PromptOps emerges as AI costs spiral out of control

As artificial intelligence models grow more sophisticated, they’re also becoming more expensive to operate. The latest large language models (LLMs) can process vast amounts of information and deliver increasingly nuanced responses, but this enhanced capability comes with a hidden cost: dramatically higher computational expenses that can quickly spiral out of control.

This challenge has sparked the emergence of “prompt ops,” a new discipline focused on optimizing how businesses interact with AI systems to maximize efficiency and minimize costs. Unlike prompt engineering, which focuses on crafting effective AI queries, prompt ops treats AI interactions as an ongoing operational challenge requiring continuous monitoring, measurement, and refinement.

“Prompt engineering is kind of like writing, the actual creating, whereas prompt ops is like publishing, where you’re evolving the content,” explains Crawford Del Prete, president of IDC, a global technology research firm. “The content is alive, the content is changing, and you want to make sure you’re refining that over time.”

The hidden costs of AI interactions

Understanding AI costs requires grasping how these systems actually work. When you send a query to an AI model, you’re charged based on “tokens”—roughly equivalent to words or parts of words that the system processes. Both your input (the prompt) and the AI’s output (the response) generate token costs, and longer interactions mean higher bills.

The problem becomes more complex with advanced reasoning models like OpenAI’s o1 and o3 systems. These models can handle more context and think through problems more thoroughly, but they also consume significantly more computational power—measured in FLOPS (floating-point operations per second)—which translates directly to higher costs.

“The more a model takes in and puts out, the more energy it expends and the higher the costs,” notes David Emerson, an applied scientist at the Vector Institute, a Canadian AI research organization. Some aspects of these transformer-based models scale quadratically with input length, meaning doubling your input can potentially quadruple your computational costs.

Consider this simple example that illustrates the cost problem:

User prompt: “Answer the following math problem. If I have 2 apples and I buy 4 more at the store after eating 1, how many apples do I have?”

Typical AI response: “Let me work through this step by step. You start with 2 apples. Then you eat 1 apple, leaving you with 1 apple. After that, you go to the store and buy 4 more apples. So you add 4 apples to your remaining 1 apple, which gives you a total of 5 apples.”

This verbose response generates unnecessary tokens and buries the actual answer. A more efficient prompt might specify: “Start your response with ‘The answer is'” or “Wrap your final answer in bold tags,” producing a concise result that costs less to generate and process.

When sophisticated AI becomes counterproductive

Advanced AI techniques like chain-of-thought prompting—where models work through problems step-by-step—can dramatically improve accuracy for complex tasks. However, these same techniques become expensive overkill for simple queries that don’t require deep reasoning.

“Not every query requires a model to analyze and re-analyze before providing an answer,” Emerson emphasizes. “They could be perfectly capable of answering correctly when instructed to respond directly.”

Another common cost driver is “context bloat”—the tendency to include excessive background information in prompts. While comprehensive context can improve AI performance, it often leads to an “everything but the kitchen sink” approach that unnecessarily increases computational costs without proportional benefits.

The emergence of prompt ops

These challenges have created demand for prompt ops, which applies software development principles to AI interactions. Just as DevOps revolutionized software deployment and maintenance, prompt ops focuses on the entire lifecycle of AI prompts—from initial creation through ongoing optimization and performance monitoring.

The discipline becomes particularly crucial as AI infrastructure remains scarce and expensive. “How do I squeeze more out of these very, very precious commodities?” Del Prete asks, referring to the GPU processing power that drives AI systems. “Because I’ve got to get my system utilization up, because I just don’t have the benefit of simply throwing more capacity at the problem.”

Early prompt ops platforms include QueryPal, Promptable, Rebuff, and TrueLens, though the field remains nascent. These tools help organizations monitor prompt performance, identify cost inefficiencies, and automatically optimize AI interactions over time.

Del Prete predicts that AI agents will eventually handle much of this optimization automatically: “The level of automation will increase, the level of human interaction will decrease, you’ll be able to have agents operating more autonomously in the prompts that they’re creating.”

Common costly mistakes and how to avoid them

Most organizations make predictable errors when implementing AI systems that drive up costs unnecessarily. The most frequent mistakes include:

Insufficient specificity: Vague prompts force AI models to generate longer, more exploratory responses as they try to cover all possible interpretations. Instead of asking “How should we improve our marketing?” specify exactly what you need: “Provide three specific tactics to increase email open rates for our B2B software company, focusing on subject line optimization.”

Ignoring problem simplification: Complex queries often can be broken into simpler, cheaper components. Rather than asking an AI to analyze an entire market research report, consider whether you can extract key data points first and then ask targeted questions about specific findings.

Underutilizing structure: AI models excel at pattern recognition and can process structured formats like bullet points, numbered lists, and even code-like formatting more efficiently than free-form text. Requesting responses in JSON or Markdown format also makes them easier to process programmatically.

Overlooking validation and monitoring: Unlike traditional software, AI responses can vary over time as models are updated or as your prompts interact with different contexts. Successful prompt ops requires ongoing performance monitoring, ideally with validation sets that can detect when response quality degrades.

Practical implementation strategies

Organizations beginning their prompt ops journey should focus on several key areas. First, establish baseline measurements for current AI usage, including token consumption, response times, and task success rates. This data provides the foundation for optimization efforts.

Next, implement structured testing approaches. Tools like the open-source DSPy framework can automatically configure and optimize prompts based on labeled examples, while built-in optimization features in ChatGPT and Google’s AI platforms offer simpler starting points.

Finally, stay current with evolving best practices. The AI field moves rapidly, and prompting techniques that work well today may become obsolete as new models and capabilities emerge. “I think one of the simplest things users can do is to try to stay up-to-date on effective prompting approaches, model developments and new ways to configure and interact with models,” Emerson suggests.

Looking ahead

Prompt ops represents more than just cost optimization—it’s becoming essential infrastructure for organizations that want to scale AI usage effectively. As models become more powerful and expensive, the discipline of systematically managing AI interactions will likely determine which organizations can afford to maintain competitive AI capabilities.

The field is still emerging, but early adopters are already seeing significant returns on their optimization efforts. As Del Prete notes, “When we look back three or four years from now, it’s going to be a whole discipline. It’ll be a skill.” For organizations serious about AI adoption, developing prompt ops capabilities now may prove as crucial as mastering cloud computing was a decade ago.