While some SaaS businesses embed AI directly into their platforms, others sell it as a premium add-on or adopt a hybrid model that blends embedded AI with usage-based monetization. With no standard pricing approach, vendors are experimenting — introducing per-interaction pricing, bundling AI into subscriptions and offering flexible, usage-based plans to meet evolving enterprise demand.
This is the second installment of our series on AI-driven pricing models, following our examination of how AI is reshaping the SaaS industry in Part 1. Now, we turn to how companies structure and package AI-powered features within their products. Choosing the right strategy is key to driving adoption while optimizing revenue.
Three core approaches to AI integration
The three approaches described below influence how businesses monetize AI, differentiate their offerings and drive long-term customer value.
The add-on approach
This model offers AI as a premium add-on, with companies charging 30%-110% above base pricing, according to research by Tom Tunguz. This model enables businesses to put commercial focus on the AI feature, accurately test demand and drive upsells without affecting the core packages. However, it also limits the immediate user base to which the AI feature is pushed, and fundamentally positions AI outside the core packages in a manner that may not be appropriate as these features become more standard across platforms.
Notion and Slack follow this model, offering AI-powered tools — such as AI-assisted writing and summarization (Notion AI) and smart recaps and automated message threading (Slack AI) — only as separate add-ons for an additional cost per user, per month.
The embedded approach
In this model, AI capabilities are integrated directly into core packages, providing immediate access to all users who have access to the relevant tier(s). The tier system can take one of two forms:
- Base tier embedding: AI features are included in basic plans to drive widespread adoption.
- Premium tier embedding: AI capabilities are reserved for higher-priced tiers, encouraging upsells.
HubSpot’s integration of AI across its CRM, marketing and sales tools led to a 21% year-over-year sales increase, demonstrating how embedding AI can drive both adoption and revenue growth. Making AI a core feature, even at the base tier, enhances engagement and retention but requires careful margin management.
The hybrid approach
This strategy blends embedded AI with premium AI upgrades, offering multiple monetization paths:
- Embedding basic AI in base tiers while offering advanced features as add-ons
- Distributing stratified AI capabilities across product tiers or as add-ons to align with different customer needs
Zendesk recently expanded access to its AI agent feature across all tiers while keeping advanced chatbot automation and workflow optimizations as premium add-ons. A recent analysis by Dave Kellogg suggests this approach is gaining traction as companies balance accessibility with premium positioning.
As AI pricing models evolve, vendors are making real-time adjustments to meet enterprise demand. Google bundled Gemini AI into its standard $14 Business plan — previously this required an additional $20 add-on — signaling a shift toward embedded AI at lower price points to drive adoption. These shifts show an evolving industry where AI pricing remains in flux (and may also be a recognition of declining costs associated with AI usage).
AI packaging strategies across industries
AI pricing models vary based on how the technology integrates into specific industries or workflows (see Table A). Some categories, like productivity software and cybersecurity, favor embedded AI, making AI-powered features standard within core products. Others — such as healthcare and sales automation — are more likely to rely on hybrid or add-on models, balancing regulatory constraints, specialized functionality and value-based pricing.
