Best Practices for Optimizing Serverless Applications for Cost and Performance

Serverless computing is becoming increasingly popular due to its pay-per-use and scalability features. For developers, it frees them from the hassle of managing infrastructure while focusing on building applications. However, inefficiencies in serverless code can result in higher costs and degraded performance. In this article, we'll go over some of the best practices for optimizing serverless applications for cost and performance.

1. Efficient Code

Efficient code is crucial to optimize both cost and performance. Serverless functions should be small and targeted to specific use-cases instead of trying to do too many things. This will prevent unnecessary resource usage, particularly when running multiple functions on a serverless platform. Instead, break down the functionality into smaller, more manageable functions. Additionally, developers should leverage the available libraries, as it can help reduce the amount of code and resources necessary.

2. Function Sizing

Developers should take the time to properly size serverless functions. Sizing functions refers to the allocation of CPU and Memory requirements required by an application that runs in the serverless environment. This allocation can have a significant impact on costs and performance. The ideal function sizing involves considering the peak and average CPU and memory usage of your application. Bigger function sizes can result in higher costs, while smaller sizes can result in longer function execution times. The several serverless providers in the market provide good guidance on function sizing. AWS recommends starting with a 128 MB and tune it for the specific workloads.

3. Optimal Load Balancing

The optimal load balancing strategy is another key factor in optimizing serverless applications for cost and performance. In this context, serverless providers balance the load across different resources available. They use different algorithms and models to keep performance efficient and resource utilization low. Developers should explore different options to find an optimal load balancing strategy. It will help them ensure enough resources are allocated to avoid latency or delays. Making more resources available only makes sense if there are users or events to consume them. Provisioning resources beyond the required peak load is always a bad idea.

4. Use Caching and Warehousing

Caching and warehousing are great ways to optimize serverless applications' performance while reducing costs. Caching is the practice of storing frequently accessed data in servers closer to end-users to reduce the retrieval time. Warehousing is a way of storing data in long-term, low-cost storage options like serverless data warehouses. Data retrieved and collected in code within a transaction despite the latency is a common waste of resources. Overusing redundant queries, database reads, and data access APIs are common culprits. Using caching and warehousing techniques can reduce the time and money expended on requests.

5. Leverage Existing Cloud Services

There are many cloud services that developers can leverage to optimize their serverless applications. For example, AWS provides a collection of infrastructure services for networking, databases, security, and other areas. Its serverless platform can integrate and work seamlessly with these services. Developments can take advantage of these existing services to reduce upfront development time and provide more optimal cost-performance ratios. Using pre-existing services can also help reduce the time and effort required by operations teams. Since less complex systems need less manual handling and maintenance, operating cost remains low.

6. Monitoring and Debugging

Effective monitoring and debugging are critical for maintaining efficient environments. It allows developers and operators to identify and fix issues that may impair cost and performance. Developers should identify and evaluate the appropriate monitoring and debugging tools for their serverless application. The serverless providers offer custom monitoring tools to developers for their respective platform. Also, other cloud services like DataDog and New Relic can measure the application's performance and provide visibility into the application stack.

7. Lifecycle Management

Proper lifecycle management of serverless applications can help their performance and reduce costs. Serverless applications consist of a range of resources that must be maintained well or disposed of from time to time, depending on the resources used. Developers need to define the appropriate lifespan for each function, events, data storage, API gateway, and other application components. Developers can also use a combination of custom and platform-specific tools to manage the lifecycle of each resource effectively.

8. Distributed Tracing

Distributed tracing is a method of tracking transactions or functions in complex microservice environments. It helps developers find and fix detrimental performance problems across interdependent parts of applications. It enhances performance so the waste of server resources is minimized. Developers can use open tracing libraries and stack trace analysis tools to help locate and diagnose problematic areas in their applications. These tools, included in providers' dashboards, can help to improve applications' performance, optimize cost and minimize the work of human operators.


Serverless computing is a highly scalable, highly cost-effective system that is rapidly becoming a popular business model for developers. However, optimization of serverless applications is crucial to avoid inefficient code and high costs. With proper development practices, developers can optimize serverless applications for performance and cost. In this article, we explored some of the best practices to do so. With the help of a combination of efficient coding habits, optimal load balancing, caching, and monitoring, developers can maximize server resource usage for building highly efficient serverless applications. Implementing distributed tracing, using existing cloud services can also help streamline the development and operational processes. Keeping these things in mind will help businesses and applications get the most out of their serverless systems, and minimize costs at the same time.

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