The Complete Guide to AI Agent Pricing Models in 2025

Over the past year, I've had countless conversations with businesses trying to navigate the complex world of AI agent pricing. As the founder of Agentman, I've seen firsthand how pricing confusion can become a major barrier to AI adoption, especially for smaller businesses. Let's explore the four core pricing models that have emerged in the market and their real-world implications.

The Four Core Pricing Models

1. Per-Execution (Run-Based) Pricing

At Agentman, we are pioneering this approach specifically for the SMB market. The concept is simple: you pay one fixed price for each completed task your agent performs, regardless of complexity or duration. Think of it like hiring a virtual employee—you're paying for outcomes, not inputs.

This model emerged from hundreds of conversations with business owners who wanted predictable costs without becoming AI experts. Whether your agent handles a quick product query or manages a complex multi-step return process, the price remains constant. All technical costs—API calls, model usage, infrastructure—are included, ensuring no surprises.

2. Outcome-Based Pricing

Sierra.ai has embraced a results-oriented approach, charging only when agents achieve specific business outcomes. This model is ideal for sales-focused use cases, such as generating qualified leads or closing sales.

However, defining success can be tricky for customer support or operational tasks. Questions like "What constitutes a successful outcome?" or "How should partial successes be billed?" complicate calculations. For instance, does resolving a delayed order count if the customer remains dissatisfied? These challenges often lead to disputes and complex billing.

3. Per-Conversation Pricing

Salesforce's Agentforce illustrates both the appeal and limitations of this model. At $2 per conversation, it works well for straightforward support interactions but struggles with complex enterprise scenarios. For sales agents, Salesforce switches to per-lead pricing, and for custom agents, they use a credit-based system.

Beyond these limitations, Salesforce's platform dependencies significantly increase costs. SMBs must also purchase Salesforce software licenses ($100s per user monthly), API call allowances, and Data Cloud access. For many, these additional costs make this model impractical.

4. Usage-Based Pricing

This model charges based on computational resources used (e.g., API calls, tokens processed, or compute time). While developers may appreciate this approach, it creates unnecessary complexity for most business users who prefer to focus on solving problems rather than monitoring token usage.

Platforms like Langbase and Wordware use this model but often combine it with hybrid options to appeal to a broader audience.

The Rise of Hybrid Models

Many platforms now mix and match pricing approaches. For instance:

Salesforce combines $2 per conversation for prebuilt agents with an entirely different "AI Credits" system for custom agents, involving calculations based on message counts, API calls, and Data Cloud usage.
Sierra.ai uses outcome-based pricing for some use cases and switches to consumption-based pricing for others, depending on complexity.

While hybrid models offer flexibility, they also increase complexity, making it harder for businesses to predict costs. Developers often spend excessive time optimizing for costs rather than improving user experience.

The Hidden Costs Nobody Talks About

In my experience, platform and implementation costs often catch businesses off guard. Here's what to expect:

Enterprise Platforms: Platforms like Salesforce's Agentforce and Sierra.ai typically require $50,000 to $200,000 in professional services fees and 3-6 months of implementation time.
Ongoing Costs: Include licensing fees, training, and integration work.

Different Businesses, Different Needs

Enterprises:

Usage-based pricing can work well for enterprises with technical teams capable of managing complex integrations and optimizing usage.
Large companies often negotiate hybrid models tailored to their specific needs.

SMBs:

Simplicity and predictability are key. At Agentman, our per-execution model allows SMBs to focus on growth without worrying about complicated pricing calculations.

Real-World Cost Comparison

Here's an example for handling 10,000 customer interactions monthly:

Comparison of AI Agent pricing models showing costs for different pricing approaches including Outcome-Based, Per-Conversation, Usage-Based, and Per-Execution models Figure: Cost comparison of different AI agent pricing models for 10,000 monthly interactions

Note: Factor in implementation and maintenance costs, which vary based on agent customization.

Looking Ahead

The AI agent pricing landscape is evolving rapidly. Key trends to watch:

Industry-Specific Pricing Models: Tailored to vertical markets.
Value-Based Pricing: Emphasizing measurable business outcomes.
Simplified SMB Solutions: Easier adoption for smaller businesses.
Transparent Total Cost of Ownership: Better clarity on true costs.

Making Your Choice

Based on my experience, here are six steps to guide your decision:

Evaluate Current Costs: Assess your costs without AI agents and potential savings or revenue gains.
Define ROI Goals: Prioritize use cases with clear ROI potential.
Assess Technical Capabilities: Understand your team's ability to manage complexity.
Estimate Interaction Volume: Determine scalability requirements.
Consider Implementation Costs: Be mindful of setup and ongoing management expenses.
Plan for Future Scaling: Choose a model that accommodates growth.

Remember, the cheapest option isn't always the most cost-effective. Simplicity and predictability can save money by making costs easier to manage.

Join the Conversation

What's been your experience with AI agent pricing? Share your challenges and successes in the comments below. Let's learn from each other and continue the conversation!