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Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
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In the rapidly evolving landscape of vendor risk management, agentic AI systems are transforming how organizations assess, monitor, and mitigate third-party risks. But as these AI agents become more sophisticated, a critical business question emerges: what's the most appropriate pricing model for these solutions? Should customers pay for the tools and processes used, or only for successful risk management outcomes?
The Rise of AI Agents in Vendor Risk Management
Vendor risk automation powered by AI agents represents a significant advancement over traditional manual processes. These intelligent systems can continuously monitor vendor relationships, analyze contractual obligations, assess compliance documentation, and identify potential risk factors with minimal human intervention.
Organizations deploying these solutions typically expect:
- Reduced time spent on vendor assessments
- More consistent risk evaluation
- Continuous monitoring rather than point-in-time assessments
- Enhanced compliance with regulatory requirements
- Better visibility into the vendor ecosystem
But how should these valuable capabilities be priced?
Understanding AI Agent Pricing Models
Usage-Based Pricing: Paying for the Tools
Usage-based pricing models charge customers based on their consumption of AI resources. This might include:
- Number of vendors assessed
- Volume of documents processed
- API calls to various data sources
- Computational resources consumed
- Number of risk assessments completed
According to a 2023 OpenView Partners report, usage-based pricing has grown in popularity across SaaS offerings, with 45% of companies now incorporating some form of consumption-based billing.
Outcome-Based Pricing: Paying for Results
In contrast, outcome-based pricing ties costs directly to successful results, such as:
- Identified compliance gaps
- Discovered vendor risks
- Prevented incidents
- Streamlined vendor onboarding
- Regulatory findings avoided
Research from Forrester indicates that outcome-based pricing models are gaining traction, with 38% of enterprise software buyers expressing preference for this approach.
Credit-Based Systems: The Middle Ground
Some vendor risk solutions use credit-based pricing, where organizations purchase credits that can be spent on various risk management activities. This model provides flexibility while setting predictable budget parameters.
The Case for Tool Usage Billing
Billing based on tool usage offers several advantages:
1. Transparency and Predictability
When organizations pay for the tools and resources they use, pricing becomes more transparent. Companies can directly correlate their vendor risk management activities with costs, making budgeting more straightforward.
2. Alignment with Infrastructure Costs
For AI agent providers, tool usage pricing aligns with their underlying costs. Running sophisticated LLM operations, orchestration systems, and maintaining robust AI guardrails requires significant computational resources.
Sridhar Ramaswamy, CEO of Neeva and former SVP of Ads at Google, notes: "The computational costs of running advanced AI systems are substantial. Usage-based models ensure sustainable service delivery while providing a clear cost structure."
3. Encourages Comprehensive Risk Management
When pricing isn't tied exclusively to "findings," organizations may be more willing to conduct thorough assessments across their entire vendor ecosystem rather than limiting evaluations to save costs.
The Case for Outcome-Based Billing
Outcome-based pricing also presents compelling benefits:
1. Value Alignment
Customers ultimately care about results, not the processes used to achieve them. Outcome-based pricing creates perfect alignment between vendor and customer incentives.
2. Risk Sharing Between Provider and Customer
With outcome-based pricing, the AI vendor shares in both the risk and reward. If their solution doesn't deliver valuable results, they don't get paid, creating a strong incentive for performance.
Andrew Chen, General Partner at Andreessen Horowitz, observes: "The most aligned business models in AI will increasingly shift toward outcomes rather than inputs. This fundamentally changes the relationship from vendor-customer to true partners."
3. Focus on Continuous Improvement
When revenues depend on successful outcomes, AI agent providers are motivated to continuously improve their systems' accuracy, effectiveness, and efficiency.
Best Practices for Pricing Vendor Risk Automation
Organizations considering AI agents for vendor risk management should evaluate pricing models based on these factors:
Consider Your Organization's Maturity
Early-stage vendor risk programs may benefit from usage-based models as they establish processes and determine value. More mature organizations might prefer outcome-based approaches that directly tie costs to risk reduction.
Evaluate Total Value of Ownership
Beyond the pricing structure, consider the total value delivered. A more expensive solution that identifies critical risks before they materialize may provide significantly more value than a cheaper alternative that misses key issues.
Look for Hybrid Approaches
The most sophisticated pricing strategies often combine elements of both models. For example:
- Base subscription with outcome-based success fees
- Credit-based systems with different credit costs for different activities
- Tiered usage with outcome guarantees
Real-World Implementation Challenges
Implementing pure outcome-based pricing for AI agents presents practical challenges:
1. Defining "Success" in Risk Management
What constitutes a successful outcome can be subjective. Is it the identification of a potential risk, the prevention of an incident, or something else? Clear definitions are essential.
2. Attribution Difficulties
When multiple systems and processes contribute to risk management, attributing specific outcomes to the AI agent alone can be difficult.
3. Long Feedback Loops
The value of prevented incidents may not become apparent for months or years, creating challenges for billing cycles and value demonstration.
The Future of AI Agent Pricing in Vendor Risk
As agentic AI systems evolve, we're likely to see pricing models that:
- Incorporate more sophisticated ROI measurements
- Deploy dynamic pricing based on the complexity of specific vendor relationships
- Offer performance guarantees backed by insurance
- Provide customizable pricing structures suited to different industry requirements
Conclusion: Finding the Right Balance
There's no one-size-fits-all answer to whether tool usage or outcome-based pricing is superior for vendor risk automation. The optimal approach depends on organizational needs, risk management maturity, and specific use cases.
The most successful implementations will likely feature pricing models that balance:
- Fair compensation for AI resources consumed
- Alignment with customer value received
- Shared risk between provider and customer
- Flexibility to adapt as vendor risk programs mature
As AI agents become increasingly central to vendor risk management, organizations should evaluate not just the capabilities of these systems but also how their pricing structures align with strategic risk management objectives and business outcomes.
When selecting a vendor risk automation solution, engage in detailed conversations about pricing philosophy and ensure the model incentivizes the behaviors and outcomes that matter most to your organization.