In a recent video from the Agentic AI Series, Akhil from Monetizely tackles the critical question of whether agentic AI should be expensive. The video, titled "The True Cost of Agentic AI: Foundation vs Small Language vs Modular Orchestration Models Explained," breaks down the economics of different AI model approaches and provides strategic insights for businesses implementing AI agents.
The Cost Paradox in Agentic AI
Akhil begins by highlighting a fundamental paradox in the current AI landscape. "Per-call AI costs are dropping faster hardware, optimized models, and scale are driving prices down," he explains. "On the other, agentic workflows chain many goals together. Perception, reasoning, tool access, memory. So the total cost per outcome can skyrocket."
This is the central challenge for businesses: while individual model calls become cheaper, the complex chains of operations required by sophisticated AI agents can lead to significantly higher total costs.
Understanding Foundation Models: Power at a Price
Foundation models like GPT-4 and Claude offer impressive capabilities for general reasoning and flexibility. However, these benefits come with substantial costs:
"They are fantastic for flexibility and open-ended understanding, but they are expensive. I compute usage costly appearance and limited fine-tuning options," notes Akhil. "When used heavily in an agentic chain, every single step adds to the cost. If an agent needs multiple API calls or sequential reasoning, you pay that price again and again and again."
This multiplicative cost effect can quickly become prohibitive for businesses trying to scale AI agent deployments.
The Case for Small Language Models
Recent research suggests that smaller, specialized language models may offer a more sustainable approach to building agentic systems. Akhil highlights their advantages:
"Small language models are not just cheaper, they are the more sustainable option for agentic systems. And here is why. They are optimized for narrow, repetitive tasks. They can run faster and cost less, up to 10 to 30 times cheaper and faster than large language models."
Despite their smaller size, these models can achieve comparable task accuracy for many typical agent workflows, including "tool calling, instruction following, and structured reasoning." This makes them ideal for modular tasks where specialized functionality is needed rather than broad reasoning capabilities.
Modular Orchestration: The Middle Path
The third approach Akhil discusses is using modular orchestration models - essentially "agent managers" that intelligently route tasks between different types of models:
"Think of them as agent managers. Routing sub-tasks intelligently between small language and foundation models. Some systems employ hybrid schedulers that decide where each step should run locally in the cloud or a specific."
This hybrid approach allows businesses to optimize for both cost and performance by:
- Offloading routine tasks to cheaper small language models
- Reserving expensive foundation models only for complex reasoning tasks
- Implementing caching systems that reduce redundant computations
The results can be dramatic: "Systems like PlanCaching reuse workflows to reduce repeated computation, cutting costs by up to 47% per task," Akhil points out.
Strategic Decision-Making Framework
Rather than choosing a one-size-fits-all approach, Akhil recommends aligning your model selection with specific task requirements:
"For open-ended reasoning, use foundation models. For repetitive, rule-based use small language model. For hybrid Multi-step chaining orchestration models."
This task-based approach ensures you're not overpaying for capabilities you don't need while maintaining performance where it matters most. Akhil emphasizes that "The goal is not just cheaper AI, it is strategically priced AI."
Key Questions for Implementation
To implement this strategic approach effectively, businesses should ask themselves:
- What agentic tasks are high-value versus routine?
- Can workflows be modularized?
- Where is the cost-performance sweet spot for your specific use case?
Conclusion
"Agentic AI does not have to be costly," Akhil concludes. "By aligning models to tasks and using smart orchestration, you create systems that are both effective and economical. Stay tactical, start modular and scale responsibly."
This strategic approach to AI model selection enables businesses to build sophisticated agentic systems without unsustainable costs. By understanding the strengths and limitations of different model types and implementing intelligent orchestration, companies can deliver powerful AI capabilities while maintaining financial efficiency.
As Akhil mentions at the end, future episodes of the series will explore how agentic AI delivers measurable ROI in B2B software, further addressing the business case for these technologies.