Cheap Tokens, Expensive Decisions

Why building your own AI stack costs more than you think

Large language models (LLMs) have fundamentally changed enterprise software development. With a capable model and an AI coding assistant, a small team can build applications that would have required months of work only a few years ago. The rise of "vibe coding" has lowered the barrier even further, enabling business users and developers alike to create AI-powered tools with unprecedented speed. That is a remarkable shift, but it has also created a misconception.

Many organizations compare the cost of an enterprise platform with the cost of running an LLM, and the conclusion often appears obvious: building seems significantly cheaper. But the reality often proves very different.

From tokenmaxxing to value optimization

Not long ago, the goal for many organizations was simple: get people using AI. Teams were encouraged to experiment, build assistants, automate tasks, and integrate LLMs wherever they could create value, adopting an "AI-first" mentality. As inference costs continued to fall, models became more capable, and hype continued to dominate the headlines. That experimentation accelerated even further, and using more AI felt like progress.

Today, many of those same organizations face an uncomfortable reality. Their AI bills continue to grow, yet the business value often isn't keeping pace, and the conversation is changing. Instead of asking, "How do we reduce the cost per token?", leaders are beginning to ask a more important question: "Was an LLM the right technology for this workload in the first place?" That represents a much healthier way of thinking about enterprise AI.

The objective never was to consume a certain number of tokens. It's to generate more business value for every dollar spent. Sometimes that means using an LLM because it genuinely creates an advantage. In many other cases, a purpose-built technology can perform the task more predictably, more efficiently, and at a lower total cost. The most effective AI architectures won't maximize LLM usage. They'll maximize the value created by combining the right technologies for the right jobs.