Serverless vs. VMs: When FaaS Saves Money

In the world of cloud computing, choosing the right infrastructure is key. Many organizations are exploring serverless computing. This model offers unique benefits. One of the biggest is potential cost savings. But when does serverless, specifically Function-as-a-Service (FaaS), actually save money over traditional virtual machines (VMs)? This article dives deep. We will analyze the cost benefits. We will also explore the scenarios where FaaS shines.

Understanding Serverless and FaaS

First, let’s clarify what serverless computing is. Despite its name, it doesn’t mean there are no servers. Instead, it means developers don’t manage them. The cloud provider handles all the infrastructure. This includes servers and operating systems. Serverless architecture is an event-driven model. It shifts management responsibilities to the provider. This allows developers to focus solely on their application code. It also eliminates the need for developers to manage hardware or software infrastructure directly.

Function-as-a-Service (FaaS) is a core component of serverless. It allows you to build applications from small, independent functions. Each function runs in response to specific events. The FaaS provider automatically scales these functions. This happens based on traffic demands. Therefore, you only pay for the compute time your code actually uses. This is a major departure from VMs, where you pay for provisioned capacity.

How Serverless Functions Work

A serverless function is a piece of code. It’s typically stateless. It’s also ephemeral, meaning it’s used and then discarded. These functions often last only for seconds. They are designed to be triggered by specific conditions. Examples include an e-commerce order, a file upload, or a user signup. Any action that can be triggered by an API call can activate a serverless function. This event-driven nature is fundamental to its cost model.

Consider a typical web application. It has a front end and a backend. The backend handles data processing and database interactions. Serverless functions can power these backend operations. Developers write functions in languages like Python, Java, or Node.js. They then deploy these functions to a managed environment. The provider takes care of execution and scaling.

The Cost Advantage of Serverless

The primary cost benefit of serverless FaaS lies in its pay-per-execution model. You are billed based on the number of requests and the duration of execution. This is fundamentally different from VMs. With VMs, you pay for the instances whether they are actively running or sitting idle. For many applications, this means paying for unused capacity.

Let’s break down the cost components:

• Compute Time: Serverless platforms charge for the actual compute time consumed. This is typically measured in milliseconds.
• Number of Requests: Each invocation of a function incurs a small charge.
• Memory Allocation: The amount of memory allocated to a function also influences its cost.

In contrast, VMs have more fixed costs:

• Instance Type: You select a VM instance with specific CPU and RAM.
• Running Time: You are charged for the VM instance for every hour or minute it is running, regardless of utilization.
• Operating System Licensing: Additional costs may apply for certain OS licenses.

Therefore, for workloads with unpredictable traffic or infrequent execution, serverless can be significantly more cost-effective. Imagine a small utility function that runs only when a user uploads a document. Running this on a dedicated VM would mean paying for that VM 24/7, even if it’s only used for a few minutes a day. A serverless function, however, would only incur costs during those few minutes of execution.

When Serverless FaaS Saves Money Over VMs

Several scenarios highlight the cost advantages of FaaS over VMs. These often involve specific workload characteristics.

1. Event-Driven and Spiky Workloads

Applications that react to events or experience unpredictable traffic spikes are ideal for serverless. Think about a website that gets a surge of traffic during a marketing campaign. With VMs, you would need to over-provision instances to handle the peak load. This leads to wasted resources during off-peak times. Serverless functions, however, scale automatically. They can handle millions of requests without manual intervention. As a result, you only pay for the resources used during the surge. This makes it incredibly cost-efficient for such scenarios.

2. Microservices and APIs

Modern applications are often built using microservices. Each microservice can be deployed as a serverless function. This granular approach allows for independent scaling and cost optimization of individual services. For example, an API endpoint that handles user authentication might have very different traffic patterns than an endpoint for generating reports. Serverless allows you to manage these costs independently. Moreover, this aligns well with the concept of serverless vs VMs: when FaaS truly saves money.

3. Background Tasks and Scheduled Jobs

Many applications require background processing. This includes tasks like sending out email notifications, processing data, or running scheduled reports. These tasks often don’t need to be running all the time. Using a VM for these jobs means keeping it running constantly. Serverless functions can be triggered on a schedule. They execute the task and then shut down. Consequently, this significantly reduces costs compared to maintaining a dedicated VM. This is a prime example of optimizing IT hardware lifespan for cost savings, by not keeping hardware running unnecessarily.

4. IoT Data Processing

The Internet of Things (IoT) generates vast amounts of data. This data often arrives in streams and requires immediate processing. Serverless functions are well-suited for handling these event streams. They can ingest, transform, and analyze data in real-time. The ability to scale elastically to handle fluctuating data volumes makes it a cost-effective solution. You don’t need to provision for the absolute maximum possible data influx, which would be prohibitively expensive with VMs.

5. Low-Traffic or Infrequently Used Applications

For applications that have very low traffic or are used only occasionally, VMs can be a significant overhead. Even a small VM incurs a monthly cost. Serverless functions, with their pay-per-execution model, can be virtually free when not in use. This is especially true for internal tools or proof-of-concept projects. The cost of keeping a VM running for an application that’s used once a month is often much higher than the cost of a few serverless function invocations.

Challenges and Considerations

While serverless offers compelling cost benefits, it’s not a silver bullet. There are challenges to consider:

1. Cold Starts

When a serverless function hasn’t been invoked recently, it may experience a “cold start.” This is the time it takes for the provider to provision the environment and load the function. This latency can impact applications requiring near-instantaneous responses. While providers are improving this, it remains a factor for performance-sensitive use cases. Solutions exist to mitigate this, such as provisioned concurrency, but these can add to the cost.

2. Vendor Lock-in

Serverless platforms are often specific to cloud providers (AWS Lambda, Azure Functions, Google Cloud Functions). Migrating between providers can be complex. This can lead to vendor lock-in. Therefore, it’s important to consider this when choosing a provider.

3. Execution Limits

Serverless functions typically have limits on execution time and memory. Complex, long-running tasks might not be suitable for FaaS. For such scenarios, VMs or containers might be a better fit. Developers need to design functions to be small and single-purpose.

4. Observability

The ephemeral and distributed nature of serverless can make monitoring and debugging challenging. Tracing requests across multiple functions and understanding overall system behavior requires specialized tools. Solutions like Splunk Observability Cloud provide end-to-end visibility for serverless architectures. This helps in detecting issues like cold starts and error spikes.

Comparing Costs: Serverless vs. VMs in Practice

To illustrate the cost difference, let’s consider a hypothetical scenario. Suppose you have an API that receives 1 million requests per month. Each request takes 50ms to process and requires 128MB of memory.

Serverless (e.g., AWS Lambda):

• Requests: 1,000,000
• Execution Time: 1,000,000 requests * 0.05 seconds/request = 50,000 seconds
• Cost per million requests (approximate, depending on region and memory): ~$2.00
• Cost per GB-second (approximate): ~$0.0000166667
• Memory cost: 1,000,000 requests * 0.05 seconds * 0.128 GB * $0.0000166667/GB-sec = ~$0.11
• Total Estimated Cost: ~$2.11

Virtual Machine (e.g., a small AWS EC2 instance):

• Assume a small instance like t3.micro costs ~$0.0104 per hour.
• Running 24/7 for a month (approx. 730 hours): 730 hours * $0.0104/hour = ~$7.59
• This VM might not even be powerful enough to handle the load efficiently. You might need a larger instance. A t3.small costs ~$0.0208 per hour, leading to ~$15.18 per month.
• Total Estimated Cost: ~$7.59 – $15.18+ (and this is for idle capacity)

As you can see, for this specific workload, serverless is dramatically cheaper. The VM cost is fixed, while the serverless cost scales with usage. This aligns with the idea that serverless computing enables cost efficiency for event-driven workloads.

When VMs Might Still Be Better

Despite the advantages of serverless, VMs retain their value in certain situations:

• Consistent, High Utilization: If your application runs at a constant, high capacity 24/7, a reserved VM instance might be more cost-effective than paying per execution. This is because you can often get significant discounts for committing to long-term usage.
• Long-Running, Resource-Intensive Tasks: As mentioned, tasks exceeding serverless execution limits or requiring sustained high CPU/memory might necessitate VMs.
• Legacy Applications: Migrating legacy applications to a serverless architecture can be a significant undertaking. In such cases, continuing to use VMs might be the path of least resistance and cost.
• Predictable and Stable Workloads: For applications with highly predictable and stable traffic patterns, the operational overhead of managing VMs might be less than the complexity of managing numerous serverless functions.
• Specific Software Requirements: Some software or operating system configurations might be difficult or impossible to deploy in a serverless environment.

It’s also worth noting that cloud cost optimization is a continuous process. You can often achieve savings by right-sizing VMs or by using autoscaling groups. For those looking to optimize their cloud spend, understanding 7 steps to cut cloud costs is crucial.

Conclusion: A Strategic Choice for Cost Savings

Serverless computing, particularly FaaS, offers significant cost benefits over virtual machines for many use cases. Its pay-per-execution model, automatic scaling, and reduced operational overhead make it ideal for event-driven applications, microservices, and workloads with unpredictable traffic. By eliminating the need to pay for idle capacity, serverless can lead to substantial savings. However, it’s essential to consider potential challenges like cold starts and vendor lock-in. Ultimately, the choice between serverless and VMs depends on the specific workload, traffic patterns, and performance requirements. A thorough cost-benefit analysis is always recommended. By strategically adopting serverless where it makes sense, organizations can unlock new levels of cost efficiency and agility. This strategic adoption can significantly impact their overall cloud expenditure and operational effectiveness.

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