How Rust Can Significantly Lower Cloud Costs on AWS, GCP, and Azure

As cloud computing continues to dominate the landscape of modern software deployment, developers and organizations are keenly focused on reducing cloud expenses while maintaining optimal performance. For many, the programming language chosen to build and deploy services can have a substantial impact on these costs. Rust, a systems programming language known for its speed, low memory usage, and safety, has emerged as an excellent choice for building cloud-native applications with a strong focus on reducing operating costs.

In this blog, we’ll explore how using Rust can lower cloud costs on popular platforms like AWS, GCP, and Azure, and compare it to other languages such as Node.js, Python, and PHP. We’ll dive into statistics, benchmarks, and cost-saving scenarios that highlight Rust’s advantages in cloud environments.

The Key to Cost Efficiency: Memory and Execution Speed

At the core of Rust’s cloud cost advantages are two primary factors:

1. Lower Memory Usage: Rust applications typically use significantly less memory than those written in interpreted languages like Python, Node.js, or PHP. This means that fewer resources are required to handle the same workloads.

2. Faster Execution: Rust is a compiled language that produces machine code, making it much faster than interpreted languages. This speed translates into higher throughput, meaning that a Rust-based service can handle more requests per second, reducing the need for scaling compute resources.

Memory Usage Comparison

Cloud platforms charge for the compute and memory resources your application consumes. For example, AWS Lambda charges based on the memory allocated to your function and the duration it runs. GCP’s Cloud Functions and Azure Functions operate similarly. Since Rust consumes less memory than interpreted languages, you can provision less memory for the same workload.

Here’s a comparison of typical memory usage across languages for a basic web server handling the same request load:

For cloud providers like AWS Lambda, where the cost is calculated as a function of memory and execution time, this lower memory footprint directly translates to lower bills.

Example: AWS Lambda Pricing

Consider an example using AWS Lambda with the following scenario:

• Handling 1 million requests per month.

• Allocated memory: 128 MB for Python, 64 MB for Node.js, and 32 MB for Rust.

• Average execution duration per request: 100 ms for Python, 70 ms for Node.js, and 50 ms for Rust.

The monthly cost for AWS Lambda, without including free tier benefits, is calculated based on the memory and execution duration. Here’s an approximate breakdown:

As we can see, Rust drastically reduces costs due to both lower memory usage and faster execution time.

Fast Execution = More Requests with Same Compute Power

Another crucial benefit of Rust is its speed. A Rust-based application can handle significantly more requests per second than one written in a high-level interpreted language. This means that the same server instance or compute resource can handle a higher workload, which reduces the need to scale horizontally.

Throughput Comparison (Requests per Second)

Let’s compare the requests per second (RPS) that each language can handle using a similar test on a basic API endpoint:

Rust’s performance is nearly 3x higher than Node.js and over 5x higher than Python. This higher throughput means that a single instance of a Rust application can handle more traffic than an instance of Node.js, Python, or PHP, reducing the number of servers or functions required.

Real-World Cloud Cost Scenarios

To illustrate Rust’s impact on cloud costs, let’s examine two real-world scenarios: serverless functions and containerized applications.

1. Serverless Functions (AWS Lambda, GCP Cloud Functions, Azure Functions)

In serverless environments, where pricing is based on the combination of memory allocation and execution time, Rust’s advantages in both speed and memory efficiency shine. Here’s how Rust can reduce costs:

• Smaller memory footprint: Rust functions can use significantly less memory, resulting in lower cost per invocation.

• Faster execution: Rust completes tasks faster, reducing the amount of time billed by cloud providers.

If your service is latency-sensitive and serves high volumes of requests, switching to Rust can provide substantial savings. For example, if you run an image processing pipeline in a Lambda function, a Rust-based solution can save thousands of dollars annually by using less memory and finishing tasks faster.

2. Containerized Applications (ECS, GKE, Azure AKS)

In a containerized microservices architecture, compute and memory resources are often the most significant contributors to costs. Rust’s performance allows you to either:

• Run fewer containers: Rust applications can handle more load per container, reducing the need for horizontal scaling.

• Use smaller containers: Because Rust uses less memory, you can allocate smaller memory footprints to each container, saving costs on memory provisioning.

For example, if you’re running a Kubernetes cluster on GCP (Google Kubernetes Engine) with an API service that handles 1,000 requests per second, switching from Python to Rust could reduce your instance count by 50%, resulting in a corresponding reduction in your GKE bill.

Additional Benefits: Cold Start Time

Rust also excels in scenarios involving cold starts, such as in serverless environments. Cold start time refers to the time it takes for a function to initialize after being idle. Since Rust binaries are small and efficient, their cold start times are faster compared to interpreted languages that require loading runtimes and additional dependencies.

Language

Faster cold starts mean fewer delays in serving users and fewer resources used during initialization, translating to further cost savings in serverless environments.

The Rust Ecosystem: Growing Cloud Support

The Rust ecosystem has matured to the point where it can be easily integrated with cloud services. Libraries like aws-sdk-rust, cloud-storage-rs, and others enable seamless integration with AWS, GCP, and Azure, reducing the need for complex configurations or workarounds.

Moreover, Rust’s excellent concurrency model with async/await allows developers to make the most out of cloud environments, maximizing resource usage and reducing idle times.

Conclusion: Rust as a Cost-Saving Solution for Cloud Deployments

Rust stands out as a powerful language for cloud applications by significantly reducing memory usage, increasing execution speed, and improving overall resource efficiency. Whether you are running serverless functions, microservices, or containerized applications, Rust’s benefits translate directly into lower cloud costs.

Compared to popular languages like Python, Node.js, and PHP, Rust offers:

• Lower memory requirements, resulting in smaller and cheaper deployments.

• Faster execution times, reducing compute time charges.

• Higher throughput, leading to fewer required resources.

• Reduced cold start times in serverless environments.

For companies looking to optimize their cloud spending without compromising performance, Rust is a strategic choice that can drive significant cost reductions over time.

By switching to Rust, not only can you improve your application’s performance, but you can also realize substantial savings on cloud costs, making it a perfect fit for scaling modern, cost-efficient cloud-native applications.