Three Proven Strategies to Monetize AI Features in SaaS (Q4 2024)
How to Monetize AI: The State of AI Monetization Across the 500 Top Players in SaaS
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Today, our guests are co-founders of PricingSaaS: John Kotowski and Rob Litterst, who help SaaS GTM teams get better at monetization.
This post was not sponsored. I hope you enjoy this deep AI monetization research as much as I did.
AI as the Hottest Topic in SaaS
Unless you’ve been living under a rock, you know generative AI has been the hottest topic in SaaS this year. Companies are launching products rapidly, experimenting with new AI pricing models, and expanding AI-powered capabilities.
At PricingSaaS, we’ve been watching closely.
PricingSaaS tracks trends across the 500 top players in SaaS.
Today, we want to share what we’ve found. Specifically, we’ll cover:
The Pace of AI Monetization,
Three Proven Strategies to Monetize AI Features in SaaS,
Top Five Capabilities Powered by AI.
This post is jam-packed with data and examples, and you should come out of it with a clear picture of how AI has evolved in 2024 and where it’s going next.
Let’s get to it.
The Pace of AI Monetization
This year, we’ve seen an explosion of AI features and functionality across all types of SaaS products.
Unfortunately, we’re seeing a recurring theme: product teams are shipping first and monetizing later.
This has often been the case historically and is the basis for books like Monetizing Innovation, which highlights how aligning pricing and product pre-launch can have a massive impact on product adoption and revenue growth.
At PricingSaaS, we’ve been broadly tracking pricing trends across the top 500 players in SaaS and have been watching AI pricing strategies unfold over the past year. The gap between AI functionality and AI monetization has been persistent:
How to monetize AI?
The good news is that both numbers are on the rise. As a result, we’re seeing various AI monetization strategies come to market. Year to date, we’ve seen three core strategies emerge:
Strategy 1: Integrate AI Into Existing Tiers,
Strategy 2: Usage-Based Model,
Strategy 3: Add-On Model.
Tiers are leading the way. We believe this is a combination of legacy billing systems inhibiting more creative pricing models and uncertainty around customer willingness to pay:
Importantly, these models are not mutually exclusive, and we’ve seen a number of hybrid approaches as well.
Next, we’ll dig into each strategy, highlighting the benefits and drawbacks and showcasing examples from leading SaaS companies.
Three Proven Strategies to Monetize AI Features in SaaS
As mentioned, we observed three AI monetization strategies across the top 500 SaaS players. Each high-level strategy has multiple approaches to go to market.
Below, we’ll cover the patterns we’re seeing and share examples of companies actively using these strategies.
Strategy 1: Integrate AI Features Into Existing Tiers
Integrating AI features into existing tiers is the most popular approach. Top 500 SaaS Companies:
There are three main ways SaaS companies integrate AI features into existing tiers:
AI across all tiers as a core feature,
A differentiator,
A premium feature.
Let’s dive into the details.
Approach A: AI as a Core Feature
Offering AI as a core feature entails adding AI functionality to all plans. While it meaningfully reduces your ability to monetize AI directly, there are benefits to this play, including:
Faster product adoption across your customer base,
Stronger competitive positioning,
Stickier product,
Can amplify usage of other features.
Example: Buffer includes AI features across all tiers, potentially reducing monthly churn from 300 to 255 customers (15% improvement):
Approach B: AI as a Differentiator
Next, we’re seeing companies utilize AI to drive upgrades from free to paid plans. This is a conventional way to monetize AI, especially if the capability you’re monetizing garners willingness to pay.
Some of the benefits of this play include:
Direct contribution to AI monetization,
Offers a controlled rollout of AI capabilities,
Positions AI capabilities as valuable features.
Example: Pipedrive's AI-powered Sales Assistant drives 44% expansion revenue through upgrades from $34 to $49 per user:
Approach C: AI as a Premium Feature
Lastly, companies reserve AI features for Enterprise customers, giving AI capabilities a premium value perception.
Benefits of this play include:
Strengthens enterprise value proposition,
Creates a powerful negotiation lever,
Offsets higher support costs.
Example: Box's Enterprise Plus tier commands a 43% premium over standard Enterprise through AI-powered features:
Strategy Summary
Regardless of the approach, integrating AI into existing tiers offers several strategic advantages compared to other models, including:
Faster feature adoption within targeted tiers.
Lower operational complexity than add-on models.
Stronger tier differentiation.
In other words, if you’re looking for a low-lift way to strengthen your existing plans and drive immediate adoption of AI capabilities, tiers are the way to go.
But there are a few downsides. Tiered AI pricing models:
Make it harder to measure direct ROI from AI features.
Limit the ability to capture value from power users.
Offer less control over margins than Add-Ons or Usage-based models.
Speaking of which, the adoption of Usage Based models, while still well behind Tiers, has risen quickly this year.
Strategy 2: Usage-Based Pricing Model
Usage-based pricing has been on the rise over the past decade, and it’s quickly become the second most popular way to price AI features.
The top 500 SaaS companies:
This works well since AI features have costs of their own, and a usage-based model allows you to ensure you’re covering those costs. Within the usage-based framework, there are two primary ways we’re seeing companies price AI features:
Consumption Pricing,
Outcome-Based Pricing.
Let’s dive into the details.
Approach A: Consumption Pricing
Consumption pricing includes either pricing for pure consumption (e.g., CPU) or pricing based on outputs (e.g., per words generated). As mentioned, consumption pricing creates a natural alignment between cost and value delivered.
Additional benefits include:
Uncapped ACV expansion as customer usage grows,
Offers the clearest way to measure ROI on AI features,
Gives customers the flexibility to scale costs up or down.
Example: Algolia charges $0.50 per 1,000 search requests and $0.60 per 1,000 recommendations:
Approach B: Outcome-Based Pricing
By far, the buzziest way to charge for AI functionality is outcome-based pricing or actually charging users for results.
Until recently, it’s been very hard for SaaS companies to actually “do” work for their customers, making any value metric a proxy for actual value.
Outcome-based pricing changes the game. It’s the holy grail of pricing models, and the business case speaks for itself.
Benefits include:
Easy to justify value given the solution is actually completing work,
The clearest way to measure business value delivered by AI features,
Highest alignment between cost and customer ROI.
Example: Intercom charges $0.99 per resolved ticket for its Fin AI Agent product rather than per interaction or user:
Strategy Summary
Whether you choose Consumption or Outcome-based pricing, the Usage-Based AI pricing model has a few compelling benefits, including:
Direct alignment of pricing with value delivered to customers.
Drives initial adoption by lowering upfront costs.
Offers flexibility for customers with varying levels of AI needs.
However, there are a few downside risks to be aware of:
Like any usage-based pricing strategy, revenue can be unpredictable, posing challenges for the company and customer, especially if predictability is a high priority.
Further, usage-based pricing can require complex metering and billing infrastructure, which can be especially challenging for companies with legacy billing systems.
The last model we’re seeing with regularity is an Add-On model.
Strategy 3: Add-On Pricing
Add-on pricing has emerged as a flexible approach to monetizing AI capabilities, offering SaaS companies a powerful lever for increasing Annual Contract Value (ACV) without disrupting their core pricing structure.
The top 500 SaaS companies:
This AI monetization strategy is particularly effective because it allows companies to capture additional value from their existing customer base while maintaining their primary revenue streams.
Companies are implementing this strategy in two distinct ways:
Seat-based Add-Ons,
Hybrid Add-Ons.
Let’s dive into the details.
Approach A: Seat-Based Add-ons
Seat-based add-ons are straightforward. A company charges a defined dollar per seat/user for AI functionality.
There are several financial and operational benefits to the add-on model, including:
Immediate ACV uplift as existing customers adopt the add-on.
Minimizes friction to purchase compared to tier upgrades since customers keep their existing plan structure.
Offers an easy way to experiment with AI before committing to a sophisticated rollout.
Example: Notion implements an $8 AI add-on to their base $10 per user monthly fee, resulting in an impressive 80% ARPU increase:
Approach B: Hybrid Add-On Pricing
The other way we see companies offer add-on pricing is through a hybrid approach. This is when a company charges a flat add-on fee with a specific usage limit, with a variable rate for additional usage.
The hybrid approach offers strategic benefits of both the tiered model and usage model, including:
Provides baseline ACV stability while capturing expansion revenue.
Offers customers a predictable cost base while still allowing the flexibility of usage-based pricing.
Better unit economics as fixed costs are covered by the base fee.
Example: Airtable charges a $6 seat-based add-on plus usage-based credits ($20 for 10K additional credits):
Strategy Summary
Whichever path you choose, the add-on AI pricing model offers several high-level benefits, including:
Faster time-to-market for new AI features.
Ability to test pricing and value proposition without affecting the core product.
Clear revenue attribution for AI features.
That said, there are two downsides:
Add-on pricing could slow the adoption of AI features compared to including them in base pricing.
Depending on the market, it can also open the door to competitors undercutting with all-inclusive pricing.
Top Five Capabilities Powered by AI
When most people think of generative AI, they picture content tools—specifically, features that brainstorm blog post ideas, whip up marketing copy, or churn out ad creatives
While AI has transformed content creation, it’s also starting to infiltrate functions across the wider organization.
We’ve identified the five most common AI capabilities among the top players in SaaS:
AI Capability 1: Content Generating and Editing
As mentioned above, content creation is one of the most common capabilities that SaaS companies offer with generative AI, with 23% of the PricingSaaS index offering this functionality.
It’s also the most mature from a monetization standpoint, with 87% of companies that offer AI content tools actively monetizing it in some way. This includes tools for writing, media generation, suggestions, and content summaries.
Example: Canva monetizes AI-powered design tools both through usage-based and tiered pricing:
AI Capability 2: Data Analysis and Enrichment
Interestingly, data analysis and enrichment are just as common among SaaS players as content generation and editing, with 23% of the PricingSaaS 500 offering these AI-powered capabilities.
However, it’s lagging behind from a monetization standpoint, with 74% of companies that offer these capabilities actively monetizing them.
This includes features like predictive analytics, sentiment analysis, behavior analysis, search and categorization, and data enrichment.
Example: Pendo powers behavioral insights with AI and offers it across all paid plans:
AI Capability 3: Customer Support and Interactions
It’s probably never been a more exciting time for Customer Support products. The rise of outcome-based pricing in this space has the entire SaaS industry buzzing. Across the PricingSaaS 500, we’re seeing 14% of companies offering AI-powered Customer Support and Interactions.
This capability is further behind on monetization, with 57% of companies that offer it actively monetizing. One reason could be that outcome-based models are harder to execute, and the companies offering this functionality want to perfect the user experience before committing to a new pricing model.
This capability includes chat interactions, but also personalized outreach (e.g., AI SDRs), and scheduling.
Example: Intercom resolves customer support cases with Fin AI Agent and charges a flat fee per resolution:
AI Capability 4: Testing and Development
Unsurprisingly, developers are getting in on the fun with generative AI as well. Just under 10% of the PricingSaaS 500 are offering testing and development capabilities powered by AI.
This functionality is relatively mature from a monetization standpoint as well, with 75% of those companies actively monetizing.
These features include assisted development, test automation, and code documentation/interpretation.
Example: GitHub offers an AI coding assistant as a tiered add-on:
AI Capability 5: Security and Risk Management
Lastly, we’re seeing a rise in AI-powered tools that help identify fraudulent intent and threat detection. Just under 8% of the PricingSaaS 500 offer this capability, but about 75% of those that do are monetizing.
This capability includes fraud detection, vulnerability detection, and bot detection and offers an optimistic future for fighting off security threats.
Example: GitLab Duo (an AI add-on) comes with Security and Vulnerability tools:
Conclusion
So, what is the current state of AI in SaaS? To summarize:
Product teams are making AI a priority. The percentage of SaaS companies offering AI has risen every quarter in 2024.
AI monetization is lagging, but the models being offered are maturing. More companies are experimenting with creative strategies, and a new class of billing startups is helping SaaS companies evolve beyond legacy billing systems.
Capabilities are moving beyond content generation. The most common use cases are starting to emerge, showing that generative AI is infiltrating tools across the entire organization.
Next year, we expect to see all three trends continue, and we look forward to tracking their progress.
To dig deeper, check out the PricingSaaS Q4 2024 AI Benchmarks Report:
And to stay tuned, check out the PricingSaaS Index for regular updates.
Bonus Infographic
An infographic with a more detailed split:
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Paweł
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