Developer creates semantic cache for LLMs to cut cloud costs

A developer has created a Go library to address the scaling challenges of Large Language Models (LLMs) by implementing a semantic caching mechanism. This solution tackles the issue of repeated, costly LLM calls for similar user queries by employing a two-tiered lookup system. The first tier uses deterministic hashing for identical requests, while the second tier leverages vector similarity search to identify semantically similar prompts, thereby reducing cloud bills for enterprises. The library is designed to be backend-agnostic, supporting various vector databases and embedding models. AI

IMPACT Reduces LLM operational costs for enterprises by enabling efficient caching of similar queries.

RANK_REASON Developer-created library for LLM scaling.

Read on dev.to — LLM tag →

• Azure
• LLM
• Ollama
• OpenAI
• Pinecone
• Qdrant
• Redis
• semantic-cache-for-llm
• Suraj
• Together
• Weaviate

• infra
• product

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