Breaking down AWS Lambda pricing – How much does a serverless app really cost?

Intro

Serverless computing is a convenient method for tackling the app development process. AWS Lambda, a standout in this space, offers a highly adaptable and budget-friendly solution. In 2024, AWS Lambda reached 1.5 million users and trillions of function invocations every month. 

However, as attractive as serverless architecture may seem, it is necessary to grasp the pricing structure of AWS Lambda in order to make wise decisions. Lambda pricing is dependent on usage, which offers scalability but can also introduce unforeseen costs if not planned properly. If your business operates within strict budgets, these cost fluctuations can impact long-term strategies. 

In this article, our team breaks down the key factors that influence AWS Lambda costs. We aim to explain how it affects scalability and budgeting for serverless apps and give you full knowledge of how to align Lambda’s pricing model with your project objectives to reach predictable expenses and sustainable growth.

How Amazon’s AWS Lambda pricing works

Invocation requests

AWS Lambda collects a predetermined amount for each operation request. On each occasion your application invokes a Lambda function, it is considered a single request. As an illustration, a fintech app might generate requests for each user login, account balance query, or transaction initiation. The cost is tied to the number of these requests, which makes it easy to forecast expenses when you understand the traffic patterns of your application.

Free tier

The initial million requests per month are provided for free.

Beyond free tier 

$0.20 for every million requests.

Compute time

Duration-based pricing calculates costs depending on how long your function runs. AWS tracks this in milliseconds. When your function initiates execution, the calculation starts and runs until the function completes. For instance, a data-processing function that takes longer to analyze large datasets will cost more than a quick operation. Businesses can monitor execution times to keep functions efficient and control costs.

Formula

Compute cost = [Memory allocated (GB)] × [Execution time (ms)] × [Price per GB-second].

Free tier

400,000 GB-seconds of computing time per month.

Beyond free tier 

$0.0000166667 per GB-second.

Source: AWS / Lambda costs as of January 2025

Factors affecting costs

• Memory allocation. The memory you designate for a Lambda function directly impacts its cost. Higher memory settings increase processing power but also raise costs. Balancing memory requirements with performance needs is needed for cost control.
• Number of requests. Applications with high traffic generate more function calls, which increases expenses. Analyzing user behavior and scaling strategically helps manage this aspect.
• Execution time. Functions that run longer will naturally result in higher charges. You can reduce execution time by refining code efficiency and limiting unnecessary operations.

Additional costs

Provisioned concurrency

If you enable provisioned concurrency, which keeps functions warm to reduce latency, you are charged separately.

Pricing

$0.0000041667 per GB-second for Provisioned Concurrency, in addition to compute costs.

Data transfer

Outbound data transfer is charged based on AWS’s standard data transfer rates.

Integrations

Services invoked by your Lambda (e.g., S3, DynamoDB) may have their own associated costs. Common options include tools for data storage, message queues, monitoring, and APIs. The choice of integration depends on the application’s requirements, such as processing data, managing real-time streams, or interacting with external services.

We recommend using the AWS Lambda pricing calculator​ to get a quote. 

Calculating the сost of running a serverless app

When estimating the cost of running a serverless application, such as a simple REST API using AWS Lambda and API Gateway, a structured approach helps you understand potential expenses clearly. Here’s how you can break it down.

1.

Estimate monthly requests

Start by analyzing the anticipated usage of your application. For example, if your API handles 100,000 requests per month, this number becomes the foundation for cost calculations.

2.

Define execution time and memory usage

The AWS Lambda fee is set according to the duration of a function’s execution and the memory assigned. For instance, a function with 128 MB of memory executing for 100 milliseconds per request will incur costs based on these parameters. AWS provides calculators to simplify these computations.

3.

Include costs for API gateway and additional services

API Gateway charges are based on the number of API calls and the type of API deployed (REST, HTTP, or WebSocket). If your application interacts with other AWS services like S3 for storing files or DynamoDB for database operations, factor in their costs as well.

4.

Use tools for monitoring expenses

Tools like AWS Cost Explorer or third-party platforms can assist in monitoring and managing your AWS Lambda expenses. 

1. AWS Cost Explorer. This tool offers detailed usage and cost data. It supports historical analysis, forecasts future costs, and identifies spending trends across services, regions, or tags. Custom reports and visualizations help in finding areas that may require attention
2. External platforms that provide additional insights and features. These are:
   – CloudHealth by VMware
   – Spot.io
   – CloudCheckr
   – Kubecost.

5.

Calculation example

Let us consider an application receiving 100,000 monthly requests. Employing a Lambda function configured with 128 MB of memory and a runtime of 100 milliseconds per invocation, the service costs would amount to a fraction of a cent per request. Adding API Gateway fees for 100,000 API calls and minimal S3 usage, the aggregate monthly cost might range between $5 and $20, depending on regional pricing and additional service usage.

How to optimize AWS Lambda costs  

AWS Lambda offers significant flexibility, but managing costs requires thoughtful planning. Here is how businesses can approach cost management while balancing performance and budget considerations.

Make the most of the free tier

AWS provides 1 million free requests and 400,000 GB-seconds of compute time monthly. Use this allowance strategically for development, testing, or running small-scale applications. However, keep in mind that high-traffic applications or resource-intensive workloads can quickly surpass these limits.

Plan for beyond the free tier

Applications with heavy traffic, long execution times, or frequent invocations may exceed the free tier. Monitor usage closely to understand when paid services kick in and forecast costs based on realistic scenarios to avoid surprises.

Adjust memory allocation for smaller tasks

As we have already learned, AWS Lambda costs depend on runtime and memory allocation. Assigning high memory to lightweight functions results in unnecessary expenses. Analyze the memory needs of each function and set allocations that match actual requirements.

Write efficient code to reduce execution time

Every millisecond counts in AWS Lambda billing. Inefficient code or poorly optimized logic increases execution time and costs. Regularly review and refine the codebase to make sure functions perform tasks as quickly as possible without sacrificing reliability.

Use asynchronous invocations for API Gateway savings

Asynchronous invocations are processed in the background, which eliminates the need for immediate responses. For applications that do not require instant feedback, this approach can significantly reduce API Gateway costs.

Apply provisioned concurrency for predictable workloads

Provisioned concurrency keeps a fixed number of function instances ready to handle requests. This reduces delays for tasks like daily batch processing. It also avoids cold starts, which happen when a function runs for the first time and causes lag. But keep in mind, while this improves response times, it also increases costs since pre-warmed instances use resources even during low demand.

AWS Lambda vs. others – Cost comparison

AWS Lambda vs. EC2

AWS Lambda and EC2 vary considerably in their pricing structures. Lambda employs a consumption-based pricing model, where fees rely on the volume of requests and processing time. Meanwhile, EC2 charges for server uptime, regardless of usage.

With EC2, you pay for the server instance hours, whether it is running or idle. Pricing varies based on such factors as instance type, region, and usage. Standard instances start at around $0.0116 per hour for the smallest size (t3.micro).

For businesses, EC2 is more compatible with established applications that demand high governance of the underlying infrastructure.

AWS Lambda vs. ECS/Fargate

Elastic Container Service (ECS) and Fargate offer a more flexible approach to compute, particularly for containerized applications. However, their pricing model also differs from Lambda’s pay-per-execution approach.

ECS imposes a cost for the resources such as CPU and memory utilized by your containers, billed by the second. Fargate, a serverless compute engine for containers, bills based on the vCPU and memory you assign. Pricing starts at approximately $0.04048 per vCPU hour and $0.004445 per GB-hour.

ECS/Fargate is ideal for companies that need to manage containers or microservices architectures. Lambda is a better choice for businesses that prefer a simpler, event-driven architecture without server management concerns or container orchestration.

Real-life case studies from T-Mobile and Edelman Financial Engines

T-Mobile adopted serverless computing to improve development efficiency and accelerate response times to customer needs. By using AWS Lambda within the Jazz platform, T-Mobile reduced infrastructure administration and operational overhead. Jazz automates numerous tasks, such as security, compliance, and testing, allowing development teams to concentrate on coding and delivering value. This approach has shortened deployment cycles, improved scalability, and allowed T-Mobile to implement a “serverless first” policy for new services. Lambda’s event-driven functions support T-Mobile’s critical applications, including its website and My Account app, driving faster innovation and adaptability.

Another successful case of AWS Lambda usage is that of Edelman Financial Engines, an American financial planning and investment advisory company. It has migrated its platform to AWS Lambda’s serverless architecture. This has allowed the company to:

• Reduce infrastructure costs
• Eliminate administrative tasks and downtime
• Scale efficiently and handle up to 60,000 requests per minute.

According to Alex Yavorskiy, CTO at Edelman Financial Engines, the company projects 94 percent savings in hard costs, or about $110,000 annually.

Conclusion

To wrap up, AWS Lambda is a cost-efficient strategy for serverless architecture. Nevertheless, businesses must thoughtfully oversee their usage to evade unpredictable costs. We advise closely monitoring request count, runtime duration, and memory distribution to be able to harmonize performance with budget requirements. A strategic approach to scaling and configuring resources makes it possible to align Lambda’s pricing with business goals and does not impact long-term financial planning.

For more insights on serverless app development, reach out to our experienced team at Touchlane. We specialize in working with AWS solutions, including Lambda, and can effortlessly integrate them into your project strategy.