In a recent episode of the Backstage Pricing series on Monetizely's YouTube channel, Ajit Kumar, CEO of Monetizely and author of "Price to Scale," provided an insightful breakdown of InVideo.io's pricing strategy. The video, titled "InVideo AI Pricing Breakdown: What Their Tiers Say About Value & Strategy," offers a professional examination of how this generative AI video platform structures its pricing and what that reveals about their business strategy.
Understanding the Core Components of Pricing Strategy
According to Kumar, any effective pricing strategy consists of three fundamental components:
- Tier Structure: Reflects customer segmentation and should be designed to fulfill the needs of each segment while maximizing revenue.
- Pricing Metric Selection: Should align with how customers perceive and value the solution.
- Price Point: The actual dollar amount charged for each tier.
Using this framework, Kumar evaluates InVideo's pricing approach to identify both strengths and weaknesses.
InVideo's Tiered Approach: Strengths in Segmentation
InVideo offers four main tiers: Plus ($28/month), Max ($50/month), Generative ($100/month), and Team ($900/month). They also provide a free option and a separate enterprise tier with custom pricing.
Kumar particularly commends InVideo's inclusion of a high-priced team plan, which he sees as a strategic advantage over competitors. As he notes, "What I'm already immediately liking is just like 11 Labs, InVideo.io has created a team package at a much higher price point where they have a couple of entry-level offerings and then they have this generative plan."
This approach contrasts with competitors like Runway ML that lack higher-priced tiers, potentially limiting their access to enterprise customers. Kumar observes: "Products such as Runway ML, they have $100 plan or maybe a little bit higher, but they don't have this really high dollar plan, which limits for those companies the access to the enterprise tier."
The Missed Opportunity in Capability-Based Differentiation
One critique Kumar offers is that InVideo, like many generative AI products, differentiates plans primarily based on usage volume rather than capabilities. He explains:
"Something that is problematic in almost all generative AI products is that the plans are not necessarily about capabilities, but the plans are simply about how much stuff you can generate."
This represents a significant missed opportunity compared to established SaaS products like Figma, which differentiate enterprise tiers through enhanced workflow capabilities, collaboration features, and enterprise-wide systems. Kumar warns that as generative capabilities become commoditized, the products that will ultimately succeed are those that integrate seamlessly into workflows:
"It behooves generative companies if they're really going to become that much of a mainstay inside an organization to build those workflow capabilities because sooner or later the generative portions of these capabilities are going to be competitively commoditized."
The Pricing Metric Problem: Credits Lack Clarity
The most significant weakness Kumar identifies is InVideo's unclear pricing metric. The platform uses "credits" as its currency, but fails to clearly explain what these represent:
"What I find hard is that I do not know what credits mean," Kumar states, noting that he had to calculate conversion rates himself to understand that 10 credits equates to 30 seconds of video.
This opacity creates confusion for potential customers who can't easily predict their costs or usage. Kumar emphasizes that while many companies are "getting used to buying per credit, you really on the pricing page need to explain to people what on earth is a credit."
He points out another ambiguity: "If I spend 10 seconds of the generative… Do I still get 50 video minutes or do my credits exhaust and I cannot generate any of these 50? So… are these 'and' or 'or'? Very basic question. Anybody is going to have this question."
Price Point Strategy: Finding the Right Balance
On price points, Kumar generally approves of InVideo's approach. He appreciates the spread from $28 to $900, which creates clear differentiation between customer segments. He also sees the $28 entry point as strategically sound:
"As a pricing person, I don't mind it too much that they start at $28 per month. That ensures that they don't get frivolous buyers… Perhaps it is a good thing that they are trying to create a proper conversion jump between free and plus so that they can focus on delivering good value."
Kumar cautions against the trend of generative AI products offering very cheap plans, noting that this can create "a lot of revenue risk, brand risk for you if you then change the pricing for them." He references the recent experience of Cursor as a cautionary tale.
Overage Credits: A Smart Monetization Strategy
One positive aspect Kumar highlights is InVideo's option to purchase additional credits. He observes, "What I like is that you can buy more credits," and notes that other companies are "not even allowing you to buy overage, which is again problematic because they are shooting themselves in the foot."
This flexibility allows customers to scale their usage as needed without creating unnecessary sales cycles, which Kumar sees as a customer-friendly approach that also benefits the company.
The Bottom Line on InVideo's Pricing
In his final assessment, Kumar concludes:
"Overall, I think segmentation-wise, they're doing a good job. Pricing metric-wise, everybody's using credits, but they do not offer a very ready credit conversion table, which is not good at all. It's not clear what a credit is. You really have to spend a lot of time on the pricing page to figure that out. And price points, I think they're probably doing a good job."
His recommendation for improvement focuses primarily on fixing the pricing metric communication "so that it becomes easy for customers to buy them."
For SaaS executives looking to optimize their own pricing strategies, InVideo's approach offers valuable lessons in both what to emulate and what to avoid. The clear tier structure targeting different customer segments demonstrates strategic thinking about market positioning, while the lack of transparency around credits highlights the importance of clarity in pricing communication.
The most important takeaway may be Kumar's observation about the future of generative AI products: true differentiation will eventually come not from raw capabilities but from how seamlessly these tools integrate into enterprise workflows. This insight suggests that SaaS leaders should think beyond feature differentiation to consider the broader ecosystem in which their products operate.