Rethinking the Economics of Agentic AI: When ‘Cheap’ Gets Complicated
Everyone thinks AI is getting cheaper. But is it really?
At first glance, the economics of AI seem to be improving for everyone. Thanks to continued model optimization and advances in hardware, the cost of running LLMs (also known as inference) is steadily decreasing. Developers today can access incredibly powerful models at a fraction of what it cost just a year ago.
But there’s a catch.
While the price per LLM query is decreasing, the number of queries required to solve real-world problems is dramatically increasing. The rise of agentic AI workflows – where multiple LLM calls are chained together to complete more complex tasks – has introduced a surprising twist: even as individual calls get cheaper, overall AI usage is getting more expensive.
This introduces a new kind of trade-off. While the unit cost of AI is going down, building truly valuable experiences often requires many more units. In effect, AI is becoming cheaper per step but more expensive per solution.
Inference is getting cheaper, but the economics haven’t caught up
The downward trend in inference costs is real and inevitable. Smaller, domain-specific models now deliver performance levels once exclusive to general-purpose giants. Optimizations in architecture, better hardware utilization, and improved orchestration are driving this change. The result is faster and cheaper access to core AI capabilities.
But this affordability can be misleading.
Most LLM providers are not yet operating at scale in a profitable way. Many model providers are still operating at a loss, subsidizing usage with investment dollars to capture market share. While underlying compute costs are decreasing, it’s unclear whether end-user pricing will follow suit or whether we’re simply in an artificially low-cost window.
At the same time, AI usage patterns are evolving. Traditional AI integrations were often built around simple interactions where a prompt in dictates a response out. But solving meaningful problems often requires multiple rounds of reasoning, context gathering, and decision-making. This is where agentic AI comes into play, chaining together dozens, or even hundreds, of LLM calls.
As AI agents become more common, the number of LLM queries per task can increase exponentially. What once took one call might now require fifty. Multiply that across a workflow, and even inexpensive queries add up quickly.
The result is a new cost equation. AI is cheaper per interaction, but more expensive per outcome. And without established best practices for orchestrating agentic workflows, many teams are still navigating unknowns around efficiency and scale.
Now, understanding AI economics means looking beyond price per call and focusing instead on the full cost of delivering value.
Rethinking how and where AI adds value
As inference gets cheaper, product teams are reevaluating where AI fits within the user experience. Rather than integrated into flagship features, they’re now infused into everyday interactions. Tasks that once required brittle rules or human input, like interpreting intent or resolving ambiguity, can now be handled by a single LLM call. Lower costs make these micro-decisions newly viable.
But as agentic workflows become more common, the economics shift again. Chaining multiple LLM calls increases total cost, latency, and potential failure points. This shifts the focus from per-query pricing to cost per successful outcome where customers pay only when an agent completes a task successfully. While this adds pressure to optimize behind the scenes, it aligns better with how businesses measure value: not in tokens used, but in jobs done.
This shift is also changing how companies think about AI investment. As agents take on tasks once performed by people, LLM usage and the productivity it brings begins to resemble labor spend more than infrastructure cost. That reframes AI not just as a technical layer, but as a strategic lever for scale.
For many organizations, these shifts require new thinking around cost modeling, ROI, and system design. It’s no longer just about whether an LLM can answer the question, but rather whether the entire system can consistently deliver the right answer at the right cost.
What’s next: ROI, saturation, and strategic patience
As agentic AI matures, adoption won’t scale evenly. Most organizations are still in exploration mode – testing workflows, measuring impact, and searching for repeatable value. In many cases, that value hasn’t materialized yet. And that’s okay.
The teams that break through will be the ones that find reliable, cost-effective workflows where AI consistently delivers outcomes at scale. But until then, most companies will cap their spend, waiting for clearer ROI signals. This creates a landscape where AI spending may plateau for the majority but spike dramatically for the few who crack the code.
That’s why strategic patience matters. Falling infrastructure costs make experimentation easier, but turning prototypes into production-ready agents still takes iteration and product-market fit. The challenge isn’t building more agents but making them useful, reliable, and cost-effective.
Organizations that stay ahead will continuously test assumptions, adapt to emerging capabilities, and treat AI as a system-level investment with long-term payoff.
SmartBear’s approach: leaning into exploration
At SmartBear, our focus is on exploring what’s possible.
As LLM costs decline, the team is intentionally embracing high-query experimentation to understand where agentic workflows create real value. In this phase, more queries aren’t a liability but a path to insight. The goal is to map how complex tasks unfold, identify which interactions are necessary, and surface patterns that can later be optimized.
This approach is especially useful in areas like testing, where model-based agents must perform nuanced, context-aware actions across varied environments. Rather than trying to minimize calls from the start, the team is investing in flexible orchestration that allows agents to reason, retry, and adapt as needed. You can see more of this in action within SmartBear Test Hub.
By giving agents room to explore SmartBear is laying the groundwork for more reliable, repeatable agentic systems. Optimization will come later. Right now, the priority is learning what good looks like.