Mastering token economics: The cost of scaling LLM applications

As businesses rapidly integrate Large Language Models (LLMs) into their core products, engineering teams are facing a new kind of operational challenge: cost predictability. Building a proof-of-concept is easy, but scaling generative AI efficiently requires a deep understanding of token economics.

If you are developing AI-powered features, you already know that API costs can spiral out of control if left unmanaged. The secret to sustainable scaling lies in understanding exactly how providers charge for inference, specifically, the crucial distinction between input and output tokens.

Our engineer, Nemanja Vasić, recently published an excellent deep dive into this exact topic, breaking down the mechanics of LLM pricing models.

Why input vs. output tokens matter

When interacting with models from providers like OpenAI or Anthropic, not all tokens are charged equal. The pricing structure is intentionally split into two categories:

• Input Tokens (Prompts): The incoming payload—including system instructions, context, and user prompts. These are billed at lower rate because the model processes input text in parallel during the prefill stage.
• Output Tokens (Completions): The generated response. These carry a bigger price tag because text must be produced autoregressively (one token at a time), making output generation the primary driver of GPU consumption and your overall API bill.

In his latest article, Nemanja breaks down the GPU hardware mechanics that force this pricing split. Understanding these physical limits is key to making better architectural decisions—like optimizing context windows and RAG pipelines—to keep your API spend predictable.

Read the full deep dive

Whether you are an engineering manager budgeting for a new AI feature, or a developer looking to optimize your system prompts, Nemanja’s technical breakdown offers practical, real-world clarity.