Today, we’ll take a look at the latest AWS vs Azure vs Google Cloud market share comparison, including the Q4 2020 earnings the ‘big three’ cloud providers have reported. Let’s take a look at all three providers side-by-side to see where they stand.
Note: several previous versions of this article have been published. It has been updated for February 2021.
AWS vs. Azure vs. Google Cloud Earnings
To level-set this comparison, first know that – unsurprisingly – the cloud market as a whole is bigger than ever. Gartner has predicted worldwide public cloud spend to grow 18% in 2021, with 70% of organizations using cloud to increase cloud spending in the wake of COVID-19.
So within that market, let’s take a look at the AWS vs Azure vs Google Cloud market share breakdown and what each cloud provider’s reports shared.
First, the big news, of course: Jeff Bezos is trading his CEO role for Executive Chair of the Amazon Board, while current CEO of AWS Andy Jassy will step up to the Amazon CEO role. No AWS CEO has yet been announced, but many bets are on Matt Garman, currently the Vice President of AWS Sales and Marketing, or else Peter DeSantis, AWS’s Vice President of Global Infrastructure.
Next, the bigger news: Amazon revenue.
Amazon reportedAmazon Web Services (AWS) revenue of $12.7 billion for Q4 2020, compared to $9.95 billion for Q4 2019. AWS revenue grew 28% in the quarter.
Amazon as a whole had their first quarter over the $100 billion mark, at $125.56 billion. That’s an increase of 44% year-over-year, and beating predictions of $119.7 billion. Earnings per share were $14.09, compared to a $7.23 forecast.
Amazon as a whole benefitted from an astronomical online holiday shopping season due to COVID-19, and also from Prime Day being held in the fourth quarter. And AWS? It made up 10% of Amazon’s sales for the quarter – and 52% of its operating income.AWS only continues to grow, and bolster the retail giant time after time.
One thing to keep in mind: you’ll see a couple of headlines pointing out that revenue growth is down and/or highlighting the fact that it’s flattening out, quoting that 28% number and comparing it to previous quarters’ growth rates, which peaked at 81% in 2015. However, that metric is of questionable value as AWS continues to increase revenue at this enormous scale (see Geekwire graph), and dominate the market (as we’ll see below). AWS added more revenue quarter-over-quarter and year-over-year than any quarter in its history. Dave Fildes, Director of Investor Relations, mentioned on the call that “If you account for this COVID anomaly this year of [AWS re:Invent] being virtual and free, AWS year-over-year revenue growth, if you look at it, actually accelerated adjusting for that from the third quarter to the fourth quarter,” an interesting tidbit both from the perspective of gaining a glimpse into what re:Invent actually does for the company, and that AWS revenue is accelerating.
While Amazon specifies AWS revenue, Microsoft only reports on Azure’s growth rate. That number is 50% revenue growth over the previous quarter. This time last year, growth was reported at 62%. As mentioned above, comparing growth rates to growth rates is interesting, but not necessarily as useful a metric as actual revenue numbers – which we don’t have for Azure alone.
Here are the revenue numbers Microsoft does report. Azure is under the “Intelligent Cloud” business, which grew 23% to $14.6 billion. The operating group also includes server products and cloud services (26% growth).
The lack of specificity around Azure frustrates many pundits as it simply can’t be compared directly to AWS, and inevitably raises eyebrows about how Azure is really doing. Of course, it also assumes that IaaS is the only piece of “cloud” that’s important, but then, that’s how AWS has grown to dominate the market.
Nonetheless, Microsoft’s cloud business is clearly generating success for the company. Intelligent Cloud delivered the highest operating income of all segments this quarter at $6.4 billion, which is 36% of total consolidated operating income.
In more exciting news for public cloud followers, Alphabet has broken out Google Cloud revenue for the first time. Thus we learned that while Google Cloud revenue has increased over the last three years, so too have their operating losses. CFO Ruth Porat notes that these operating losses “reflect that we have meaningfully built out our organization, ahead of revenue.”
This quarter, Google Cloud reported revenue of $3.83 billion, an increase of 47% year-over-year. Operating losses were $1.24 billion compared to losses of $1.19 billion one year previously. For the full fiscal year 2020, Google Cloud’s revenue was $13 billion, with $5.6 billion operating losses.
Note that the Google Cloud unit includes not only Google Cloud Platform but also Google Workspace (formerly G Suite).
One highlight was that deals over $250 million tripled during 2020, and several billion-dollar deals were closed during the year.
We’ll add Alibaba Cloud to this list for the first time as the cloud computing division is profitable as of this quarter. The cloud computing arm of the Chinese retail giant earned $2.47 billion this quarter, an increase of 50% year-over-year.
Cloud Computing Market Share Breakdown – AWS vs. Azure vs. Google Cloud
When we originally published this blog in 2018, we included a market share breakdown from analyst Canalys, which reported AWS in the lead owning about a third of the market, Microsoft in second with about 15 percent, and Google sitting around 5 percent.
In 2019, they reported an overall growth in the cloud infrastructure market of 42%. By provider, AWS had the biggest sales gain with a $2.3 billion YOY increase, but Canalys reported Azure and Google Cloud with bigger percentage increases.
As of February 2021, Canalys reports that the worldwide cloud market grew 32% this quarter to $39.9 billion. For the full year of 2020, cloud infrastructure spending grew 33% to $142 billion. AWS has 31% of the market, followed by Azure at 20%, Google at 7%, Alibaba Cloud close behind.
Bezos has said, “AWS had the unusual advantage of a seven-year head start before facing like-minded competition. As a result, the AWS services are by far the most evolved and most functionality-rich.”
Our anecdotal experience talking to cloud customers often finds that true. It seems clear that in the case of AWS vs Azure vs Google Cloud market share – AWS still has a substantial lead, and their market share remains steady. With that said, all players are pushing growth and innovation and driving public cloud adoption across the board.
Google Cloud Platform offers a range of machine types optimized to meet various needs. Machine types provide virtual hardware resources available to a virtual machine that vary by virtual CPU (vCPU), disk capability, and memory size, giving you a breadth of options. Within every machine family there’s a set of machine types that offer a combination of memory and processor configuration. With so much to choose from, finding the right Google Cloud machine type for your workload can get complicated.
Since we’ve gone over EC2 instance types and Azure VMs, we’re doing an overview of each GCP machine type. The image below shows the basics of what we will cover, but remember that you’ll want to investigate further to find the right machine type for your particular needs.
A version of this article was published in 2018. It has been completely revised and updated for 2021.
General-Purpose Machine Type Family
General-purpose machine types are resources managed by Google Compute Engine. Each machine type in the general-purpose machine type family is curated for specific workload types. You’ll find that these machine types are suitable for a variety of common workloads. Some examples of these workloads include: development and testing environments, databases, mobile gaming and web applications. These machines are known for offering the best balance of performance and price. Within the General-purpose family, you can choose from four general-purpose machine types: E2, N2, N2D, and N1.
E2 Machine Types
E2 VMs provide a variety of compute resources for the lowest on-demand pricing across all general-purpose machine types. E2 machine types also utilize dynamic resource management, which offers numerous benefits for workloads that prioritize cost savings. These machine types offer the lowest cost of ownership on Google Cloud – you’ll see up to 31% of savings compared to N1. Additionally, pricing for E2 VMs already includes sustained use discounts and are also eligible for committed use discounts – which increases potential savings of up to 55%.
Best fit: Workloads such as small-to-medium databases, web serving, and application development and testing environments that don’t require large instance sizes, GPUs or local SSD would all be good fits for E2.
N2 Machine Types
N2 machine types are the second generation general-purpose machine types that support up to 80 vCPUs and 640 GB of memory. N2 VMs offer you the ability to get about 30% higher performance from your VMs, and shorten many of your computing processes. These machine types offer higher memory-to-core ratios for VMs created with the extended memory feature.
Best fit: General purpose workloads including web and application servers, enterprise applications, gaming servers, content and collaboration systems, and most databases.
N2D Machine Types
N2D machine types are the largest general-purpose machine type with up to 224 vCPUs and 896 GB of memory. These VMs are designed to provide you with the same features as N2 VMs.
Best fit: Web applications, databases, workloads, and video streaming.
N1 Machine Types
N1 VMs are first-generation general-purpose machine types that support up to 96 vCPUs and 624GB of memory. Though most would recommend using one of the second-generation general-purpose machine types, N1 VMs do offer a larger sustained use discount than N2 machine types. Additionally, they have support for Tensor Processing Units (TPUs) in select areas.
Compute-Optimized Machine Type Family
Compute-optimized machine types are ideal for compute-intensive workloads. These machine types offer the highest performance per core and most consistent performance on Compute Engine. Compute-optimized machine types are suitable for workloads such as game servers, latency-sensitive API serving and high-performance computing (HPC). These machines have 40% greater performance than previous generation N1.
C2 Machine Types
C2 machine types provide full transparency into the architecture of the underlying server platforms which will allow you to fine-tune the performance. C2 machine types run on a newer platform, offer more computing power, and are typically more powerful for compute-intensive workloads compared to the N1 high-CPU machines.
C2 VMs also offer up to 20% sustained use discounts. Additionally, they are eligible for committed use discounts, which would bring potential savings up to 60%.
Memory-Optimized Machine Type Family
Memory-optimized machine types are fit for tasks that require intensive use of memory with higher memory-to-vCPU ratios due to the fact that they offer the highest memory configurations across our VM families with up to 12 TB for a single instance. It is important to note that these machine types do not support GPUs. Memory-optimized machine types offer up to 30% sustained use discounts. Additionally, they are eligible for committed use discounts, bringing additional savings up to greater than 60%.
These machine types are best fit for in-memory databases and in-memory analytics.
M2 Machine Types
M2 VMs support the most demanding and business critical database applications with up to 12TB of memory. These machine types offer the lowest cost per GB of memory on Compute Engine, which makes them a perfect choice for workloads that utilize higher memory configurations and have low compute resources requirements.
M1 Machine Types
M1 machine types are the first generation memory-optimized machine types that offer 4TB of memory. Similar to M2 machine types, M1 machine types offer the lowest cost per GB of memory on Compute Engine.
Accelerator-optimized Machine Type Family
Accelerator-optimized VMs were added in July 2020. This machine type family is optimized for demanding compute workloads – this would be workloads such as high-performance computing and CUDA-enabled machine learning.
A2 Machine Types
Every A2 VM has a set amount of A100 GPUs that offer 20x improvements in computing speed in comparison to previous generation NVIDIA V100 GPUs. These machine types are currently available through Google’s alpha program.
Shared-Core Machine Types
Share-core machine types are a cost-effective option that works well with small or batch workloads that only need to run for a short time. They use partial vCPUs that run on one hyper-thread of the host CPU running your instance. These machine types use context-switching to share a physical core between vCPUs so they can multitask.
The GCP shared-core family provides bursts of physical CPU for brief periods of time in moments of need. They’re like spikes in compute power that can only happen in the event that your workload requires more CPU than you had allocated. These bursts are only possible periodically and are not permanent.
Custom Machine Types
Predefined machine types vary to meet needs based on high memory, high vCPU, a balance of both, or both high memory and high vCPU. If none of the machine types meet your needs, Google has one more option for you – custom machine types. With custom machine types, you can define exactly how many vCPUs you need and what amount of system memory for the instance. They allow you to independently configure CPU and memory to find the right balance for your applications, so you’re only paying for what you need. They’re a great fit if your workloads don’t quite match up with any of the available predefined types, or if you need more compute power or more memory, but don’t want to get bogged down by upgrades you don’t need that come with predefined types.
About GPUs and machine types
On top of your GCP instance types, Google also offers graphics processing units (GPUs) that can be used to boost workloads for processes like machine learning and data processing. You can only attach GPUs to predefined or custom machine types. In general, the higher number of GPUs attached to your instances, the higher number of vCPUs and system memory available to you.
What Google Cloud Machine Type should you use?
Between the predefined options and the ability to create custom Google Cloud machine types, Google offers enough variety for almost any application. Cost matters, but with the resource-based pricing structure, the actual machine you chose matters less when it comes to pricing.
With good insight into your workload and usage trends you have the resources available to find the machine type that fits your business needs.
If you’re looking to break into the cloud computing space, or just continue growing your skills and knowledge, there are an abundance of resources out there to help you get started, including free Google Cloud training. If you know where to look, open-source learning is a great way to get familiar with different cloud service providers.
With the combined knowledge from our previous blog posts on free training resources for AWS and Azure, you’ll be well on your way to expanding your cloud expertise and finding your own niche. No matter where you are in the learning process, there are training resources for every experience level and learning type – get started learning now with these 7 free Google Cloud Platform training resources:
1. Google Cloud Free Program
For free, hands-on training there’s no better place to start than with Google Cloud Platform itself. Within the Google Cloud free program you’ll have two options – sign up for a free trial or free tier. As a new Google Cloud customer, you can get started with a 90-day free trial. If you’re already a Google Cloud customer and are looking for a free option, you can sign up for Google Cloud’s free tier. GCP’s free program option is a no-brainer thanks to its offerings.
Access to all GCP products. You’ll have everything you need to experiment with building and running apps, sites, and services. Firebase and the Google Maps API are included with your free trial.
$300 credit is yours to spend for the next 90-days, an expansion from their previous 60-day period and a sizable offer in comparison to Azure’s $200 for 30 days, so take advantage.
No autocharges after the trial period ends – a rarity for most free trials, and a guarantee that this training resource is 100% free.
An always-free option. GCP’s free tier takes the cake with this an always-free tier that gives you enough power to run a small app despite limitations on product and usage. Free tier customers can use select Google Cloud products free of charge, with specified monthly usage limits, making this a perfect option for learning purposes.
For help with navigating the platform as you use it, check out GCP’s documentation for a full overview, comparisons, tutorials, and more.
On the Google Cloud training page, you’ll find plenty of classes tailored to your interests or role so you can get technical skills and learn best practices for using the platform. As another free Google Cloud training option, Google has also teamed up with Coursera, an online learning platform founded by Stanford professors, to offer courses online so you can “skill up from anywhere.”
Coursera includes a number of free courses including topics in Machine Learning, Architecting, Data Engineering, Developing Applications, and the list goes on.
In conjunction with Coursera, Google Cloud offers hands-on training with specialized labs available via Qwiklabs, a learning lab environment for developers. Choose a “quest” from their catalog and get started with 50+ hands-on labs from beginner to expert level. Here you’ll learn new skills in a GCP environment and earn cloud badges along the way. Get started with GCP Essentials and work your way into more advanced, niche topics like Managing Cloud Infrastructure with Terraform, Machine Learning APIs, IoT in Google Cloud, and so on.
4. Plural Sight
Pluralsight is a technology skills platform that offers a full breadth of Google Cloud courses, learning paths, and skills assessments. You’ll find several Google Cloud resources to help level up your skills. If you’re looking to dive deeper into Google Cloud, this is a great option – get started learning with a free trial and make sure to keep an eye out for training discounts offered by Google.
GitHub provides users a number of materials that can help further your Google Cloud training. The great thing about this platform is collaboration among the users, this community brings together people from all different backgrounds so they are able to provide knowledge about their own specialties and experiences. Here’s a great list of Google Cloud training resources that can help you.
You can never go wrong with YouTube. With an endless amount of free videos, YouTube offers an abundance of Google Cloud training options for those of you who prefer to watch the movie instead of reading the book (you know who you are). Some of the most popular YouTube channels for free Google Cloud Platform training include:
Google Cloud Platform (640k subscribers) – “helping you build what’s next with secure infrastructure, developer tools, APIs, data analytics and machine learning.”
Edureka (2.29M subscribers) is a full-service, online learning platform with curated content in Big Data and Hadoop, DevOps, Blockchain, AI, Data Science, AWS, Google Cloud, and more. Their YouTube channel is a “gateway to high-quality videos, webinars, sample classes and lectures from industry practitioners and influencers.” If you’re jumping into GCP with no prior knowledge or experience, the What is Google Cloud Platform tutorial will help get you started.
7. Blogs & Forums
Blogs are a great way to keep your mind flowing with new insights, ideas, and the latest on all things cloud computing. Google Cloud and Qwiklabs have blogs of their own, perfect for supplemented reading with their trainings. For a more well-rounded blog with content on other service providers, check out Cloud Academy.
Take Advantage of These Free Google Cloud Training Resources
It is clear that cloud computing is here to stay and as cloud technology continues to grow and advance, free training resources only continue to emerge so it’s important to stay up to date on new resources. We picked the 7 above for their reliability, variety, quality, and range of information. With the current working remote culture, this is the perfect time to take advantage of free google cloud training online. Whether you’re new to Google Cloud or consider yourself an expert, these resources will expand your knowledge and keep you up to date with what’s latest in the platform.
The deliverability of cloud governance models has improved as public cloud usage continues to grow and mature. These models allow large enterprises to tier and scale their AWS Accounts, Azure Subscriptions and Google Projects across hundreds and thousands of cloud users and services. When we first started talking to customers 5+ years ago, mostly AWS users at the time, they often had a single AWS account for their entire organization and required third-party tools to manage usage and costs by project, line of business or application owner. But now, the “Big 3” cloud providers offer an array of ways for even the largest Fortune 500 enterprises to set up, run and manage their use of the dizzying volume of cloud services.
Why Cloud Governance Models are Important
The main way cloud providers allow cloud administrators to manage and grant access to their services is by leveraging Identity and Access Management (IAM) and providing options for roles and policies that govern both access and usage. IAM lets you grant granular access to specific AWS, Azure and/or Google Cloud resources and helps prevent access to other resources. IAM lets you adopt the security principle of least privilege, where you grant only necessary permissions to access specific resources like VM’s, Databases, Storage, Containers, etc.. With IAM, you manage access control by defining who (identity) has what access (role) for which resource.
In ParkMyCloud, we apply this with Teams and Roles. Admins can create Teams (equivalent to Projects, Applications, or Lines of Business) and can invite a Team Lead to manage that PMC Team, and they can in turn grant users access and set permissions for them, which can then by automated based on policies, usually by leveraging tags but you can use other metadata as well.
What if you want more flexibility with the cloud providers to both manage user access and to more tightly align your cloud services and usage to your organizational structure, projects and applications? Each of the major providers has designed ways for large enterprises to implement a hierarchical usage of cloud users and services that probably can look very similar to that enterprises organization chart. (If you can understand their jargon.)
How AWS, Azure, and Google Apply Cloud Governance Models
We dug into AWS, Azure and Google and this is what we found:
Amazon Web Services (AWS)
Tier 1: AWS Organization
Tier 2: Organization Unit
Tier 3: AWS Accounts
Tier 4: Tags
Tier 1: Azure Enterprise Portal
Tier 2: Departments
Tier 3: Accounts
Tier 4: Subscriptions
Tier 5: Resource Groups
Tier 6: Tags
Tier 1: Organization
Tier 2: Folders
Tier 3: Projects
Tier 4: Resources
Tier 6: Tags
Tips for implementing Cloud Governance Models:
Research and attend web sessions on these cloud governance models to ensure you understand the nuance
Implement your cloud provider’s latest hierarchies and governance models prior to mainstream cloud adoption in your organization
Make sure you run the hierarchies you plan to implement by CloudOps, ITOps, DevOps and FinOps to ensure proper organizational mapping and reporting
The cloud providers have done a pretty good job of documenting their roles, policies and hierarchies and creating a graphical representation of their current hierarchical structures cloud governance models. Of course, none of them use the same terminology – I mean, why would you, too easy, right? (And why does Google rank a ‘Folder’ above a ‘Project’? )
With these options available to you, your cloud operations team can make sure to use this to your advantage when planning new resources, accounts, and use cases within your organization. Let us know your thoughts and if you use any of these models to improve your cloud usage.
During its virtual Google Cloud Next ’20 “On Air” series, Google announced the introduction of BigQuery Omni. This is an extension of its existing BigQuery data analytics solution to now analyze data in multiple public clouds, currently including Google Cloud and Amazon Web Services (AWS), with Microsoft Azure coming soon. Powered by Google Cloud’s Anthos, and using a unified interface, BigQuery Omni allows developers to analyze data locally without having to move data sets between the platforms.
BigQuery Engine to Analyze Multi-Cloud Data
Google Cloud’s general manager and VP of engineering, Debanjan Saha, says “BigQuery Omni is an extension of Google Cloud’s continued innovation and commitment to multi-cloud that brings the best analytics and data warehouse technology, no matter where the data is stored.” And that, “BigQuery Omni represents a new way of analyzing data stored in multiple public clouds, which is made possible by BigQuery’s separation of compute and storage.”
According to Google Cloud, this provides scalable storage that can reside in Google Cloud or other public clouds, and stateless, resilient compute that executes standard SQL queries.
Google Cloud reports that BigQuery Omni will:
Break down silos and gain insights on data with a flexible, multi-cloud analytics solution that doesn’t require moving or copying data from other public clouds into Google Cloud for analysis.
Get consistent data experience across clouds and datasets with a unified analytics experience across datasets, in Google Cloud, AWS, and Azure (coming soon) using standard SQL and BigQuery’s familiar interface. BigQuery Omni supports Avro, CSV, JSON, ORC, and Parquet.
Securely run analytics to another public cloud with a fully managed infrastructure, powered by Anthos, so you can query data without worrying about the underlying infrastructure. Users can choose the public cloud region where their data is located, and run the query.
Why is Google Aiming Multi-Cloud?
Many organizations leveraging public cloud are doing so with multiple clouds: 55% of organizations are multi-cloud according to a recent survey from IDG, and 80% according to a recent Gartner survey. (Is this actually necessary? Maybe.)
Google Cloud has been the most open to supporting this multi-cloud reality, and perhaps implicit in releases like Anthos and BigQuery Omni is Google’s recognition that it’s #3 in the market, and many of its customers have a presence in AWS or Azure.
So, BigQuery Omni actually involves physically running BigQuery clusters in the cloud on which the remote data resides. This is something that in the past, could only be done if your data was stored only in Google Cloud. Now with Kubernetes-powered Anthos, as well as the visualization tool gained in Google’s acquisition of Looker, Google is moving toward a middleware strategy. Now, it is offering services to bridge data silos, as a strategy to gain market share from its bigger competitors. Expect to see more similar service offerings coming from Google as they look to break AWS’s lead on public cloud.
Whether you’re new to public cloud altogether or already use one provider and are interested in trying another, you may be interested in a comparison of the AWS vs Azure vs Google free tier. The big three cloud providers – AWS, Azure and Google Cloud – each have a free tier available that’s designed to give users the cloud experience without all the costs. They include free trial versions of numerous services so users can test out different products and learn how they work before they make a huge commitment. While they may only cover a small environment, it’s a good way to learn more about each cloud provider. For all of the cloud providers, the free trials are available to only new users.
AWS Free Tier Offerings
AWS free tier includes more than 60 products. There are two different types of free options that are available depending on the product used: always free and 12 months free. To help customers get started on AWS, the services that fall under the free 12-months are for new trial customers and give customers the ability to use the products for free (up to a specific level of usage) for one year from the date the account was created. Keep in mind that once the free 12 months are up, your services will start to be charged at the normal rate. Be prepared and review this checklist of things to do when you outgrow the AWS free tier.
Azure Free Tier Offerings
The Azure equivalent of a free tier is referred to as a free account. As a new user in Azure, you’re given a $200 credit that has to be used in the first 30 days after activating your account. When you’ve used up the credit or 30 days have expired, you’ll have to upgrade to a paid account if you wish to continue using certain products. Ensure that you have a plan to reduce Azure costs in place. If you don’t need the paid products, there’s also the always free option.
Some of the ways people choose to use their free account are to gain insights from their data, test and deploy enterprise apps, create custom mobile experiences and more.
Google Cloud Free Tier Offerings
The Google Cloud Free Tier is essentially an extended free trial that gives you access to free cloud resources so you can learn about Google Cloud services by trying them on your own.
The Google Cloud Free Tier has two parts – a 90 day free trial with a $300 credit to use with any Google Cloud services and always free, which provides limited access to many common Google Cloud resources, free of charge. Google Cloud gives you a little more time with your credit than Azure, you get the full 90 days of the free trial to use your credit. Unlike free trials from the other cloud providers, Google does not automatically charge you once the trial ends – this way you’re guaranteed that the free tier is actually 100% free. Keep in mind that your trial ends after 90 days or once you’ve exhausted the $300 credit. Any usage beyond the free monthly usage limits are covered by the $300 free credit – you must upgrade to a paid account to continue using Google Cloud.
Free Tier Limitations
It’s important to note that the always-free services vary widely between the cloud providers and there are usage limitations. Keep in mind the cloud providers’ motivations: they want you to get attached to the services so you start paying for them. So, be aware of the limits before you spin up any resources, and don’t be surprised by any charges.
In AWS, when your free tier expires or if your application use exceeds the free tier limits, you pay standard, pay-as-you-go service rates. Azure and Google both offer credits for new users that start a free trial, which are a handy way to set a spending limit. However, costs can get a little tricky if you aren’t paying attention. Once the credits have been used you’ll have to upgrade your account if you wish to continue using the products. Essentially, the credit that was acting as a spending limit is automatically removed so whatever you use beyond the free amounts, you will now have to pay for. In Google Cloud, there is a cap on the number of virtual CPUs you can use at once – and you can’t add GPUs or use Windows Server instances.
For 12 months after you upgrade your account, certain amounts of popular products are free. After 12 months, unless decommissioned, any products you may be using will continue to run, and you’ll be billed at the standard pay-as-you-go rates.
Another limitation is that commercial software and operating system licenses typically aren’t available under the free tiers.
These offerings are “use it or lose it” – if you don’t use all your credits or utilize all your usage, there will be no rollover into future months.
Popular Services, Products, and Tools to Check Out for Free
AWS has 33 products that fall under the one-year free tier – here are some of the most popular:
Amazon EC2 Compute: 750 hours per month of compute time, per month of Linux, RHEL, SLES t2.micro or t3.micro instance and Windows t2.micro or t3.micro instance dependent on region.
Amazon S3 Storage: 5GB of standard storage
Amazon RDS Database: 750 hours per month of db.t2.micro database usage using MySQL, PostgreSQL, MariaDB, Oracle BYOL, or SQL Server, 20 GB of General Purpose (SSD) database storage and 20 GB of storage for database backups and DB Snapshots.
For the always-free option, you’ll find a number of products as well, some of these include:
AWS Lambda: 1 million free compute requests per month and up to 3.2 million seconds of compute time per month.
Amazon DynamoDB: 25 GB of database storage per month, enough to handle up to 200M requests per month.
Amazon CloudWatch: 10 custom metrics and alarms per month, 1,000,000 API requests, 5GB of Log Data Ingestion and Log Data Archive and 3 Dashboards with up to 50 metrics.
Azure has 19 products that are free each month for 12 months – here are some of the most popular:
Linux and Windows virtual machines: 750 hours (using B1S VM) of compute time
Managed Disk Storage: 64 GB x 2 (P6 SSD)
Blob Storage: 5GB (LRS hot block)
File Storage: 5GB (LRS File Storage)
SQL databases: 250 GB
For their always free offerings, you’ll find even more popular products – here are a few:
Azure Kubernetes Service: no charge for cluster management, you only pay for the virtual machines and the associated storage and networking resources consumed.
Azure DevOps: 5 users for open source projects and small projects (with unlimited private Git repos). For larger teams, the cost ranges from $6-$90 per month.
Azure Cosmos DB (400 RU/s provisioned throughput)
Unlike AWS and Azure, Google Cloud does not have a 12 months free offerings. However, Google Cloud does still have a free tier with a wide range of always free services – some of the most popular ones include:
Google BigQuery: 1 TB of queries and 10 GB of storage per month.
Kubernetes Engine: One zonal cluster per month
Google Compute Engine: 1 f1-micro instance per month only in U.S. regions. 30 GB-months HDD, 5 GB-months snapshot in certain regions and 1 GB of outbound network data from North America to all region destinations per month.
Google Cloud Storage: 5 GB of regional storage per month, only in the US. 5,000 Class A, and 50,000 Class B operations, and 1 GB of outbound network data from North America to all region destinations per month.
Check out these blog posts on free credits for each cloud provider to see how you can start saving: