Token Optimization

Technical terms
About 1 min read

An optimization process that strategically manages the usage of tokens, which are the basic units of data processed by LLMs, to reduce API call costs and improve the model's Time to First Token (TTFT) and overall inference performance.

Also known as
Token ManagementPrompt CachingContext CachingPrompt Compression

Detailed explanation

Token optimization is an essential technology for ensuring both the cost-efficiency and performance of LLM services. Going beyond simply shortening prompt lengths, 'Prompt Caching'—which keeps recurring system instructions or large-scale knowledge bases in server memory for reuse—plays a key role. Major providers such as OpenAI, Anthropic, and Google offer up to a 90% price discount and more than an 80% reduction in latency for cached tokens. In addition, token efficiency can be enhanced through 'prompt compression' which retains only the core context, 'model routing' which allocates tasks to lower-cost models depending on difficulty, and 'semantic chunking' in RAG environments. This is a key metric that determines the actual Total Cost of Ownership (TCO), particularly when choosing enterprise AI tools that need to process large volumes of documents or maintain long conversation contexts.

Why It Matters in Tool Selection

LLM API costs are proportional to token usage, and non-optimized services incur exponential cost increases when processing large volumes of data. Especially in services centered on multi-turn conversations or document analysis, operating costs can differ by up to 10 times depending on support for 'prompt caching', which directly impacts service profitability and market competitiveness.

What to Look For

  • Does the API provider specify a 'prompt caching' feature and corresponding discount policy?
  • Is it structured to separate static system instructions and dynamic user inputs to increase the cache hit rate?
  • Does it include compression technology that removes unnecessary tokens while maintaining model accuracy for long context inputs?

Example

For instance, when an AI chatbot referring to a 10,000-token legal guideline pays the full rate for the first question, and then, for subsequent questions in the same session, retrieves the already calculated guideline tokens from the 'cache' to instantly generate responses at one-tenth of the cost.

Related terms

Prompt EngineeringContext WindowLatency