Google Cloud Functions vs AWS Lambda: A Comprehensive Comparison

Google Cloud Functions and AWS Lambda are two of the most popular serverless computing platforms. Google Cloud Functions is a serverless compute service that allows you to run small code snippets, called functions, in response to events.

These platforms are designed to help developers build scalable and efficient applications. Google Cloud Functions supports a wide range of programming languages, including Node.js, Python, and Go.

Both Google Cloud Functions and AWS Lambda offer event-driven architecture, which means they trigger code execution based on specific events. This architecture allows for more efficient resource utilization and cost savings.

Both Google Cloud Functions and AWS Lambda offer robust event-driven architectures, allowing developers to build scalable and efficient applications. This means you can create functions that respond to specific events, like changes to a database or a file upload.

Event-driven capabilities are a key feature of both platforms, enabling you to write code that's triggered by real-time events. This allows for more dynamic and interactive applications.

Google Cloud Functions and AWS Lambda both support a wide range of event sources, including HTTP requests, Cloud Storage, and Cloud Pub/Sub. This makes it easy to integrate your functions with other services and build complex workflows.

With event-driven capabilities, you can build applications that respond quickly to changing data or user input. This is especially useful for real-time analytics, gaming, and other applications that require fast and efficient processing.

Resource Configuration and Scaling is a crucial aspect to consider when choosing between Google Cloud Functions and AWS Lambda. Each provider offers unique features to manage and scale resources.

Google Cloud Functions automatically allocates CPU power proportional to configured memory. This means you don't have to worry about manually scaling resources as your workload increases.

AWS Lambda allows separate CPU and memory configuration, giving you more control over resource allocation. This can be particularly useful for workloads with specific requirements.

Both providers offer virtually unlimited and automatic scaling, but as mentioned earlier, there are other factors at play. This means you can focus on developing your application without worrying about resource constraints.

To give you a better idea of the concurrency limits for each provider, here's a comparison:

Note that AWS is the only provider to offer highly customized concurrency management options.

Google Cloud Functions and AWS Lambda are both popular serverless computing services, but their pricing structures differ in some key ways.

Both services use a pay-per-use model, but AWS Lambda charges in 1ms increments after the first 100ms, while Google Cloud Functions charges in 100ms increments.

The pricing for both services is based on the number of requests, memory allocated to the function, and execution duration. However, Google Cloud Functions prices CPU and memory separately, allowing for more granular resource allocation.

One of the main contributors to FaaS cost is the number of requests, typically billed for every million requests per month. For AWS Lambda, this costs $0.20 per million requests, while Google Cloud Functions charges $0.40 per million requests.

Compute time is another factor in FaaS cost, measured in GB-seconds (higher-memory execution will cost more than lower-memory execution of the same duration). AWS Lambda charges $0.0000166667 per GB-second, while Google Cloud Functions charges $0.00002400 per vCPU-second (CPU) and $0.00000250 per GB-second (memory).

Here's a comparison of the free tier quotas for each provider:

Both AWS Lambda and Google Cloud Functions offer a free tier quota, but Google Cloud Functions offers an extra 1 million free requests per month.

Google Cloud Functions offers a serverless runtime environment for building and connecting cloud services. You can write simple, single-tasking functions that are tied to events generated by infrastructure and cloud services.

Functions in Google Cloud Functions can be created in various programming languages, such as Node.js, Python, and Go. This provides flexibility and ease of use.

With Google Cloud Functions, your code is executed in a fully managed environment, which means you don't have to worry about managing servers, configuring software, or updating platforms. Resource sharing occurs automatically in response to events, allowing your function to scale from a few to millions of calls per day with no effort on your part.

Lambda can be used together with many other services on AWS, such as Serverless Application Model (SAM), to build serverless applications.

Serverless Application Model (SAM) is an open-source framework created to build serverless applications.

Serverless Application Repository is a managed repository created for serverless applications, providing a convenient way to store and manage your code.

Cloud9 is an integrated development environment (IDE) for writing, running, and debugging your code, making it easier to develop and deploy serverless applications.

In the Cloud Functions Hands-on Lab, you can earn basic tenets of serverless architecture and create functions from the GCP console.

Google's Cloud Functions is created for serverless code, allowing you to write and deploy functions without worrying about the underlying infrastructure.

Google's App Engine is a serverless application development platform, providing a scalable and secure way to build and deploy applications.

Cloud Run is a fully managed platform that automatically scales containers, making it easier to deploy and manage serverless applications.

AWS Lambda supports a wide range of languages, including Java, Go, PowerShell, Node.js, C#, Python, and Ruby.

A table below shows the currently supported FaaS runtimes for AWS, Azure, and GCP:

GCP Cloud Functions, in particular, supports a diverse set of languages, including Go and Visual Basic, which are notable exceptions to the general trend.

My goals for this test were simple: I wanted to understand which serverless function performs fastest and which one is more reliable. To measure performance, I looked at average image render time as an indicator of speed.

I also wanted to determine reliability by examining the standard deviation of image render time. This metric helped me understand how consistent each service was in its performance.

To assess reliability, I also looked at HTTP response rates. Ideally, I would expect a response 100% of the time, but in reality, the services sometimes failed to respond at all.

In terms of performance, Google Cloud Functions and AWS Lambda have some key differences. AWS Lambda is slightly faster and more stable than Google Cloud Functions, with an average response time of 117.16 ms compared to 176.80 ms.

Both services are designed to handle concurrent requests, but it's essential to understand how they scale workloads in response to increased demand. Not all functions are created equal, so different workloads might require different settings to optimize performance.

The stateless nature of functions deployed on a FaaS like Google Cloud Functions or AWS Lambda makes it difficult to build complex applications that require state management. To overcome this, cloud providers like AWS, Azure, and GCP offer orchestration services that integrate with functions as "steps" in a workflow.

Here's a brief rundown of the orchestration services offered by each provider:

Cold Start Mitigation is a crucial aspect of Function-as-a-Service (FaaS) performance. Both AWS Lambda and Google Cloud Functions offer strategies to mitigate cold starts.

Provisioned Concurrency is a technique that keeps a specified number of initialized instances ready to respond to invocations. This can greatly reduce cold start times.

Minimum Instances is another strategy that keeps a specified number of instances warm and ready to serve requests. This is especially useful for applications that require frequent invocations.

Allocating more CPU can also help reduce cold start times for compute-intensive functions. This is because more CPU power can handle the initial overhead of starting a function instance.

Here are some strategies to mitigate cold starts:

Network Performance and Stability is a crucial aspect to consider when choosing a cloud provider. AWS Lambda is slightly faster and more stable than Google Cloud Function, with an average response time of 117.16 ms compared to 176.80 ms.

The difference in response time can be significant, especially for applications that require fast rendering and return of data. Monitoring response time is essential to ensure that your application is performing optimally.

To give you a better idea, here's a comparison of the maximum timeouts offered by each cloud provider:

Remember, increasing the timeout is not always the solution, and it should be considered in conjunction with adjusting the memory.

Orchestration services are built to integrate with functions as "steps" in a workflow, where the output of one step can be passed as input to another step. This enables building complex workflows in a serverless approach.

Functions are stateless, meaning they're not aware of other functions or execution results. This makes FaaS scalable and easy to provision, but difficult for complex applications.

Orchestration services, like AWS Step Functions, Durable Azure Functions, and GCP Workflows, provide a way to manage state and build complex workflows. These services are often very scalable and have many features beyond the scope of this article.

The power of FaaS services lies in their event-driven nature, where certain "interesting events" can trigger an execution of the function. This event-driven approach is a key feature of FaaS.

Here's a list of orchestration services offered by cloud providers: