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Unlock the Power of Caching to Scale AI Solutions with LangChain Caching Comprehensive Overview 14 min read

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Despite the transformative potential of AI applications, approximately 70% never make it to production. The challenges? Cost, performance, security, flexibility, and maintainability. In this article, we address two critical challenges: escalating costs and the need for high performance and reveal how caching strategy in AI is THE solution.

Running AI models, especially at scale, can be prohibitively expensive. Take, for example, the GPT-4 model, which costs $30 for processing 1M input tokens and $60 for 1M output tokens. These figures can quickly add up, making widespread adoption a financial challenge for many projects.

To put this into perspective, consider a customer service chatbot that processes an average of 50,000 user queries daily. Each query and response pair might average 50 tokens for both. In a single day, that translates to 2,500,000 tokens, up to 75 million input 75 million output and in a month. At GPT-4s pricing, this means the chatbots owner could be facing about $2250 in input token costs and $4500 in output token costs monthly, totaling $6750 just for processing user queries. What if your application is a huge success, and you have 500,000 user queries or 5 million user queries per day?

Todays users expect immediate gratification a demand that traditional machine learning and deep learning approaches struggle to meet. The arrival of Generative AI promises near-real-time responses, transforming user interactions into seamless experiences. But sometimes generative AI may not be fast enough.

Consider the same AI-driven chatbot service for customer support, designed to provide instant responses to customer inquiries. Without caching, each query is processed in

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Maximizing AI Efficiency in Production with Caching: A Cost-Efficient Performance Booster - Towards Data Science