Token Pricing: How LLM Costs Work and How to Cut Them
When you use a large language model, you’re not just paying for intelligence—you’re paying for tokens, the basic units of text that LLMs process, whether they’re words, parts of words, or punctuation. Also known as text chunks, these are what determine how much you spend every time you ask a question or generate content. Every word you type, every response you get, gets broken into tokens—and each one has a price. It’s not the model size alone that adds up; it’s how many tokens fly back and forth. A single conversation can use thousands, and if you’re running this at scale, those costs multiply fast.
That’s why prompt compression, a technique that shortens input text without losing meaning, helping reduce token usage and lower costs is no longer optional. Tools like LLMLingua can shrink your prompts by up to 80%, cutting your bill without making answers worse. Then there’s KV cache, the memory storage that holds previous tokens during generation, often taking up more space than the model weights themselves. If your system isn’t optimized for it, you’re wasting compute—and money—on repeated work. And it’s not just about input. The length of the output matters too. Longer responses mean more tokens, more memory, and higher latency. That’s why teams using LLMs for customer support or internal tools focus on concise outputs, not just accuracy.
Real savings come from understanding the whole chain: how your prompts are built, how the model processes them, and how memory is managed. Companies that track token usage per user, per query, and per feature see their LLM budgets drop by 40% or more. It’s not about using the biggest model—it’s about using the right one, the right way. You’ll find posts here that break down exactly how to compress prompts, how KV cache impacts your infrastructure costs, and why smaller models with smart prompting often beat larger ones in real-world use. These aren’t theory pieces. They’re the tactics teams are using right now to keep AI affordable, scalable, and sane.
Latency and Cost as First-Class Metrics in LLM Evaluation: Why Speed and Price Matter More Than Ever
Latency and cost are now as critical as accuracy in LLM evaluation. Learn how top companies measure response time, reduce token costs, and avoid hidden infrastructure traps in production deployments.