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Cutting LLM Token Costs

Cutting LLM token costs with practical methods for lowering agent and API bills, including prompt caching, spend caps, and local indexing. Compare methods side by side.

8 articles·Updated Oct 25, 2026
Illustration of a metering pipe with a hard mechanical stop gate limiting the flow of tokens, representing a hard spend cap on metered AI API usage.

How to Limit AI API Costs With Hard Spend Caps

Learn how to set hard, pre-call spend caps on AI APIs, provider by provider, plus loop guards and rate limits that stop a runaway agent's bill.

limit AI API costsAI API spend caphard spend limit
A diagram-like physical sequence shows one large data block shrinking through five processing stations into a small cluster of compact blocks, illustrating cumulative reduction in LLM API usage.

How to Reduce LLM API Costs: 5 Methods Compared

Learn how to cut LLM API costs with prompt caching, batching, model routing, context trimming, and self-hosting, plus the pricing math behind each method.

reduce LLM API costsLLM prompt cachingLLM batch API discount
Diagram of repeated context tokens funneling into a local index grid, which filters out duplicates so only a small residual stream of tokens reaches the model endpoint below.

How to Reduce LLM Token Usage With Local Indexing

Learn how local vector databases, AST indexes, and semantic tool routers can cut LLM token spend 40-90% by sending only the context each query needs.

reduce LLM token usagelocal indexingLLM cost reduction
Illustration of a funnel filtering a dense stream of tokens through narrowing perforated plates into an open modular framework, contrasted with a sealed opaque vault, representing token cost reduction in LLM agent architectures.

How to Reduce LLM API Token Costs for AI Agents

Agentic workflows burn 5 to 1,000x more tokens than chat. Here's the caching, code execution, routing, and self-hosting playbook that cuts agent bills 40 to 90 percent.

reduce LLM API token costsAI agent costsprompt caching