EchoCache
Stop paying twice for identical LLM queries
What it does
Ultra-low latency semantic caching layer for OpenAI, Anthropic, Gemini, and open-source LLMs. Reduce API bills by 80% and accelerate responses to <30ms.
Ultra-low latency semantic caching layer for OpenAI, Anthropic, Gemini, and open-source LLMs. Reduce AI API bills by up to 80% and accelerate responses to <10ms using int8 scalar quantized vector similarity search.
EchoCache intercepts your LLM calls, maps prompt semantics in a local vector space, and returns cached responses in under 10ms . Save 80% on API costs without blocking runtime execution. Traditional exact-match caches fail on LLMs due to slight wording adjustments. EchoCache maps inputs into vector spaces to capture semantic equivalence. Compresses vector dimensions using 8-bit scalar quantization. Fits 4x more items inside local cache indexes, minimizing memory usage with zero latency degradation. Sentence embeddings calculate cosine distance to match prompts even when syntax is completely rephrased. Fine-tune confidence requirements (e.g. 0.92) per endpoint to balance accuracy vs. cache…from echocache.vercel.app
Does the same job
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Hey folks, As much as we love GPT-4, it's expensive and can be slow at times. That's why we built GPTCache - a semantic cache for autoregressive LMs - atop the vector database Milvus and SQLite. GPTCache provides several benefits: 1) reduced expenses due to minimizing the number of requests and tokens sent to the LLM service 2) enhanced performance by fetching cached query results directly 3) improved scalability and availability by avoiding rate limits, and 4) a flexible development environment that allows developers to verify their application's features without connecting to LLM APIs or…
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