LLM Cost Metrics: How to Measure and Cut AI Inference Expenses

When you run a large language model, a type of AI system trained on massive text datasets to generate human-like responses. Also known as LLM, it powers everything from chatbots to code assistants—but running it isn’t free. The real cost isn’t just the price per API call. It’s hidden in token usage, the basic units of text processed by the model, which directly impact pricing and speed, KV cache, the memory used to store past attention results during inference, now bigger than the model weights themselves, and compute footprint, the processing power needed to run each request, often the bottleneck in real-time apps.

Most teams think cost is about model size. It’s not. A 7B model with poor prompt design can cost more than a 70B model with smart compression. The top performers track LLM cost metrics like cost per query, tokens per second, and memory per concurrent user. They use tools like FlashAttention to slash KV cache memory, INT8 quantization to reduce compute needs, and SwiftKV to reuse cached data across similar prompts. Some cut token costs by 80% using prompt compression—not by dumbing down the input, but by removing redundancy without losing meaning. Others use chain-of-thought distillation to run smaller models that mimic big ones, dropping inference costs by 90% while keeping accuracy above 90%. These aren’t theory experiments. They’re daily practices at companies running LLMs at scale.

If you’re paying for LLMs, you’re already paying for memory, compute, and tokens. The question isn’t whether you can afford it—it’s whether you’re measuring the right things. The posts below show exactly how teams are tracking these metrics, fixing leaks, and building cheaper, faster AI systems. You’ll see real examples: how one startup cut its monthly bill by $12,000 with simple prompt tweaks, how a healthcare app reduced latency by 60% using caching, and why some teams avoid big models entirely—not because they’re weak, but because they’re overkill.