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
DigitalOcean
Score 8.6 out of 10
N/A
DigitalOcean is an infrastructure-as-a-service (IaaS) platform from the company of the same name headquartered in New York. It is known for its support of managed Kubernetes clusters and “droplets” feature.
$5
Starting Price Per Month
Google Compute Engine
Score 8.7 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
DigitalOcean
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
1GB-16GB
$5.00
Starting Price Per Month
8GB-160GB
$60.00
Starting Price Per Month
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
DigitalOcean
Google Compute Engine
Free Trial
No
No
Yes
Free/Freemium Version
No
No
Yes
Premium Consulting/Integration Services
No
No
No
Entry-level Setup Fee
No 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.
DigitalOcean is an easier and cheaper way to [set up] new machines. The UX is really good and it's easy to find what you need. Competition offer[s] a complicated way to manage machines and the cost is sometimes more than [...] double of what DigitalOcean offer[s]. However[,] …
Amazon has a very complex UI and many products to offer. They haven't polished up their UI and it has a much greater learning curve compared to DigitalOcean. However, Amazon Web Services (AWS) does have more comprehensive cloud computing services, which forces some companies to …
GCE was an easy choice for us after evaluating our options. We needed something that was dynamic enough to handle our specialized stack, but easy enough that our engineers weren't spending too much time configuring and launching. We found AWS's offering to be similar but …
We have tried using DigitalOcean Droplets for some of our minor and non critical VMs. In our experience, Google Compute Engine fares well in comparison the DigitalOcean Droplets as they provide better availability, better support and in general, a better experience.
The Google Cloud computing engine is fair at the top because it bills customers, automatic discounting for extended use, and how fast it can be turned on. We enjoy things around setting it up very easily via APIs and CLI commands, and with the always-on recommendations from …
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.
Pricing scale is good. Google Cloud Compute provides additional facilities free of cost (limited storage). Received one year free credits to get started. Nearest regions are available. Others amenities including free repository service available. UI is modern and fast to load. …
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 …
We ultimately chose Google Compute for the price difference as compared to other providers. Google's pricing for Windows servers is even lower than Microsoft's own cloud service, Azure. The terminology used across Google Compute is much easier to understand than the …
Lambda excels at event-driven, short-lived tasks, such as processing files or building simple APIs. However, it's less ideal for long-running, computationally intensive, or applications that rely on carrying the state between jobs. Cold starts and constant load can easily balloon the costs.
DigitalOcean is perfect for hosting client websites, running marketing tools, and managing media storage with Spaces and CDN. The use of Droplets to quickly launch landing pages or WordPress sites for campaigns is a Godsend. It’s great for fast, cheap, and scalable solutions. But for complex microservices or projects needing strict compliance (like HIPAA), DigitalOcean may not always be the best fit, but that depends heavily on your project.
You can use Google Cloud Compute Engine as an option to configure your Gitlab, GitHub, and Azure DevOps self-hosted runners. This allows full control and management of your runners rather than using the default runners, which you cannot manage. Additionally, they can be used as a workspace, which you can provide to the employees, where they can test their workloads or use them as a local host and then deploy to the actual production-grade instance.
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
Developing test cases for Lambda functions can be difficult. For functions that require some sort of input it can be tough to develop the proper payload and event for a test.
For the uninitiated, deploying functions with Infrastructure as Code tools can be a challenging undertaking.
Logging the output of a function feels disjointed from running the function in the console. A tighter integration with operational logging would be appreciated, perhaps being able to view function logs from the Lambda console instead of having to navigate over to CloudWatch.
Sometimes its difficult to determine the correct permissions needed for Lambda execution from other AWS services.
Some products/services available on other Cloud providers aren't available, but they seem to be catching up as they add new products like Managed SQL DBs.
While they have FreeBSD droplets (VMs), support for *BSD OSs is limited. I.e. the new monitoring agent only works on Linux.
There are no regions available on South America.
They don't seem to offer enterprise-level products, even basic ones as Windows Server, MS SQL Server, Oracle products, etc.
Its pretty good, easy and good performance. Also, interface is very good for starters compared to competitors. Infra as Code (IaC) using Terraform even added easiness for creation, management and deletion of compute Virtual Machines (VM). Overall, very good and very easy cloud based compute platform which simplified infrastructure, very much recommend.
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.
I honestly can't think of an easier way to set up and maintain your own server. Being able to set up a server in minutes and have fully control is awesome. The UX is incredibly intuitive for first-time users as well so there's no reason to be intimidated when it comes to giving DigitalOcean a shot.
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.
Google Compute Engine works well for cloud project with lesser geographical audience. It sometimes gives error while everything is set up perfectly. We also keep on check any updates available because that's one reason of site getting down. Google Compute Engine is ultimately a top solution to build an app and publish it online within a few minutes
It works great all the time except for occasional issues, but overall, I am very happy with the performance. It delivers on the promise it makes and as per the SLAs provided. Networking is great with a premium network, and AZs are also widespread across geographies. Overall, it is a great infra item to have, which you can scale as you want.
Amazon consistently provides comprehensive and easy-to-parse documentation of all AWS features and services. Most development team members find what they need with a quick internet search of the AWS documentation available online. If you need advanced support, though, you might need to engage an AWS engineer, and that could be an unexpected (or unwelcome) expense.
They have always been fast, and the process has been straight-forward. I haven't had to use it enough to be frustrated with it, to be honest, and when I have an issue they fix it. As with all support, I wish it felt more human, but they are doing aces.
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.
AWS Lambda is good for short running functions, and ideally in response to events within AWS. Google App Engine is a more robust environment which can have complex code running for long periods of time, and across more than one instance of hardware. Google App Engine allows for both front-end and back-end infrastructure, while AWS Lambda is only for small back-end functions
DigitalOcean is an inexpensive product as compared to other products available in the market. The UI is easy and the beginner can also understand the UI with the step by step guide. It provides a lot of custom features and the user needs to pay only for what they are using. Amazon has a complex UI and is on the expensive side. DigitalOcean is simple to use and is easily manageable and the servers can easily be set up without additional cost and such.
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.
Positive - Only paying for when code is run, unlike virtual machines where you pay always regardless of processing power usage.
Positive - Scalability and accommodating larger amounts of demand is much cheaper. Instead of scaling up virtual machines and increasing the prices you pay for that, you are just increasing the number of times your lambda function is run.
Negative - Debugging/troubleshooting, and developing for lambda functions take a bit more time to get used to, and migrating code from virtual machines and normal processes to Lambda functions can take a bit of time.
Positive - Elastic computer instances make it possible to pay for only for what you need.
Positive - Competitive pricing - some of the products that DigitalOcean offers are much cheaper than those offered by competitors.
Negative - Having to go to other cloud computing platforms for more specific, advanced services like Computer Vision optimized services, GPU cloud compute instances, etc...