Large language models (LLMs) are revolutionizing how businesses operate and how research is conducted. Services like ChatGPT have made accessing these powerful AIs easier than ever. But what if you can't rely on online services due to data privacy or security? What if you need to run an LLM locally? Deploying LLMs on your own hardware can quickly get expensive, especially with the constant release of new, resource-intensive models. How do you choose the best LLM for your needs and budget? Researchers have developed a new, open-source toolkit called CEBench to tackle this very problem. CEBench focuses on the cost-effectiveness of LLM pipelines, helping you find the sweet spot between performance and price. Traditional benchmarking tools often overlook the financial aspect, focusing solely on how well a model performs certain tasks. CEBench takes a different approach by considering both the effectiveness and the economic implications of running various LLM setups. Imagine trying to find the perfect balance between a model's accuracy and the cost of running it on different hardware. This toolkit allows you to easily experiment with different configurations, from the models themselves to the way they retrieve information. CEBench then tracks how these changes affect both performance and resource usage, such as memory and processing time. For example, imagine you're building a mental health assistant that analyzes patient interviews to assess their psychological well-being. Data privacy laws mandate local data storage and LLM deployment. By using CEBench, you can test different LLMs and settings to find the most accurate yet affordable solution for your hardware. Another example is reviewing legal contracts. Here, you might prioritize an online LLM service to avoid server maintenance costs. CEBench can guide you in selecting the right service and parameters that minimize expenses while achieving the desired accuracy level. CEBench even estimates how much a given pipeline would cost to run on various cloud servers, helping you to predict expenses before committing to a specific setup. This is particularly useful for researchers and smaller companies working with tighter budgets. The toolkit isn’t perfect, though. Currently, latency estimations could be more precise. Future updates aim to provide even more accurate cost projections. Nevertheless, CEBench offers a much-needed tool for navigating the complex landscape of LLM deployment, ensuring you get the most out of your AI investment.