Reducing the costs of cloud computing: some tips

Serverless computing is on the rise, and with it comes the potential risk of increasing cloud spending. Enterprises need to take a proactive approach to serverless costs through strategy, optimization, and monitoring. Here are some tips on how to prepare your development team to approach serverless cloud costs proactively, rather than reactively. Firstly, choosing the right serverless computing solution starts with your cloud service provider (CSP). Each major CSP offers serverless computing with its own pricing model. It is important to learn these pricing models to ensure cost-effectiveness. For instance, Amazon Web Services offers tiered pricing for AWS Lambda. Efficient coding practices are vital in minimizing serverless computing costs. To reduce costs, minimizing the size of the code, using optimal libraries, and optimizing function performance is important. During the design phase, it is essential to determine the appropriate amount of resources that each serverless function requires to minimize costs. Training your cloud developers to use compute only when necessary is crucial in cost optimization. For example, using step functions to call APIs instead of Lambda functions means that you only pay for the step functions. Setting and tracking relevant serverless cost KPIs is also important. Core to managing and tracking serverless costs is embracing KPIs including cost per execution, function duration, idle time, memory usage, CPU usage, number of invocations, and error rate. Most organizations’ cloud FinOps expertise and practices are still in growth mode. Adding serverless computing to your technology stack can raise new specters of cloud spending concerns, making a full-court approach to managing serverless cloud costs essential. Automating cost management practices when feasible and implementing cost monitoring and alerts over your serverless computing projects early in the project lifecycle can help manage these costs effectively. Consider upgrading to a serverless cost optimization tool as part of your overall commitment to serverless computing. New cloud optimization tools focus on the optimization of serverless costs, such as Epsagon, IOpipe, and Lumigo, which use machine learning algorithms to analyze your usage patterns and recommend further optimizations to reduce costs. In conclusion, adopting serverless computing requires a proactive approach to serverless costs through strategy, optimization, and monitoring. Choosing the right serverless computing solution, introducing efficient coding practices, right-sizing functions starting at the design phase, and using compute only when necessary are all vital steps. Setting and tracking relevant serverless cost KPIs, playing full court when it comes to cost monitoring and alerts, and considering using a specialized serverless cost optimization tool can all help to minimize serverless computing costs.

Microsoft adds a Copilot Chat, Semantic Kernel SDK

Microsoft has recently unveiled a new open-source sample app, Copilot Chat, which serves as a perfect tool for developers who are looking to integrate AI and large language model (LLM) intelligence into their own applications. The app is built to showcase the capabilities of Microsoft's Semantic Kernel SDK and can be utilized to construct chatbots featuring natural language processing, file uploading, and speech recognition. The possibilities of Copilot Chat are vast, and developers can use it to create applications for various sectors, such as customer service, e-commerce, training and education, HR, and more. The ability to scale is one of the main advantages of chatbots, according to Microsoft. Chatbots help businesses to satisfy rising demand without the need for hiring more employees, resulting in cost reductions and increased revenue. Additionally, chatbots can enhance user experience, increase efficiency, provide personalized recommendations, and improve accessibility. Developers can enhance chatbots' intelligence by using LLM-based AI, with updated information provided through the Semantic Kernel. Copilot Chat is described as an "enriched intelligence app," which means it will continue to learn and improve with use. To use the sample app, developers will need to upgrade to the latest version of Semantic Kernel from GitHub and follow the provided instructions. It's important to note that while Copilot Chat is an excellent educational tool, it's not recommended for production deployments. The Semantic Kernel SDK is designed to be a lightweight tool that allows developers to combine conventional programming languages with the most recent LLM AI prompts, as well as templating, chaining, and planning capabilities. With these features, developers can build chatbots that are intuitive, efficient, and intelligent, leading to improved customer satisfaction and a competitive edge.