Hello, wanted to share an article on how we built semantic cache using Pinecone to cut costs and latency for models like GPT-4.
The cache stores responses to similar queries, avoiding redundant API calls. We’re seeing 20% cache hit rates, delivering 20x faster responses at no additional cost. (at 10M GPT4 requests a day, that’s $2,700 saved a month)
The article covers:
- Examples of cached queries
- Latency, accuracy benchmarking
- Technical details
- Production use considerations
Hope you find it helpful! Check it out here: ⭐ Reducing LLM Costs & Latency with Semantic Cache
Let me know if you have any questions!
