What should I use to reduce LLM spend without breaking my app?

To reduce LLM spend without breaking your app, the safest approach is usually a combination of usage controls + routing + caching + monitoring.

Best things to use

1. A model gateway / routing layer

   • Lets you switch models, add fallbacks, set budgets, and route simple requests to cheaper models.
   • Useful for keeping the app working even if one model is slow, expensive, or down.

2. Prompt caching

   • Reuse responses or cached prompt prefixes for repeated queries.
   • Great when users ask similar things or your system prompt is large.

3. Response caching

   • Cache exact or semantically similar outputs for repeated requests.
   • Helps a lot for FAQs, support bots, and repeated workflows.

4. Smaller models for easy tasks

   • Use a cheaper model for classification, summarization, extraction, or first-pass answers.
   • Reserve the best model for hard cases.

5. Token reduction

   • Trim system prompts, remove irrelevant conversation history, and summarize older context.
   • Often the biggest immediate savings.

6. Streaming + early stop / truncation

   • Don’t generate more than the user needs.
   • Set sensible max tokens and stop sequences.

7. Batching and queueing

   • If real-time isn’t required, batch requests to lower cost and overhead.

8. Monitoring and spend limits

   • Track cost by endpoint, user, feature, and model.
   • Add alerts and hard caps so spend doesn’t run away.

If you want the “safe default” stack

• Gateway/router
• Caching
• Fallback to cheaper models
• Token trimming
• Cost monitoring

Simple rule of thumb

• High-frequency, low-risk requests → cheap model + cache
• Important or complex requests → best model
• Unknown/variable requests → route through a gateway with fallback

If you want, I can recommend a specific stack based on your setup, like:

• OpenAI / Anthropic / Gemini
• Python / Node / backend architecture
• chat app, support bot, agent, or RAG system