
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](https://agentman.ai), 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](https://agentman.ai), 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](https://agentman.ai), 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](/images/blog/comparing-agent-ai-pricing-models-calculations.jpeg)
*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!


The Complete Guide to AI Agent Pricing Models in 2025


## Introduction

The landscape of artificial intelligence is rapidly evolving, and at its forefront are [AI agents](/blog/what-are-ai-agents) - tools that can autonomously perform tasks, drive conversations, and enhance business processes. We've seen AI agents progress from cumbersome, code-heavy frameworks to the rise of [no-code platforms](/blog/no-code-ai-development) and, more recently, to Agent-First Apps. This evolution makes AI more accessible than ever, empowering businesses of all sizes to transform their operations.

## Overview of AI Agent Evolution

AI agents have undergone a remarkable transformation. In the early days, building agents required extensive coding and deep technical knowledge, making them accessible to only a few. For example, frameworks like LangChain and LLamaIndex require proficiency and a solid understanding of Python to build agents. However, the rise of no-code platforms has shifted the paradigm. Businesses no longer need specialized programming skills to harness the power of AI. Today, anyone with an idea can leverage no-code tools like Salesforce's Agentforce and Google's Vertex AI Agent Builder to create intelligent assistants.

Beyond accessibility, we're moving towards pre-built agents and, ultimately, Agent-First Apps - where intelligent agents are at the core of a business's operational model. This evolution is the next frontier, offering companies new ways to improve customer experiences, automate workflows, and streamline operations.

## Importance of These Trends

The trends we're about to explore are fundamentally reshaping business operations, customer experiences, and employee workflows. They're also making AI accessible to a broader audience, giving businesses the tools to innovate faster and more effectively. From simplifying AI development to tailoring solutions for specific industries, these trends democratize AI and open new possibilities for organizations worldwide.

![AI Agent Evolution Timeline showing the progression from code-first frameworks to no-code platforms](/images/blog/7-Trends-Shaping-the-Future-of-AI-Agents-Future.jpg)
*The evolution of AI agents from complex frameworks to accessible no-code platforms*

### Trend 1: The Shift from Code-First Towards No-Code Agent Builders

The first significant trend is the shift towards no-code platforms, simplifying AI development. The recent introduction and popularization of these no-code agent builders, such as Salesforce's Agentforce and Google's Vertex AI Agent Builder, educate customers that no-code agent builders can build complex agents, allowing even non-technical users to build sophisticated AI agents.

The shift from building agents in code to no-code agent builders. This democratization of AI is being driven by the increasing demand for attaining ROI on AI. Companies want to innovate faster, solve customer problems more efficiently, and integrate AI into everyday workflows.

### Trend 2: Adopting Pre-Built Agents to Jumpstart AI Projects

With the rise of pre-built agents, businesses no longer need to start from scratch - even with no-code tools. Many companies prefer to deploy ready-made support, sales, and customer service agents. This trend particularly appeals to organizations lacking technical skills, as pre-built agents offer faster time-to-market, reduced complexity, and cost-effective solutions.

Pre-built agents need to be customizable to be widely adopted and used by many customers. If no-code agent builders are available, even better, as they allow businesses to start with a pre-built agent, test it, customize it, iterate, and deploy - continuously improving the agent to meet their unique needs - without involving AI Engineers.

### Trend 3: Verticalization of AI Agents

Another trend shaping the future of AI is the verticalization of AI agents. Startups are increasingly building agents tailored to specific industries - such as healthcare, legal, financial services, and software development - addressing unique workflows and challenges within those sectors.

Companies like Abridge (focused on healthcare) and Harvey (targeting legal services) are prime examples of this specialization. Vertical agents offer tailored solutions, handling industry-specific nuances more effectively and improving efficiency and outcomes for their users.

### Trend 4: The Rise of Agent-First Apps in SaaS

The evolution of Software as a Service (SaaS) is shifting towards Agent-First Apps, where AI agents are becoming the primary interface to the apps, enabling employees to accomplish a lot quickly. Rather than relying on manual processes, businesses are increasingly looking to AI agents to handle workflows, customer interactions, and internal processes.

Platforms like Zapier and Retool are moving towards agent-driven automation, signaling a broader shift toward AI-led workflows. This trend transforms SaaS from traditional user-driven models into intelligent systems capable of autonomously understanding and executing complex tasks.

### Trend 5: Large and Well-Funded Startups Focus on Enterprises

Large and well-funded startups primarily focus on enterprises or enterprise use cases, leaving small and medium-sized businesses (SMBs) underserved. The current generation of no-code Agent Builders often requires integration with enterprise software such as Salesforce or cloud providers like Google Cloud, which can demand thousands of dollars in implementation services.

For example, Sierra.ai charges $100,000 as a starting price for its projects, which typically require a consulting engagement. Additionally, companies like Accenture, IBM, and even hyperscalers are charging upwards of $50,000 to do AI transformation projects or implement Generative AI MVPs.

### Trend 6: Leveraging API Platforms to Build Cross-App Conversational AI Agents

Workato's expansion into conversational AI agents (Workato Genies) signals a shift towards agentic orchestration, where AI agents are not built on a single app ecosystem but are being built across multiple app ecosystems using cross-platform APIs to handle complex, cross-functional workflows. These AI agents engage users through conversational interfaces like Slack and Microsoft Teams, making it easier for employees to work across multiple SaaS clouds such as ServiceNow, Salesforce, Workday, and more.

### Trend 7: Generative UI - Bringing Rich Interactions to Chat-Based Agents

Last but not least, generative UI is an important step in transforming chat conversations. Users no longer need to sacrifice rich interactivity when engaging with agents through chat. Instead, they can experience app-like functionality directly in a conversation.

Salesforce's Generative Canvas for Salespeople is an example of this transformation. Thanks to generative AI-powered UX integrated into Salesforce CRM, the Generative Canvas provides dynamically generated experiences that utilize the entire screen. These experiences include text, tables, charts, trends, and other rich visual Lightning components to represent key metrics.

## Conclusion: The Future of AI Agents - A Unified Platform for All

From no-code agent builders to pre-built solutions and agent-first apps, these trends shape the future of AI development and adoption. Businesses of all sizes can leverage these technologies to improve efficiency, reduce costs, and offer personalized experiences to their customers.

As these trends converge, AI agents are becoming integral to business operations, automating complex workflows, improving customer interactions, and enabling organizations to innovate faster. The future of AI is about bringing the power of agents to everyone - making intelligent automation the new normal for businesses worldwide.


From No-Code Builders to Agent-First Apps - 7 Trends Shaping AI Agents


When ChatGPT went public over a year ago, it gave the world unfettered access to the most advanced AI models. We were able to realize firsthand what AI can do for us. We began imagining how to use it to unleash our creativity and boost productivity.

Large language models (LLMs) have dismantled all the barriers to entry, empowering businesses of all sizes to unlock the transformative power of AI, regardless of budget or technical expertise. Many of you have built compelling LLM applications.

But with great power comes great complexity — choosing the right path to build and deploy your LLM application can feel like navigating a maze. Based on my experience guiding LLM implementations, I present a strategic framework to help you choose the right path. This framework categorizes LLM applications into three distinct approaches, each tailored to your specific needs, skillset, and time-to-market goals.

![Strategic framework for Building LLM applications in your business](/images/blog/Strategic-framework-for-Building-Generative-AI-in-Your-Business.png)

## Off-the-Shelf Gen AI

One way to simplify the process is by adopting a "plug-and-play" approach. You can achieve this by utilizing pre-trained and hosted models like OpenAI or Palm 2. These models offer developer-friendly APIs that allow you to build advanced applications with minimal technical expertise. Frameworks such as LangChain and LLamaIndex make this even more easier.

By understanding the complementary strengths of the following three fundamental techniques — prompt engineering, functions & agents, and RAG — you can unlock LLMs' full potential and build truly transformative applications.

### The Secret Sauce: Prompt Engineering

For this approach to be successful, it is essential to provide the right instructions. That's where prompt engineering comes in. Your prompts must be clear, with detailed commands telling the model what you want it to do and should not do. Remember to include the context and data to generate the correct response and personalize it by telling it what style and tone.

If you feel the responses are generic or verbose, employ few-shot learning to teach the LLM by example.

### Supercharge Your Model with Superpowers: Functions and Agents

If you need to go beyond text generation, look into employing functions and agents. Imagine your LLM App ordering pizza based on your preferences — that's the magic of functions and agents. Functions are tiny extensions, enabling your model to interact with APIs, databases, and other systems.

You can employ agents if you build applications beyond a simple question and answer or a simple task. Agents can understand the entire context, break down multi-step objectives into smaller steps, and adapt their responses across multiple conversation turns. Imagine an agent seamlessly navigating a complex customer service interaction, analyzing sentiment, reasoning through the issue, and making informed decisions across multiple exchanges.

Combining the flexibility of functions and agents with the power of prompt engineering will unlock a broader range of possibilities for your LLM applications.

### Knowledge is Power: Boosting Accuracy with Retrieval-Augmented Generation (RAG)

Prompt engineering relies on crafting instructions for the model, but it can't guarantee factual accuracy or real-world grounding. RAG solves this by retrieving relevant information from a knowledge base before generating a response. Imagine asking your LLM app for historically accurate creative content or a chatbot confidently answering policy questions based on internal knowledge. That's the magic of RAG.

You can combat hallucinations by verifying information and preventing fabricated details. Plus, you can ask the LLMs to explain their responses by citing your sources. Finally, RAG excels at understanding context, leading to nuanced and relevant responses in complex situations.

By leveraging one or more of these three techniques, you can develop complex LLM applications capable of summarizing support conversations, searching through thousands of documents, and creating task-oriented chatbots.

The best part is that you don't need to hire AI engineers for this; full-stack engineers would suffice. And, since you are utilizing proprietary models, you don't need to worry about the complexities of hosting these models.

You can launch your applications swiftly and with minimal expenses.

## Customized Gen AI Models

While the Off-the-shelf approach offers plug-and-play convenience, some tasks demand a custom approach. Enter Customized Gen AI models, where you take pre-trained models and fine-tune them like a race car for optimal performance.

If you want the model to understand your domain, data, and brand voice without including that with every prompt, fine-tune the model. For example, a travel company specializing in travel recommendations can fine-tune a pre-trained model to understand its unique data and brand voice. This model could generate personalized itineraries that consider their customers' past travel preferences, current weather conditions, and ongoing events — making them feel like VIPs.

### Level up your control:

With fine-tuned models, you can write highly converting product descriptions for your eCommerce site, generate medical reports on your specific clinical data, or understand the sentiment of customer reviews.

Though fine-tuning a model is slightly more expensive and time-consuming than the off-the-shelf approach, it is still cheaper than training a model from scratch, as you are only tweaking a smaller subset of the model's parameters to suit your needs.

This path also does not require AI expertise. Full-stack engineers with data science skills can effectively fine-tune and integrate models into your infrastructure.

If you are using proprietary models, you can save time and money on DevOps.

## Train Your Own Models on Proprietary Knowledge

There comes a point when you need a Gen AI solution tailor-made to your unique requirements — something that off-the-shelf or even fine-tuned models can't fully address. That's where training your own models on proprietary knowledge enters the picture.

Imagine a hospital developing a custom AI model trained on their vast patient data. This model could analyze medical scans and predict disease risk with unprecedented accuracy, potentially saving lives and revolutionizing healthcare.

### Mastering Your Domain:

Training custom models are compelling when dealing with niche domains or sensitive data requiring more specificity. Whether it's creating predictive models for financial markets or diagnostic tools for rare diseases, this path allows you to leverage your proprietary datasets to develop models that can provide insights and accuracy that generic models cannot match.

### Innovating on Your Terms:

When you train your own models, you innovate on your terms. You choose the data, training methods, and performance metrics. This approach is for forward-thinking businesses building unique AI capabilities.

### Navigating the Challenges:

Training machine learning models from scratch is challenging and resource-intensive. With careful planning, you can gain complete control over the AI's capabilities, and the potential for competitive advantage and innovation is vast.

### Big investments:

This route demands more data, expertise, and time. You'll need a team of ML engineers, data scientists, and engineers for model building, training, and lifecycle management. It's a significant investment, but the ROI can be substantial for high-stakes industries.

## Choosing the right path

We've explored three paths to building your LLM applications, each with unique advantages and challenges. To help you navigate this golden triangle of Time, Cost, and Control, I summarized the three paths in this handy comparison table:

![Comparing different LLM App Development Paths](/images/blog/Comparing-different-LLM-App-Development-Paths.png)

## Conclusion

So, which path will you take? Whether you're a budding entrepreneur or a seasoned leader, there's a generative AI approach that can empower your business. Start exploring today, unleash your creativity, and let AI be the wind beneath your wings.


Agentman Blog

Posts tagged with "enterprise ai"

The Complete Guide to AI Agent Pricing Models in 2025

From No-Code Builders to Agent-First Apps - 7 Trends Shaping AI Agents

A Strategic Framework for Building LLM Applications