---
Title: "How to Reduce LLM Token Costs for Enterprise AI"
Url: "https://computer.io/answers/how-to-reduce-llm-token-costs"
Topic: "Agentic AI"
Description: "Cut LLM token costs: retrieve less but better context, cache, route simple tasks to smaller models, trim prompts and monitor usage."
---

# How do you reduce LLM token costs for enterprise AI?

Reduce LLM token costs by sending the model less but more relevant context, using structured, connected data instead of long document dumps, caching repeated prompts and results, routing simple tasks to smaller models, trimming instructions and conversation history, limiting agent loops, and monitoring token use per workflow so you can fix the most expensive ones.

## Tactics

| Tactic | Effect |
|---|---|
| Better retrieval | Fewer irrelevant tokens |
| Structured data | Compact facts vs long documents |
| Prompt caching | Reuse common prefixes |
| Model routing | Small models for easy tasks |
| History summarization | Shorter context in long chats |
| Loop limits | Stops runaway agents |
| Usage dashboards | Find costly workflows |

## FAQ

### What drives LLM costs?

Input and output tokens, model size, and how many calls each task makes.

### Do longer context windows cost more?

Using more context costs more, even if the window allows it.

### How do I track token usage?

Use your platform's observability or provider dashboards, broken down by workflow.

## Related terms

- [Tokens (in AI)](https://computer.io/glossary/tokens-in-ai)
- [Context window](https://computer.io/glossary/context-window)
- [Context engineering](https://computer.io/glossary/context-engineering)

## How Computer helps

- [How computer works](https://computer.io/how-computer-works)
- [Enterprise search and answers](https://devrev.ai/enterprise-search-and-answers)

## Further reading

- [Tokenmaxxing](https://devrev.ai/blog/tokenmaxxing)
- [Ai agent cost management](https://devrev.ai/blog/ai-agent-cost-management)