Today, we’re thrilled to sit down with Vijay Raina, a renowned expert in enterprise SaaS technology and software design. With a deep background in architecture and thought leadership, Vijay brings a unique perspective to the evolving world of AI infrastructure and innovative billing models. In this conversation, we’ll explore the groundbreaking approach of results-based billing for AI agents, the challenges faced by agent developers, and how new platforms are addressing skepticism around AI adoption in the enterprise space.
How did you first become interested in the concept of results-based billing for AI agents, and what potential do you see in this model?
My interest in results-based billing stemmed from observing how traditional SaaS pricing models, like per-user fees or flat subscriptions, often fail to align with the actual value delivered, especially in the AI space. AI agents operate differently—they’re often working behind the scenes, and their impact isn’t always immediately visible. I saw a huge opportunity to shift the focus to outcomes, where agent makers and customers alike can tie costs directly to measurable results, like margin saved or tasks automated. The potential here is transformative; it builds trust by ensuring customers pay for real impact, not just usage, and it helps agent developers sustain their business by reflecting the true worth of their technology.
What do you think are the biggest flaws in traditional pricing models when applied to AI agent platforms?
Traditional models like per-user fees or unlimited usage subscriptions just don’t fit the AI agent world. For one, agent makers are hit with variable costs—usage fees to model providers and cloud services can stack up fast, especially if customers overuse the system without clear value. Plus, unlimited usage can push developers into financial losses if they can’t predict or control consumption. These models also fail to address customer perception; companies are wary of paying for AI that might not deliver, especially with so many pilot projects failing. Without linking price to tangible outcomes, agent makers struggle to justify their worth, and customers feel like they’re gambling on unproven tech.
Can you explain how a results-based billing system could help agent developers demonstrate the value their AI solutions bring to customers?
Absolutely. Results-based billing flips the script by focusing on outcomes rather than inputs. Instead of charging for seats or usage hours, the system measures specific impacts—like how much time or cost an agent saves through automation. For example, if an AI agent streamlines a sales process and cuts down manual work by a certain percentage, the billing reflects that direct benefit. This approach makes value transparent to customers, so they’re not just paying for a tool but for a result they can see and measure. It also pushes developers to prioritize effectiveness, ensuring their agents deliver real impact rather than just activity.
How does tying billing to measurable outcomes address the skepticism many companies have about investing in AI projects?
Skepticism around AI often comes from the high failure rate of pilots—many projects don’t make it to production because they can’t prove their worth. Results-based billing tackles this head-on by shifting the risk. Companies aren’t shelling out upfront for promises; they’re paying only when the AI delivers something concrete, like improved efficiency or cost reduction. This builds trust because the financial commitment is directly tied to success. It also forces agent developers to focus on practical, high-impact use cases rather than flashy but ineffective features, which helps combat the perception that AI is just hype without substance.
What challenges do you foresee in implementing a billing model that depends on defining and measuring ‘results’ for AI agents?
Defining and measuring results is tricky because not all outcomes are straightforward to quantify. For instance, an AI agent might improve decision-making in subtle ways that don’t immediately show up in hard numbers like revenue or time saved. There’s also the challenge of setting fair benchmarks—both developers and customers need to agree on what constitutes a ‘result’ worth paying for, which can vary wildly by industry or use case. On top of that, building the infrastructure to track and attribute value accurately requires sophisticated data systems, which can be a barrier for smaller startups. It’s a complex puzzle, but solving it is key to making this model work at scale.
How do you think results-based billing could evolve to support AI agents that perform background tasks which might go unnoticed by customers?
Background tasks are a big part of what AI agents do, but their value often flies under the radar. Results-based billing can evolve by creating mechanisms to surface and quantify this hidden work. For instance, dashboards or reports could highlight how an agent’s background processing—like data analysis or predictive maintenance—prevents issues or saves resources over time. Billing could then be tied to these indirect benefits, even if they’re not immediately obvious. It’s also about educating customers on the cumulative impact of these quiet contributions, ensuring they understand why they’re being charged for work that isn’t front-and-center but still drives value.
What is your forecast for the future of AI agent billing models as adoption grows across industries?
I believe we’re just at the tip of the iceberg with AI agent billing. As adoption grows, I expect results-based models to become the standard because they align incentives so well—customers pay for value, and developers are rewarded for impact. We’ll likely see more granular and customizable billing tied to specific KPIs, tailored to different industries like healthcare, logistics, or sales. There’s also potential for hybrid models that blend outcome-based fees with minimal base subscriptions to cover infrastructure costs. Over the next decade, I think competition will drive innovation in how we define and measure results, ultimately making AI more accessible and trusted across the board.
