Introduction Generative AI has become a shared C-Level priority with many enterprises setting goals in their annual statement and numerous press releases. As Generative AI is gaining traction, there is much anticipation around their evolving model performance capabilities. However, as developers increasingly move beyond Generative AI pilots, the trend is shifting to compound systems. The SOTA results often come from compound systems incorporating multiple components rather than relying solely on standalone models. A recent study by MIT Research has observed that 60% of LLM deployments in businesses incorporate some form of retrieval-augmented generation (RAG), with 30% utilizing multi-step chains or compound systems. Rise of Compound Systems A Compound AI System addresses AI tasks through multiple interconnected components, including several calls to different models, retrievers, or external tools. AI models are constantly improving, with scalability seemingly limitless. However, com
Introduction: In the rapidly evolving landscape of Generative AI (Gen AI), managing the scale and cost of Large Language Models (LLMs) presents a formidable challenge for enterprises diversifying their application portfolios. As organizations increasingly integrate these powerful tools across various services, the absence of comprehensive visibility and cost controls can easily steer budgets into the red. Karini AI steps in as a game-changer, offering a meticulously designed dashboard that not only sheds light on the otherwise opaque realm of Gen AI expenditures but also puts the reins of cost management firmly in the hands of businesses. Exploring Karini’s Dashboards: Karini’s dashboards allow you to examine your cost, usage, and resource statistics thoroughly. They enable you to identify cost drivers, the most widely used resources, such as models and connectors, and overall statistics about data ingestion and deployment completions. It offers the following capabilities: Statisti