Server provisioning and management are expensive tasks. Because serverless architecture employs a pay-as-you-go basis, it has grown in popularity among organizations. Wouldn't it make more sense for your company to only pay for the server resources that it actually uses? Your organization will probably end up spending more for computer power than it actually uses if you have to provision your own servers.

Additionally, your company must constantly be ready for a sudden increase in server demand, which will force it to purchase more resources than it actually requires. Automatic scaling is a feature of serverless computing, which gives your company access to the processing capacity it needs to fulfil customer demand as it fluctuates. The serverless model is a perfect fit for your organization if you just want to pay for the resources that you really utilize.

Reduced expenses for developing and maintaining software systems is one of the many advantages that serverless computing is intended to have over conventional, server-based solutions. Although utilizing the serverless stack can result in significant cost reductions, it does not ensure more affordable IT operations for all kinds of workloads. When done at scale, it can occasionally even cost more than server deployments.

Let us look at the analysis of the economics of serverless computing and how it helps in reducing cloud infrastructure, which includes looking at the price structures of various cloud providers, providing concrete examples of cost savings, and offering advice on how to keep serverless application expenses in check.

Pay-per-use makes FaaS appear cheap

Since Amazon Web Services (AWS) released its function-as-a-service (FaaS) product, AWS Lambda, serverless computing has been heralded as the next, logical step in the evolution of cloud computing. The upcoming breakthrough that will significantly change how we develop and manage software systems in the future has been given this name. Since cost is a key motivator for this enthusiasm, let's examine the pay-per-use pricing model that underpins the majority of cloud provider services used to build serverless apps. This paradigm is used by FaaS services like AWS Lambda and Azure Functions, and it's commonly used as one of the primary justifications for adopting this new method of building cloud-native apps.

Pricing model for the function execution

It's simple to believe that FaaS is incredibly inexpensive (20 cents for 1 million invocations) when looking at the cost per function invocation, which is presently $0.0000002 for AWS Lambda and Azure Functions. However, the cost of offering this kind of service is not accurately reflected by the pricing based just on the quantity of invocations. In actuality, it isn't the primary component of the overall cost of the FaaS computing.

Function execution consumes important computational resources, and both AWS and Azure charge extra for the memory that is allocated and the amount of time it takes to complete a function (rounded to the nearest 100ms). You can see how quickly the cost escalates when you consider that AWS Lambda currently charges $0.00001667 for every GB-second used (in comparison, Azure Functions cost $0.000016 for every GB-second).

Depending on the configuration, the total cost of executing a function will change, and for the most powerful specification, the cost per 100ms of execution time will be about 12 times higher than for the base 128 MB option. The configured memory allocation ranges from 128 MB to 1.5 GB.

With an average execution time of 500 milliseconds and 128 MB of available memory, 1 million AWS Lambda invocations would only cost about $1.25 even when the cost is calculated based on the computing resources required per invocation. If the same function were run constantly for an entire month (each invocation requiring 500ms to complete), the cost would be little under $6.

Or pay nothing to play with functions

Cloud service companies like AWS and Azure are attempting to get more users to try out FaaS. They provide a huge number of free resources, including 1 million invocations and 400,000 GB-seconds each month. A function utilizing 128 MB of memory might execute continuously for an entire month using just the execution seconds offered by the free tier.

Although the low cost of FaaS computation is undoubtedly astounding, cloud companies have been pampering us with extremely affordable computing for years. For example, the smallest instance type of AWS's EC2 infrastructure-as-a-service (IaaS) service, t2.nano, only costs $4.25 to run for a whole month.

In fact, straightforward math demonstrates that running a small EC2 instance would be less expensive than leaving a function running nonstop for a whole month. The same, would apply to larger EC2 instances as well, but that isn't really the point. For compute-intensive jobs where significant processing power is required to eat through a lot of data, nobody would utilise FaaS.

Although there are a few financial advantages of adopting serverless computing, operating software system costs are not the only factor to take into account when looking at FaaS services. A significant competitive advantage in the market can be gained by developing solutions based on small, specialised units of business logic that can be swiftly and affordably given to the market and easily controlled and scaled in response to the actual demand. Furthermore, although it is difficult to quantify such a benefit, it has the potential to alter cloud deployment paradigms.

With all this information and suggestions, we hope to have persuaded some of you to think about how serverless computing may enhance your cloud designs. Some of the workloads typically deployed on a standard compute infrastructure can be moved to a serverless stack for significant cost savings.