a) how your customers perceive value, and (b) how you want to capture that value. Traditional SaaS had it easy: seat-based pricing with feature-tier gating (Basic/Pro/Enterprise). AI-based software flips the script because you often have real usage costs, plus the potential to deliver (and measure) actual outcomes that your customers really value—like driving more sales or saving them real dollars on operational costs.
Here’s a quick reference chart summarizing the five main pricing models:
Use this matrix as a starting point to figure out which approach might resonate with your buyers and your product’s unique value prop.
1. Platform Pricing (Flat or Tiered)
Platform pricing is essentially a subscription fee for broad access to your AI’s capabilities. Think: “$X per month (or year) for unlimited usage,” or tiered packages (Basic/Pro/Enterprise).
Pros
Dead simple. Finance teams at big companies love a predictable budget line item.
Encourages organization-wide adoption without fear of usage overages.
Cons
You might get hammered by outlier usage if you don’t have a usage policy.
Don’t get to take advantage of as many upsell opportunities as the product gets more usage
Best For
A starting point for offering additional pricing modules.
Early-stage deals when you just want the big logo on your site.
Enterprise deals where the buyer demands cost certainty or hates the idea of pay-per-use.
Tip: If your AI agent has real variable costs, consider capping usage or layering in fair-use language.
2. Seat-Based Pricing
Seat-based (per user) is the classic SaaS approach. You charge, for example, $30/user/month, and if the customer adds five new reps, you get expansion in monthly revenue.
Pros
Familiar: Everyone from Slack to Salesforce uses it, so it’s easy to explain in the sales process.
Works if each user truly experiences separate, individual value—like AI coding assistants for devs.
Cons
Value misalignment for automation-based AI. If your AI takes over tasks that used to require a whole team, then your seat count drops—so you lose revenue as your product gets more successful!
Doesn’t account for usage intensity. One user could be hammering the AI, while another barely logs in.
Best For
Tools where human usage is still the main driver of ROI (e.g. dev assistants, sales writing tools).
High collaboration or user adoption contexts. You grow revenue as they roll it out to more employees.
Tip: If your AI replaces people, or if it’s more of an agent than a user tool, you might consider charging per “AI agent” seat. That keeps the seat logic but doesn’t punish you if they automate away real humans.
3. Consumption-Based Pricing
Consumption-based or “pay-as-you-go” means you charge per API call, per message, per CPU-hour, per thousand tokens—whatever usage metric fits. This approach is super common in cloud infrastructure. With the rise of generative AI, it’s everywhere.
Pros
Ties revenue to actual usage, so if customers scale up, you make more.
Can attract new customers with a low or even free entry tier, then let them scale.
Cons
Bill shock is real. If a big marketing campaign suddenly triggers an explosion of usage, your customer might freak out at the next invoice.
Hard to forecast your own revenue. Investors like predictable recurring revenue, so you might have to get creative with how you present your MRR.
Best For
Developer-facing AI products, where usage is easy to measure and devs expect usage-based pricing (OpenAI, Amazon Rekognition, speech-to-text services, etc.).
Use cases where value is clearly tied and aligned to usage
Tip: Provide usage dashboards and notifications. The more you can help customers manage their usage (and thus their bill), the better your relationship.
4. Add-On (Feature) Pricing
Add-on pricing is basically “À La Carte.” You’ve got a base subscription (e.g., bronze, silver, gold), but advanced modules or specialized AI capabilities cost extra. Think: “$30/user/month for the base app, plus $10/user/month if you want the advanced generative AI feature pack.”
Pros
Great for upselling. Start small, then add more modules as the customer sees ROI.
Customers only pay for what they need, so it can be a compelling pitch.
Cons
Can quickly create packaging confusion if you slice features too finely.
Customers might feel nickeled-and-dimed if you label core features as paid add-ons.
Best For
Broad platforms with distinct modules. E.g., “core CRM” plus “AI analytics,” plus “AI lead-scoring.”
Verticals with specialized compliance or domain features that not everyone needs.
Tip: Keep the menu limited. One to three well-defined add-ons is usually enough. If you have dozens of little add-ons, it starts to feel like you’re running an App Store inside your product.
5. Outcome-Based Pricing
Outcome-based models tie your fees to specific results. For instance, you charge $2 per successfully resolved customer support ticket, or a percentage of new revenue your AI-driven ads generate.
Pros
Easy ROI pitch: “You only pay when you actually get value.”
Potentially big upside if you take a slice of significant revenue or cost savings.
Cons
Defining the “outcome” can be messy. Who decides if a support ticket was “resolved” by the bot vs. a human agent?
Can lack predictability that is usually the foundation for business models for enterprise software vendors.
Best For
Customer Support: Many new AI support bots charge per ticket resolved. If the bot fails, the ticket goes to a human, and the company doesn’t pay for that.
Sales: Pay per qualified lead, or a percentage of revenue gain from your AI’s leads.
Cost-Saving Automations: E.g., “We take 20% of the cost savings we generate.”
Tip: If you go outcome-based, consider a hybrid: a small base fee plus a success fee. That way, you’re not on the hook for indefinite costs if it takes longer to see results.
Hybrid Approaches & Final Advice
In reality, the best approach is likely a hybrid approach where you use multiple pricing strategies to maximize value, for example:
Base subscription (platform or seat) + usage overages: You get baseline revenue but still capture upside if they use alot of the AI.
Seat + consumption: Each seat pays a monthly fee, but heavy usage or growth over time triggers an additional per-use charge.
Add-on modules + outcome-based: The advanced AI module might have a success-based component.
A few final pointers:
Start Simple: If you’re an early-stage startup, sales velocity is super important so your best bet is to pick the model that is relatively easy for your target customer to grasp and for you to manage. You can always refine or expand your pricing later.
Optimize Later: As your company scales, experiment with pricing models that will help you maximize ACV and Net Dollar Retention per customer. This later point is super important - you want to see each customer have a chance to grow 25-50% per year in upsells.
Speak the Language of ROI: Whether it’s seat-based or outcome-based, always connect the dots for your buyer: “Here’s how we save/make you money.” It makes higher price tags more palatable.
I’ve seen all these models in the wild. The best approach boils down to the fundamentals: Which pricing mechanic best reflects caturing the value your product delivers?