Understanding AWS Lambda Limitations in real-life applications

Understanding AWS Lambda Limitations in real-life applications


When utilizing AWS Lambda for real-life applications, it is crucial to grasp its limitations. While AWS Lambda offers significant advantages, understanding and navigating these limitations are essential for successful deployments. In this blog, we will explore the limitations of AWS Lambda in real-life scenarios. We will provide valuable insights to help you build efficient and robust serverless applications.

AWS Lambda Introduction

AWS Lambda is a serverless computing service provided by Amazon Web Services (AWS) that allows you to run your code without equipping or managing servers. Developers can focus on writing application logic without worrying about underlying infrastructure, scaling, or maintenance through AWS Lambda. With Lambda, execute your code in response to events, such as changes to data in Amazon S3 buckets, updates in DynamoDB tables, or API calls.

The key features and benefits of AWS Lambda:

  • Serverless Architecture

  • Event-Driven Execution

  • Automatic Scaling

  • Pay-per-Use Pricing

  • Wide Language Support

  • Integration with AWS Ecosystem

  • Developer Productivity

AWS Lambda Limitations

Apart from powerful serverless computing services, AWS Lambda has certain limitations that also need to be considered when designing and implementing real-life applications.

Let’s discuss some common limitations.

Execution Time Limit

AWS Lambda functions have a maximum execution time limit, which varies depending on the chosen runtime. For example, the default maximum execution time for Node.js is 5 minutes. Long-running tasks that exceed this limit may need to be broken down into smaller functions or implemented using other AWS services.

Memory and Storage Constraints

Each Lambda function has a maximum memory allocation, ranging from a few megabytes to several gigabytes, depending on the runtime. The available storage space within the function's execution environment is also limited. These constraints should be considered when handling large data sets or when utilizing external dependencies or libraries.

Cold Start Latency

When a Lambda function is invoked for the first time or after a period of inactivity, there might be a noticeable delay due to the initialization process. This latency, known as a cold start, can impact applications that require near-instantaneous response times. Strategies such as using provisioned concurrency or implementing warm-up techniques can mitigate this issue.

Concurrency Limitations

AWS Lambda imposes a maximum limit on the number of concurrent executions across all functions within an AWS account. This concurrency limit varies depending on the region and account configuration. Exceeding this limit can result in throttling of function invocations, affecting application performance during high traffic periods.

File System Access

Lambda functions operate in an ephemeral execution environment without persistent file storage. While temporary storage is available during function execution, it cannot be relied upon for long-term storage. Applications requiring file system access may need to leverage alternative storage services like Amazon S3 or Amazon EFS.

VPC Networking Challenges

Lambda functions within a Virtual Private Cloud (VPC) require additional setup and configuration. Running functions inside a VPC can introduce complexities related to network connectivity, security group configurations, and availability of internet access. This can impact the ease of deployment and increase latency for functions accessing resources outside the VPC.

Limited Execution Environment Customization

Although AWS Lambda supports multiple programming languages, the execution environment is pre-configured and managed by AWS. This means certain system-level customizations, such as installing specific software or modifying operating system settings, are not directly possible within the Lambda environment.

Deployment Package Size

AWS Lambda imposes a maximum size limit for deployment packages, which varies based on the runtime. If the code and dependencies exceed the limit, alternative strategies such as utilizing external storage or implementing a layered architecture may be required.

Testing and Debugging

Debugging and testing Lambda functions can be challenging, especially in complex application scenarios. While AWS provides debugging tools and logging mechanisms, the absence of a traditional development environment can make certain debugging techniques more inconvenient.


It's essential to consider the discussed limitations while designing AWS Lambda-based applications to ensure they align with the requirements and constraints of the chosen use case. Understanding and mitigating these limitations can help optimize the performance, scalability, and reliability of real-life applications using AWS Lambda.