Artificial intelligence (AI) has captivated the business world with its potential to drive efficiency and transform operations. Large language models like OpenAI’s GPT-5 and Google (NASDAQ:GOOGL)’s Gemini 2.5 have become focal points. However, for many companies, the reality of implementing AI largely revolves around inference rather than just the models themselves. Inference — the phase where AI models actually deliver predictions, insights, or responses — creates ongoing costs that can accumulate swiftly and perhaps unexpectedly.
In earlier discussions, the primary emphasis was often placed on the pre-training of AI models, a process likened to educating college students through general coursework. This foundational training is resource-intensive but largely a one-off event. Enterprises typically rely on third-party providers like OpenAI, Google, or Microsoft (NASDAQ:MSFT) to conduct these training sessions, thus largely outsourcing the burden of initial model development. This setup contrasts with the persistent and scalable nature of inference, which requires continuous computational power and incurs substantial operational costs each time AI is queried.
What is the true cost of AI inference?
Inference is fundamentally about applying pre-trained models to new data, which demands computational resources every single time a query is made. A chatbot responding to user questions or a system identifying fraud cases are practical examples of inference at work. Unlike the fixed cost of training, running these inference tasks results in ongoing expenses that quickly add up, affecting the bottom line.
How do companies deal with inference costs?
Many enterprises perceive model training as a precursor handled by technology giants, while inference presents a recurring fiscal challenge that they must manage internally. By transitioning from cloud-based solutions to self-hosted alternatives, businesses like a construction company, which saw costs shift from less than $200 to $10,000 monthly, managed to stabilize these expenses at around $7,000. Through optimization and strategic approaches, firms strive to manage these ever-present costs.
Inference costs have shown signs of reduction. Noteworthy, the 2025 AI Index Report from Stanford highlights a 280-fold decrease in inference costs. This trend promises financial relief for enterprises depending on AI. Yet, given that AI adoption is escalating, the potential for accumulating expenses persists. The reality remains that adoption rates continue to rise, with roughly half the tech companies seeing positive returns on investment, according to PYMNTS Intelligence.
The proliferation of AI solutions in customer service, where countless inquiries necessitate constant tokens or text chunk processing, exemplifies the ongoing nature of AI inference expenses. Loss leaders, like lower-cost options for services such as ChatGPT, have effectively amplified the reach of AI by offering limited-access at minimal cost, thereby increasing the user base into hundreds of millions weekly.
“Pretraining a model — the process of ingesting data, breaking it down into tokens and finding patterns — is essentially a one-time cost,” according to Nvidia. “But in inference, every prompt to a model generates tokens, each of which incur a cost.”
A shift in perspective is necessary for companies to truly manage AI-related expenses effectively. Business leaders are starting to recognize that managing inference costs crucially impacts their operations, outstripping the initial glamor of novel AI model developments. Firms are advised to analyze their inference strategy critically to optimize financial outcomes.
Pavel Bantsevich, product manager at Pynest, noted, “Costs ballooned to $10,000 a month once people started using [the analytics tool].” This highlights the importance of considering inference in resource planning.
Effectively strategizing for AI inference costs involves balancing operational methodologies with financial foresight. Firms must determine how they allocate computing tasks, whether through cloud providers or on-premises solutions, while preparing for potential expenditure escalation as AI adoption widens. Understanding these economic dimensions will guide companies in navigating their AI investments intelligently.
