Stop chatting Constrained VS Unconstrained LLM use cases (medium.com)

The ongoing debate around large language models (LLMs) often conflates their usefulness with profitability, leading to polarized opinions about their real-world value. This discussion gains clarity by distinguishing two main categories of LLM use cases: constrained and unconstrained. Constrained use cases involve tightly controlled tasks with limited token usage, straightforward outputs (e.g., JSON responses, short summaries), and well-defined goals. These applications—like web scraping, autocomplete, and basic code reviews—offer predictable, verifiable results and tend to be more cost-effective and profitable for businesses. Examples include Firecrawl and Cursor’s tab feature. In contrast, unconstrained use cases allow greater freedom both in input and output length and often tackle complex, open-ended tasks such as iterative coding, full app development, or no-code tool interactions. These require larger context windows, generate more tokens, and produce results that are subjective and harder to evaluate, driving up costs and variance in usefulness. This category is closely associated with chat-based interfaces like ChatGPT, which popularized unrestricted, conversational LLM use. While unconstrained applications are more exciting and flexible, they often struggle with profitability due to fixed subscription pricing and high resource consumption. The key takeaway is that sustainable LLM business models currently favor constrained usage, where cost, predictability, and value align better. Unconstrained LLMs, propelled by unlimited chat norms established by OpenAI, create challenges in monetization and efficiency. For broader viability, companies must either develop cheaper, optimized models or rethink pricing toward per-token charges and innovate constrained integrations rather than defaulting to freeform chat interfaces. This framework helps demystify LLM adoption debates and spot clearer paths toward profitable AI deployments.