Reducing the costs

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.