AI Pricing for Agencies: Choosing the Best Approach

It seems that AI is everywhere these days. In fact, it’s so ubiquitous that a recent Gallup poll found that nearly all Americans—99%—interact with AI-enabled products at least once a week. 

What does this mean for agencies? As AI tools become a staple in everyday life, your clients will increasingly expect you to not only integrate them into your offerings, but provide clear, competitive pricing that maximizes their value. 

To meet those expectations, agencies will need a deeper understanding of AI’s capabilities, its potential costs, and the best way to position these services as part of a compelling value proposition. 

Below, we’ll take a closer look at: 

• The kinds of AI tools and services agencies can offer, 
• Common pricing models, along with their relative advantages and disadvantages, 
• How agencies can identify the best fit(s) for their clients and business needs.

What kinds of AI tools and services should agencies offer?

Leveraging AI in your service offerings can refine or even transform your agency—boosting your reputation as an innovator, providing increasing value to clients, and unlocking new revenue streams and opportunities for growth.

With a considered approach, the right tools can keep your agency ahead amid an often-shifting landscape. Here’s a few worth considering as you start to map out your own path:  

Analytical platforms

By analyzing massive datasets and uncovering patterns that traditional tools (or human eyes) might overlook, AI-powered analytical platforms help take some of the guesswork out of agency decision-making. But, these tools aren’t just for internal use—they also help agencies deliver a number of high-value services to clients, too. 

For example, agencies can offer AI-powered sales forecasting tools to help clients anticipate unexpected demand shifts, optimize resource allocation, and prioritize other high-value targets identified in analysis. Similarly, demand forecasting tools can assess variables like purchasing behavior and supply chain trends, enabling agencies to guide clients toward smarter inventory planning and promotional strategies. 

Beyond forecasting, analytical AI can also play a helpful role in preventing churn: flagging at-risk customers by identifying complex engagement or purchasing patterns. 

Agencies can employ all of these insights in high-value client services—helping brands refine customer retention strategies, optimize digital ad targeting, or personalize future marketing campaigns.

Marketing automation tools

Many agencies use generative AI to help their clients deliver automated, personalized content at scale. Done properly, this can drive some major wins for your clients—for example, one McKinsey study found that companies who “excel” at offering customers a personal touch generate 40% more revenue than their “average” competitors. 

For instance, these kinds of platforms can tweak individual messaging based on a lead’s actions, interests, and stage along the buying journey—significantly increasing the likelihood of conversion. Moreover, AI can segment audiences with highly specific precision, allowing for hyper-targeted campaigns that address niche needs and pain points. 

Natural language processing

Natural language processing (NLP) tools are capable of understanding and generating human language at scale, and can be a valuable help when engaging in communication-heavy tasks. For agencies, it’s an opportunity to offer AI-powered solutions that can enhance your client’s customer experiences while reducing their operational and labor costs.

For example: AI-driven chatbots can handle routine customer inquiries with immediate, organic-sounding responses, freeing up human agents for more complex issues. Agencies can help clients optimize and deploy these systems to reflect their brand voice or unearth other insights related to these interactions. 

Beyond chats, agencies can leverage AI to broadly analyze sentiments across reviews, social media, and support tickets—allowing brands to identify trends, anticipate concerns, and proactively adjust their messaging, when necessary. 

Custom models

While there are plenty of powerful “off-the-shelf” AI tools, some agencies may find that a one-size-fits-all approach might not fully align with all of their clients’ strategic needs. 

Here, agencies can help clients develop tailored AI solutions by  combining elements of existing approaches—like analytics, automation, or NLP—into powerful hybrid systems, fine-tuned to specific goals and outcomes. For instance, a client with a complex workflow might benefit from integrating predictive analytics with marketing automation, providing both deep insights and action.  

Though implementing these systems may require specialized expertise and resources, the payoff can be significant, both in terms of your results and reputation as an innovative, strategic partner. 

AI pricing for agencies: A closer look

Effectively pricing AI tools and services is a challenge that many agencies are now navigating for the first time. 

As usual, finding the right balance between value and accessibility is the key element of a successful pricing model. In the following sections, we’ll explore the most common pricing models, their strengths and potential drawbacks, and how agencies can choose the best fit for their clients and business goals.

Value-based and performance-based

This pricing model ties the cost of services directly to the results they generate for clients. Rather than flat fees, agencies charge based on measurable outcomes, like increased revenue or reduced costs. For example, an agency might take a percentage of revenue growth from an AI-driven campaign or charge fees tied to other metrics, like cost-savings.

• Pros: Aligns the agency’s incentives with client success, fostering trust and long-term relationships. Clients are usually open to this model, as it allows them to see quantifiable results before committing significant resources.
• Cons: Depending on the relevant metrics used, this system could require a fair degree of robust tracking and reporting mechanisms to ensure accuracy. Additionally, it may not be suitable for agencies offering services with more abstract or indirect outcomes (e.g. brand awareness campaigns). It could also be a risk for agencies in the event a campaign produces unsuccessful results. 
• Ideal for: High-impact AI solutions—e.g. optimization, predictive analysis—that deliver measurable performance improvements.

Subscription-based and retainer-based pricing

Charging clients a recurring fee for AI-powered services or other ongoing support can lead to predictable costs for your clients, along with new streams of recurring revenue for your agency.

Subscription-based pricing works well for agencies offering ongoing AI-driven services.  For example, an agency might charge a monthly subscription fee for performance monitoring, analytics, or optimization. Similarly, a retainer agreement could provide a client with guaranteed access to your team’s experts for regular maintenance or campaign adjustments.

• Pros: A steady and predictable revenue stream, with ongoing support and regular updates for clients.
• Cons: May require extensive initial setup and ongoing maintenance that other models won’t.
• Ideal for: AI services that need regular updates and support, such as analytics platforms or marketing automation tools.

Tiered pricing

Tiered pricing structures can provide some flexibility for clients while ensuring recurring revenue growth for your agency. 

For example, a digital marketing agency might offer basic AI-powered campaign automation in its standard package, while higher-tiered plans include advanced AI-driven audience segmentation, predictive analytics, or personalized content generation.

• Pros: Attracts a large pool of users by offering entry-level services at a low cost. 
• Cons: A slight balancing act: This approach requires a careful balance between offering enough value at the introductory tier  to attract users, while reserving premium features that are compelling enough to encourage future upgrades.
• Ideal for: Agencies looking to scale their user base quickly with offerings like AI-driven productivity tools or chatbots.

Usage-based and hourly-based pricing

This model ties costs directly to the amount of resources or time a client uses, making it both flexible and scalable. 

For example, an agency offering AI-driven data processing might charge per terabyte of data processed, ensuring clients pay only for what they use. Similarly, hourly-based pricing can apply to services like AI model training or troubleshooting, where project timelines can often vary depending on complexity.

• Pros: Flexible and scalable, aligning costs with actual usage. Transparent and easy for clients to understand.
• Cons: Revenue can be unpredictable if client usage is inconsistent.
• Ideal for: Custom AI projects where the scope may change, or services that can easily track usage metrics, such as cloud computing resources or data processing services.

Project-based and service-based pricing

This model allows agencies to set clear costs for one-off projects with a defined scope or to license custom-made solutions for ongoing support or optimization.  For example, an agency might charge a flat fee for building an AI-powered recommendation system tailored to a client’s business needs.

• Pros: Offers a clear cost structure for clients and works best for projects with well-defined parameters and goals. Service contracts can generate ongoing, predictable revenue.
• Cons: Project-based pricing may limit revenue potential if new projects are not consistently acquired and these types of projects require significant upfront development investments. Unforeseen hurdles or costs could be an issue. 
• Ideal for: Agencies delivering custom AI-powered services, such as tailored automation solutions, predictive analytics, or machine learning models with long-term optimization needs. 

Hybrid pricing

Hybrid pricing—you guessed it—combines different approaches so agencies can more precisely tailor their approach. This flexibility makes it an attractive option for agencies with a range of AI-enhanced service offerings or clients with varied requirements.

The possibilities are pretty broad: an agency might charge a subscription fee for ongoing support or management, while incorporating usage-based pricing for other features or performance-based elements tied to measurable results. This approach can help agencies maximize revenue while offering clients a more customized pricing structure.

• Pros: Highly adaptable, allowing agencies to fit pricing to client needs and project specifics. Can help maximize revenue by combining multiple models.
• Cons: Complex to manage and communicate to clients. Requires clear contracts and robust tracking systems.
• Ideal for: Agencies with diverse service offerings and varied client requirements.

Recommendations for agencies

So—where’s an AI-curious agency to go from here? 

For many agencies, subscription or retainer-based pricing tends to make the most sense. These models can lock in steady revenue streams while also fostering client relationships and trust over time. 

However, flexibility remains key for agencies with diverse offerings or clients with varied needs. In these cases, incorporating elements of performance-based or hybrid pricing models can help tailor your approach and maximize client satisfaction. 

Ultimately, the best pricing strategy is one that aligns with your agency’s services and client expectations. Strategic pricing, clear communication, and a commitment to client success will set your agency apart in this increasingly breakout field.