Deploying AI agents at scale can lead to high API charges. Processing thousands of long context prompts daily accumulates token costs quickly and increases response latency.
1. Semantic Prompt Caching
Save previous queries and responses in memory. If a new user query is semantically similar to an older request (e.g. 95% vector proximity), serve the cached response immediately, bypassing the LLM API call entirely.
Using semantic caching tools (like Redis) can reduce API billing and drop response latencies down to single-digit milliseconds for repeated queries.
2. Dynamic Model Routing
Do not use expensive models (like GPT-4o or Claude 3.5 Sonnet) for basic categorization. Route simple tasks (like entity extraction or language detection) to lighter, faster models (like GPT-4o-mini or Llama 3 8B), reserving heavy models for complex logic.
3. Prompt Compression
Strip unnecessary adjectives and redundant sentences from your system instructions. Compress long RAG logs to contain only the key metrics, reducing input token overhead by up to 30%.