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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 today's rapidly evolving security landscape, organizations are increasingly turning to AI agents to automate and enhance their security operations. With the rise of agentic AI in cybersecurity, one critical question emerges: what's the optimal pricing model for these sophisticated security solutions? Should vendors charge per seat, per action, or based on outcomes? Let's explore the pros and cons of each approach to help security leaders and procurement teams make informed decisions.
The Security Operations Automation Revolution
Security operations centers (SOCs) face unprecedented challenges: alert fatigue, talent shortages, and increasingly sophisticated threats. AI agents designed specifically for security operations automation promise to address these challenges by handling routine tasks, accelerating threat detection, and enabling human analysts to focus on high-value activities.
Unlike traditional security tools, these new security AI agents can:
- Autonomously investigate alerts
- Make decisions within defined guardrails
- Learn from analyst feedback
- Orchestrate responses across multiple security systems
But with this new technology comes new questions about fair and effective pricing models.
Per-Seat Pricing: The Traditional Approach
Per-seat pricing has long been the standard for enterprise software, including security tools.
Advantages
- Predictability: Organizations can easily budget based on team size
- Simplicity: Straightforward to understand and administer
- Alignment with traditional procurement: Fits existing purchasing processes
Disadvantages
- Limited value correlation: A small team using AI agents extensively may derive significantly more value than a large team with limited usage
- Scalability concerns: Creates artificial barriers to deploying the technology where it could provide value
- Potential underutilization: Organizations may restrict access to stay within seat limits, reducing overall security posture
According to Gartner, by 2025, over 60% of enterprises will shift away from traditional per-seat licensing for security tools toward more flexible consumption models.
Per-Action Pricing: Usage-Based Models
Usage-based pricing ties costs directly to the volume of actions the AI agent performs, creating a more direct connection between use and cost.
Advantages
- Pay for what you use: Direct correlation between usage and cost
- Scalability: Can easily scale up during incidents or high-alert periods
- Efficiency incentives: Encourages optimizing the deployment for maximum efficiency
Disadvantages
- Unpredictable costs: May lead to budget surprises during major incidents
- Potential hesitation to use: Teams might avoid using the tool to control costs
- Complex measurement: Defining what constitutes an "action" can be challenging
A recent study by OpenView Partners found that SaaS companies with usage-based pricing models grew at a 29% higher rate than those with purely subscription-based models.
Outcome-Based Pricing: The Value-Driven Approach
Perhaps the most innovative approach is outcome-based pricing, where costs are tied directly to measurable security improvements or outcomes.
Advantages
- Perfect value alignment: Organizations pay based on actual security value delivered
- Risk sharing: Vendors have skin in the game for customer success
- Focus on results: Both parties concentrate on improving security outcomes
Disadvantages
- Measurement challenges: Defining and measuring security outcomes can be complex
- Attribution problems: Difficult to isolate the impact of the AI agent from other security controls
- Contract complexity: Requires sophisticated agreements and monitoring mechanisms
According to Forrester Research, outcome-based pricing models for security technologies remain rare but are growing in popularity, especially among innovative vendors confident in their solutions' effectiveness.
Hybrid Models: The Emerging Standard for AI Agents
Many security vendors offering agentic AI solutions are now implementing hybrid pricing approaches that combine elements of multiple models.
Credit-Based Pricing
One increasingly popular approach uses a credit system where different actions consume varying amounts of credits based on their complexity or value. Organizations purchase credit bundles and can allocate them however they choose.
The benefits include:
- Flexibility: Organizations decide how to use their credits
- Predictability: Upfront purchase of credits provides budget certainty
- Value alignment: More complex or valuable actions cost more credits
Tiered Usage Models
Another approach combines a base subscription with tiered usage levels:
- Base tier: Covers fundamental capabilities with predictable costs
- Consumption tiers: Additional usage charged at predetermined rates
- Outcome accelerators: Discounts or bonuses based on achieving certain security outcomes
Factors to Consider When Selecting a Pricing Model
When evaluating pricing models for security operations AI agents, consider:
Organizational maturity: Larger enterprises with established security programs may benefit from outcome-based approaches, while smaller organizations might prefer the predictability of seat-based pricing.
Usage patterns: Organizations with highly variable security event volumes may benefit from credit-based systems that allow for peaks without penalty.
Budget structure: Some organizations have inflexible budgets that work better with predictable subscription costs rather than variable usage fees.
LLM Ops considerations: With AI agents built on large language models, consider how the underlying LLM costs scale with usage.
Orchestration capabilities: More sophisticated agents that can orchestrate actions across multiple systems may deliver more value, justifying premium pricing.
Guardrails and governance: Solutions with robust guardrails that prevent improper actions may reduce risk and therefore justify different pricing structures.
The Future of Security AI Agent Pricing
As agentic AI continues to revolutionize security operations, pricing models will evolve to better align costs with value. We expect to see:
More outcome guarantees: Vendors increasingly offering guarantees or refunds if specific security outcomes aren't achieved
Dynamic pricing: Prices that adjust based on the complexity of security environments or threat landscapes
Community multipliers: Discounts for contributing to collective defense or sharing anonymized threat intelligence
Value-based tiers: Different pricing for preventative actions versus remediation or response actions
Conclusion: Choose the Model That Aligns With Your Security Goals
The ideal pricing model for security operations automation should align vendor success with your security outcomes. Whether you opt for seat-based, action-based, outcome-based, or a hybrid approach, ensure the model:
- Encourages rather than inhibits usage where it provides value
- Scales appropriately with your organization
- Provides reasonable predictability for budgeting
- Creates the right incentives for both your team and the vendor
As AI agents become central to modern security operations, selecting the right pricing model isn't just a procurement decision—it's a strategic choice that impacts how effectively your organization can leverage this powerful technology to improve your security posture.
The most successful security leaders will choose pricing models that align with their unique needs while creating partnership-oriented relationships with vendors where both parties succeed when security improves.