Managing Cost and Latency in LLM Applications
Explore practical techniques for managing cost and latency in large language model applications. Understand token usage billing, and apply strategies like model selection, prompt engineering, caching, batching, and asynchronous processing to optimize performance and expenses. Gain insights into balancing trade-offs between cost, latency, and output quality for scalable, efficient LLM deployments.
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The data handling safeguards covered in previous lessons, such as cleaning, PII redaction, and access control, all carry operational overhead. But there is a broader cost challenge that compounds on top of those safeguards. Every single API call to a large language model incurs a measurable expense, driven primarily by how many tokens the model reads and generates. Unlike traditional software where compute cost is relatively fixed per request, LLM costs scale directly with the volume and length of each interaction.
Consider an enterprise customer support system handling 10,000 queries per day. Each query includes a system prompt, retrieved context documents, and the user’s question. If token usage goes unmanaged, monthly costs can spiral from hundreds to tens of thousands of dollars without any increase in the number of users. Latency compounds the problem. Slow responses degrade user experience and reduce throughput.
This lesson walks through the economics of LLM API calls, explains how token consumption translates to billing, and introduces four practical strategies enterprises use to bring both cost and latency under control. Those strategies are caching, model selection and routing, batching, and async processing.
How token usage drives billing
To understand LLM costs, you first need to understand what you are actually paying for. LLM providers do not charge by the number of API calls or by wall-clock time. They charge by tokens. The exact tokenization varies by model and provider, but the billing principle is consistent.
Providers like OpenAI, Anthropic, and AWS Bedrock charge separately for input tokens and output tokens, often at different rates. Input tokens include everything the model reads: the system instructions, any context documents injected through a RAG pipeline, and the user’s actual query. Output tokens cover the generated response. Both sides of the transaction contribute to the bill. ...