AWS Lambda is a serverless computing platform that lets users run code without provisioning or managing servers. With Lambda, users can run code for virtually any type of app or backend service—all with zero administration. It takes of requirements to run and scale code with high availability.
$NaN
Per 1 ms
Google Compute Engine
Score 8.3 out of 10
N/A
Google Compute Engine is an infrastructure-as-a-service (IaaS) product from Google Cloud. It provides virtual machines with carbon-neutral infrastructure which run on the same data centers that Google itself uses.
$0
per month GB
Pricing
AWS Lambda
Google Compute Engine
Editions & Modules
128 MB
$0.0000000021
Per 1 ms
1024 MB
$0.0000000167
Per 1 ms
10240 MB
$0.0000001667
Per 1 ms
Preemptible Price - Predefined Memory
0.000892 / GB
Hour
Three-year commitment price - Predefined Memory
$0.001907 / GB
Hour
One-year commitment price - Predefined Memory
$0.002669 / GB
Hour
On-demand price - Predefined Memory
$0.004237 / GB
Hour
Preemptible Price - Predefined vCPUs
0.006655 / vCPU
Hour
Three-year commitment price - Predefined vCPUS
$0.014225 / CPU
Hour
One-year commitment price - Predefined vCPUS
$0.019915 / vCPU
Hour
On-demand price - Predefined vCPUS
$0.031611 / vCPU
Hour
Offerings
Pricing Offerings
AWS Lambda
Google Compute Engine
Free Trial
No
Yes
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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Prices vary according to region (i.e US central, east, & west time zones). Google Compute Engine also offers a discounted rate for a 1 & 3 year commitment.
The best GCP products - GKE for containerization workload fit to the VM machines specified for different application type (monolithic). These services can be easily integrated with each other with additional benefits.
I've used Rackspace, AWS, and Digital Ocean to host virtual environments. In my opinion, GCE has a robust feature set on par with any other mainstream virtual hosting company. I would say AWS and Digital Ocean are comparable, and Rackspace would be slightly less robust than …
[AWS Lambda] is very well suited for the projects that doesn't have any infra but needs it where short running processes are required. But if your application need to run continuously than this might not be the very apt tool for you.
Google Compute Engine is so easy to implement and run. It doesn't require much knowledge to build an app since they provide multiple options to choose from with their prebuilt sample list. We can easily make customization on any website app we built for our client according to their needs and make changes if required.
Lambda provides multiple methods for triggering functions, this includes AWS resources and services and external triggers like APIs and CLI calls.
The compute provided my Lambda is largely hands off for operations teams. Once the function is deployed, the management overhead is minimal since there are no servers to maintain.
Lambda's pricing can be very cost effective given that users are only charged for the time the function runs and associated costs like network or storage if those are used. A function that executes quickly and is not called often can cost next to nothing.
Scaling - whether it's traffic spikes or just steady growth, Google Compute Engine's auto-scaling makes sure we've got the compute power we need without any manual juggling acts
Load balancing - Keeping things smooth with that load balancing across multiple VMs, so our users don't have to deal with slow load times or downtime even when things get crazy busy
Customizability - Mix and match configs for CPU, RAM, storage and whatnot to suit our specific app needs
Putting a significant portion of your codebase into AWS Lambda and taking advantage of the high level of integration with other AWS services comes with the risk of vendor lock-in.
While the AWS Lambda environment is "not your problem," it's also not at your disposal to extend or modify, nor does it preserve state between function executions.
AWS Lambda functions are subject to strict time limitations, and will be aborted if they exceed five minutes of execution time. This can be a problem for some longer-running tasks that are otherwise well-suited to serverless delivery.
I give it a seven is usability because it's AWS. Their UI's are always clunkier than the competition and their documentation is rather cumbersome. There's SO MUCH to dig through and it's a gamble if you actually end up finding the corresponding info if it will actually help. Like I said before, going to google with a specific problem is likely a better route because AWS is quite ubiquitous and chances are you're not the first to encounter the problem. That being said, using SAM (Serverless application model) and it's SAM Local environment makes running local instances of your Lambdas in dev environments painless and quite fun. Using Nodejs + Lambda + SAM Local + VS Code debugger = AWESOME.
Having interacted with several cloud services, GCE stands out to me as more usable than most. The naming and locating of features is a little more intuitive than most I've interacted with, and hinting is also quite helpful. Getting staff up to speed has proven to be overall less painful than others.
GCE is highly available system. So far we have not experienced any planned or unplanned outages. We do experience application errors however they are not GCE reated. GCE provides us with wide ranges of option to make systems highly available like block storage, global load balancing, GPU accelerators and Machine Managers which manages different VMs.
It's good in terms of Performance, as compared to other VMs or Compute Engine Services by other providers. Google Compute Engine integrates seamlessly with other Google Cloud Platform services, allowing users to leverage a wide range of tools and services without significant performance degradation.
I have not needed support for AWS Lambda, since it is already using Python, which has resources all over the internet. AWS blog posts have information about how to install some libraries, which is necessary for some more complex operations, but this is available online and didn't require specific customer support for.
The documentation needs to be better for intermediate users - There are first steps that one can easily follow, but after that, the documentation is often spotty or not in a form where one can follow the steps and accomplish the task. Also, the documentation and the product often go out of sync, where the commands from the documentation do not work with the current version of the product.
Google support was great and their presence on site was very helpful in dealing with various issues.
Azure Functions is another product that provides lambda functionality, but the documentation for some of Azure's products is quite hard to read. Additionally, AWS Lambda was one of the first cloud computing products on a large cloud service that implemented lambda functions, so they have had the most time to develop the product, increase the quality of service, and extend functionality to more languages. Amazon, by far, has the best service for Lambda that I know.
Google Compute Engine provides a one stop solution for all the complex features and the UI is better than Amazon's EC2 and Azure Machine Learning for ease of usability. It's always good to have an eco-system of products from Google as it's one of the most used search engine and IoT services provider, which helps with ease of integration and updates in the future.
I was able to perform a lot of processing on data delivered from my website and little or no cost. This was a big plus to me.
Programming AWS Lambda is quite easy once you understand the time limits to the functions.
AWS Lambda has really good integration with the AWS S3 storage system. This a very good method of delivering data to be processed and a good place to pick it up after processing.
