9 Key Benefits of Cloud Migration You Need To Know

Over the past ten years, cloud computing has gained popularity and changed the way businesses operate. For enterprises of all sizes, it provides a wealth of benefits. A number of advantages come with moving to the cloud, including the capacity to effectively grow your operations, reduce expenses, and improve security. This post will go into more depth about these benefits and provide instances of actual businesses that have successfully made the switch to the cloud. Let’s first address typical worries regarding cloud migration, such as cost, security, and stability. You’ll know exactly what cloud migration requires, how difficult it might be, and—most importantly—what amazing advantages it offers by the time you’re done.

What is Cloud Migration?

Your data, apps, and IT resources must be moved to a cloud-based computing environment as part of cloud migration. This change has astounding benefits for companies of all sizes, including improved data security, increased agility, cost effectiveness, and more.

The cloud offers significant opportunities for small enterprises to quickly expand operations or escape the hassle of overseeing an own IT team. Larger businesses, on the other hand, may use the cloud to reduce expenses while maintaining unbroken service availability.

Is Cloud Migration Difficult and Challenging?

is cloud migration difficult and challenging

Nowadays, a lot of organizations choose to move their data, apps, and information from their own servers or nearby data centers to the public cloud. This action is designed to fulfill the particular requirements of each organization and has several advantages. Depending on how many resources are employed in the project, the difficulty of this migration procedure varies. Business services, online and mobile apps, IoT devices, edge servers, CRM systems, productivity software, enterprise databases, remote desktops, SD-WAN, network administration tools, and more platforms may all be transferred to the cloud.

Public cloud service providers with a solid reputations like AWS, Microsoft, IBM, Google, and Oracle give companies access to a strong and outstanding infrastructure. These service providers enable companies to function at a pace and scale that is unheard of thanks to high-speed fiber-optic connections that span several data centers across the world. Additionally, they provide a wide range of programming, web development, and support tools for mobile applications. For organizations that decide to host their operations in the cloud, this translates to improved support, quicker performance, and increased dependability.

Benefits of Cloud Migration

benefits of cloud migration

High Security

Security is a top worry for businesses, and cloud computing is a potent tool for addressing these worries head-on. Cloud service companies take enormous precautions to safeguard their systems, making it very difficult for hackers to get past their protections. They use strict security procedures and continuously monitor their systems, often round-the-clock, to quickly spot any shady activity.

Additionally, cloud service providers are aware of how crucial data preservation is. They often generate backups, ensuring that organizations can quickly restore their data in the case of data loss brought on by unforeseeable catastrophes like floods or fires. Furthermore, some service providers give extra services that are especially made to help organizations resume operations as soon as possible in the event of an interruption.


Scalability is a significant benefit of cloud computing. This implies that organizations may easily alter their computer resources to meet their needs. This functionality is very useful for businesses that face fluctuating demand or unexpected spikes in website traffic.

Simply said, cloud computing enables firms to rapidly extend or contract their resources as needed. Assume a corporation need additional processing power during peak periods, such as Christmas sales. They can easily scale up their resources with cloud computing. During slower periods, on the other hand, they may easily cut back on resources to save money.

Cost Saving

For enterprises, cloud computing provides huge cost reductions. In the past, businesses had to spend a lot of money on purchasing physical infrastructure and pricey software applications to support their operations. Microsoft Office 365 survey reveals that 82% of small and medium-sized businesses (SMBs) have experienced cost reductions after embracing cloud technology. In addition, 70% of these businesses are reinvesting the saved funds back into their own operations.

Businesses may, however, avoid these up-front fees by moving to the cloud and only paying for the services they really use. Additionally, cloud providers manage updates and maintenance, negating the requirement for enterprises to employ specialized in-house IT staff. In simpler terms, cloud computing lets businesses save a ton of money by not having to buy expensive equipment and software upfront. They only pay for what they use, like renting instead of buying.

Plus, the cloud provider takes care of all the technical stuff, so the company doesn’t need as many IT people. It’s like having a cost-effective and hassle-free IT solution at your fingertips. Deloitte reports that a significant portion (62%) of the IT budget allocated by business and professional service companies is devoted to internal maintenance. However, embracing cloud migration can bring substantial advantages to your business. One such benefit is the ability to leverage economies of scale offered by public cloud providers like AWS, Microsoft, IBM, Google, and Oracle.

High Integration

By utilizing cloud technology, your company may effortlessly combine various systems and improve the efficiency of its services. According to a Frevvo poll, 59% of small and medium-sized businesses (SMBs) reported greater productivity after implementing cloud solutions. Data centers, like any other equipment, can become burdened with increased workloads and decreased efficiency over time. However, when it comes time to replace hardware, organizations now have the option of migrating their apps to the cloud. This change has various advantages, including the cloud provider handling hardware and software upgrades, saving money and time, and guaranteeing that apps always function on the most recent infrastructure.

Cloud services and apps are constantly improved, updated, and expanded to meet the demands of businesses and customers. This adaptability enables your cloud environment to expand and adapt to match your changing business needs, allowing your team to do more than ever before. Moving to the cloud also allows your mission-critical apps to adapt to changes in user traffic in real-time. Furthermore, your cloud provider can manage the complexities of managing your infrastructure, allowing you to concentrate on what actually matters: your business. With the simplicity and convenience of cloud-based remote access, your staff can focus on working diligently and pushing your company’s development.


By keeping your data in the cloud, you ensure that it is accessible no matter what the status of your physical infrastructure is. Cloud migration has the benefit of allowing people of your organization to access critical data and business information from anywhere in the globe, on any device. This creates several options for your company to grow and expand while meeting operational needs. Furthermore, having backup and logging systems in place becomes critical, especially when recovering from an outage and determining the underlying cause of the problem. Backups allow you to quickly restore activities, while logs provide vital insight into the cause of the problem. Following a cloud migration, your team will be able to deploy, upgrade, and debug numerous computers without being restricted to a single location. This flexibility reduces the headaches typically associated with traditional on-premises setups. And the best part is, the cloud’s consistent provisioning and deployment processes foster collaboration, ensuring that your entire team is synchronized and working towards the same goals.

Improved Business Agility

This is the main reason for the evolution of modern global economy. With access to dynamic, on-demand IT resources, you can keep pace with competitors and with the changing scenarios. Cloud fulfills 99% of your needs and companies need not wait for months to get hardware components and do installations. Companies can rapidly enter the market by leveraging the valuable capabilities offered by cloud providers through leasing arrangements.

Data Center Consolidation

Companies now do not have to manage their own data center premises. IT executives can collaborate with third-party cloud providers and can reallocate resources to higher-value activities. Also, enterprises can integrate operations and provide access to cloud services as and when needed resulting to increased efficiency.

Enabling Digital Transformation

With the spurt in enterprises getting upgraded to cloud solutions and with the recent advances in cloud computing, leaders have increasingly digitized core functionality, including SAP, CRM, data analytics, and much more. Those who migrate from legal technologies see improved productivity in their workforces and tap the new opportunities of generating revenue.

Accelerates Growth

Organizations are growing rapidly by integrating new acquisitions into existing platforms in the cloud. This is helping to scale quickly according to the demand and can use autoscaling functionality with flexible data management services.

Cloud Migration with Softqube Technologies

migration with softqube technologies

Many businesses have already taken the wise decision to migrate their operations to the cloud, and they have enjoyed significant benefits as a consequence. Netflix is a perfect example, having successfully moved its whole infrastructure to the cloud in 2016. This strategic decision enabled Netflix to lower expenses while increasing scalability, allowing it to successfully handle its large user base. Softqube Technologies has played a critical role in supporting easy and successful migrations to Amazon Web Services (AWS) for several enterprises over the years.

Undoubtedly, cloud migrations may be difficult, necessitating the assistance of a dependable company to guide you through the entire procedure. Softqube Technologies takes pleasure in collaborating with your team at every stage of the migration process to ensure a smooth and efficient transition. Our objective is to cultivate long-term ties that go beyond particular projects, building collaborations that will persist for years. To acquire a free evaluation and price for your cloud migration, please contact us using the link given. Please do not hesitate to contact our cloud migration specialists immediately to explore how we can help you achieve your objectives.

AWS Lambda Pricing: Know How To Control The Costs.

Synopsis: The ultimate purpose of this blog is to ascertain the AWS Lambda cost strategies and to prevent unexpectedly large AWS Lambda bills. Go ahead and check out how AWS stands apart from those rental virtual machines and the price changes that happen due to the API Gateway. After reading this blog you will be capable enough to determine the cost estimation of running a serverless application and also the inevitable cost optimization strategies.

Besides procurement, the study of the cost has become one of the most crucial factors of the software development life cycle. Every solution architect must understand the nuances of choosing any specific technology. Software architectures created on abstractions such as containers, orchestration, and with the current serverless (FaaS) amplifies the growth velocity and shrinks complications and costs.

The overall cost of running applications is based upon several factors such as database choice, application adoption, hosting, etc. Companies need experts who provide quick and practical decisions to handle the cost complexities that cannot be given completely to the development teams.

AWS Lambda Pricing is pretty direct and open. However, the cost of running a serverless application is much complex and challenging as compared to other aspects.

What is AWS Lambda Pricing

AWS Lambda is one of the highly refined series of cloud technology products that you can purchase. Also renowned as serverless or function-as-a-service or FaaS. To make it simple, it is a piece of code that runs in an ephemeral container that gets terminated after serving its purpose. These are invocation tasks through which you pay only for what you use. Every single Lambda function is configured depending on the size of the memory in GB and the execution time in milliseconds.

The Pricing Model

Memory Size (GB)

From the AWS console, this will be the maximum memory size configuration that you can allocate to your function. The function does not use this memory always, as this memory is not the actual one. Even when you reduce the usage of function memory but do not tweak this configuration, you will be unable to have any cost reduction.

Execution Time

The actual time that a function takes to execute its code logic. For eg., if your function is making an outgoing call and awaits the response, the idle time also gets counted in the function’s execution time.

The overall duration of the executing functions gets calculated from the time your code begins execution. Unless it returns or gets terminated, and rounded up to the nearest 100ms.

The cost of each function is calculated by multiplying both these values together to produce a unit in GB-sec. Since GB-sec is dependant on several unpredictable factors. Here’s a chart that shows the execution cost of 100000 invocations of a Lambda function over varying time. You will observe that the execution time of the ,Lambda function depends on the language runtime and 3rd party dependencies.

Note: GB-sec is just a composite unit used to measure AWS Lambda offering. Nor does it showcase gigabyte nor a second. The current price of AWS Lambda is

  • Compute charges: $0.00001667/invocation
  • Request charges: $0.2/M <requests

AWS Lambda Pricing vs EC2

A brief comparison of the charges between the two varied use cases:

Low Compute Use Case

The following scenario can be taken for the application. The use cases can be such as CRON jobs, transform and upload, read/write to DynamoDB, etc.

  • Allocated memory 512 MB
  • No. of invocations: 20,000 times/month
  • Execution duration: 1 sec

AWS Lambda Pricing Calculation

GB-sec = 20,000 * 512/1024 = 10,000 GB-sec

Compute charges = 10,000 * $0.00001667 = $0.1667

Request charges = (20,000/1,000,000) * $0.2/Million = $0.004

Total charges = $0.1667 + $0.004 = $0.1707

EC2 Pricing Calculation

Even if we consider the smallest available on-demand instance t2.nano, the monthly cost would be $5.832

High Compute Use Case

The following scenario can be taken for the application. The use cases can be ETL jobs, real-time data processing and video processing.

  • Allocated memory 2496 MB
  • No. of invocations: 30,000,000 times/month
  • Execution duration: 500 sec

AWS Lambda Pricing Calculation

GB-sec = 30,000,000 * 0.5s * 2496/1024 = 36,562,500 GB-sec

Compute charges = 36,562,500 * $0.00001667 = $609.5

Request charges =  (30,000,000/1,000,000) * $0.2/Million = $6

Total charges = $609.5 + $6 = $615.5

EC2 Pricing Calculation

Considering this as a high-end compute work, even if we consider m4.large with 8GB memory, the monthly cost would be $138.2424


  • On-demand instances are suitable options. Lambda is designed for the functions that can be executed rapidly after being triggered by the events.
  • You can avail of the on-demand EC2 instances by spinning up the container manually each time a function gets called.
  • A high amount of processing overhead gets added up and makes the function slow.
  • If at all you want to use EC2, you need your instance readily available and waiting for the triggering event. That means, a 100% instance usage per month.
  • The real backbone behind Lambda’s pricing model is that your functions are not going to run 100%. Lambda remains at its best at around and below 3 million function executions per month.
  • If your traffic gets ahead of 3 million, then it’s time to move to EC2. Before that, you can consider the next observation.
  • From the viewpoint of use cases, AWS Lambda and EC2 have varied implications. AWS Lambda becomes a perfect fit for low compute use cases like serverless authentication, CRON jobs, Alexa skills, etc.
  • High compute resources are used for use cases such as real-time audio/video transforming, ETL jobs, etc.
  • No one size fits all use cases and so tried and tested approach becomes the best

Amazon API Gateway Pricing

Amazon API Gateway charges for used APIs without any upfront fees. Pay for the number of API calls you receive and the amount of data transferred and caching. As an example:

  • Charges for API Calls: $3.50 per million API calls received.
  • Data Transfer Costs: First 1 GB free then $0.09/GB for next 9.99 TB.

For example, a regional API received 5 million API calls per month and each API call returns responses of 3 KB in size without caching.

  • Amazon API Gateway API Calls charge: 5 million * $3.5/million = $17.50
  • Total size of data transfers: 3 KB * 5 million = 15 million/KB = 14.3 GB
  • Data transfer charges: 14.3 GB * $0.09 = $1.29
  • Total Amazon API Gateway charges: $17.5 + $1.29 = $18.79

API Gateway Significance in Serverless Apps

API provides a value-add to the serverless functions by taking care of normalizing access points for system function. This includes security and monitoring. In consideration of the viewpoint architecture, APIs assist in functioning bridge service modeling among the systems. This is done by acting as an HTTP of, an endpoint to trigger your functions.

API Gateway enables collection of function in a manner that they look like a single API developed to meet the specific application requirements. This approach enables tackling the load of security, orchestration, and transformation of the microservices. Moreover, it works as a gateway to forward requests only for those that are authenticated and authorized.

Price Calculation of Serverless App

The serverless costs exceed pay-per-trigger. Here we shall analyze the running cost of a demo serverless application and find out how much AWS Lambda and other services cost in actual sense. As a demo app, we will analyze the Voting App. The architecture is as below:

Taking you through the charges that are incurred for utilizing the below services:

  • API Gateway
  • AWS Lambda
  • DynamoDB
  • Amazon S3
  • CloudWatch Logs
  • Network Data Transfer

Voting App served almost 2 million requests per month.

1. API Gateway

The current API Gateway Pricing is $3.50 per million requests, plus data charges. Considering we have 1.5 GB of cache memory and 2 million requests/month, our total cost would be $34.36

2. AWS Lambda

Since most of the compute work is focused on the 60 lines of code that two of our Lambda function executes, this is where I was expecting huge costing. However, it isn’t. Here we have 2 Lambda functions with 256 MB compute memory, serving 2 million requests/month.
Lambda function 1 executes at 400 ms and Lambda function 2 executes at 500 ms. The total cost will be Lambda Function 1 ($0.20) + Lambda Function 2 ($0.20) = $0.40

3. DynamoDB

The DynamoDB pricing includes permanent free tier up to 25 write units and 25 read units. This means we don’t have any charges for that. The only cost that we will have to incur is of 1 GB Dataset with strong data consistency. Hence, the overall cost will be $2.81

4. Amazon S3

This will be storing our static content like HTML, CSS and Java. The S3 cost for standard storage of 1 GB would be $0.02

5. CloudWatch Logs

The CloudWatch costs for the service coming from logs being sent to CloudWatch Logs and the storage of these logs. These logs are generated by the AWS Lambda function execution and by API Gateway execution. You can control how many logs you need to generate from AWS Lambda and hence optimize accordingly.

Our demo app ingests 200 MB of data every day which is monitored over 3 parameters with archival of logs. All this comes down to 3 GB * $0.50 = $1.5 Plus, storage cost of $0.03/GB = $0.09 Hence, total cost = $1.59

What is the actual cost of Serverless?

It comes down to a monthly cost of $50.14 approx. The most amazing thing is there is something more than just CPU and RAM. The majority of people opt for serverless and the main cost drivers would never be AWS Lambda, but API requests, storage, and network transfers.

And everything about the hidden costs is as below:

API Requests

As discussed in the previous section, it is obvious that our serverless apps are going to be API heavy and its significance is undeniable. This will cost you around $3.50 per million executions.


If you’re sending data in/out, you need to carefully monitor the cost of this. At $0.50-$0.90 per GB-out and $0.1-$0.2 between VPCs/regions on AWS. This has a high chance of getting expensive.

Code Maintenance

The serverless coding is equal to the maximum code lines. With each new functionality add-on to the software system, the number of lines grows steeper as they are inevitable to maintain the software functionality. Every function has its own logic as the shared code is an antipattern.

Cold Starts

Cold start increases the execution time. It is directly proportional to the costing.

Function Monitoring

AWS Lambda lures the users with its attractive low-cost solutions. This serves as one of the primary reasons behind its faster performance compared to competitors.

However, no matter how much the developers keep a keen eye on the maximum size limit and execution time, there are high chances of costs getting out of your control for an end number of reasons. Some of them are DDoS attacks, excess memory allocation, or a simple bug in your code bundle.

This necessitates the requirement of monitoring function efficiently. The three basic ways you can get started are as below:

Manual Approach

Based on the number of functions, it takes from minutes to hours. For example, every single time your Lambda function gets executed, record prints:

Duration: 1000 ms Billed Duration: 500 ms Memory Size: 1024 MB Max Memory Used: 50 MB

The above record print depicts that the function you’ve executed is just using 50MB out of 1024MB. In such cases, you can save money by allocating 128MB. If you go allocating less memory than this, your function might not execute at all.

The development teams prefer higher memory allotment as more CPU means quicker executions and substantially lower costs. And so, the recommended solution is to test and iterate your function’s execution time on different memory sizes.

This approach is the most workable for the team as it has fewer functions since monitoring them becomes easy. Besides, this needs monitoring of functions on regular basis. However, it takes considerable time if you have many functions.

Cost Explorer

AWS Console provides AWS Cost Explorer. Through this explorer, you can monitor the pricing of various AWS services over a week, month, and quarter. You can also classify based on the region, usage type, tag, name, etc.

If you have tagged your Lambda functions properly, this will ease your monitoring up to a great extent. Also, you can tally the pricing of your Lambda functions and iterate the changes accordingly.

Besides, if you haven’t tagged your function properly, AWS Cost Explorer won’t help much. In such cases, you can leverage CloudWatch Alarms to get a notification when a function exceeds the configured limit.
Even though it helps in monitoring the cost of Lambdas, it doesn’t give you insight into what is wrong and where. This helps only in figuring out whether Lambda costs have increased or not whenever an issue occurs.

3rd Party Tools

Several third-party tools come in handy. They help you with cost optimization indirectly by efficient and accurate monitoring of your AWS Lambda functions. Some of them are:

  • Epsagon
  • Thundra
  • Dashbird
  • Stackery
  • IOpipe

If you tune your executing time and memory size you will save a lot on computing resources and costs. With that, it becomes equally critical to keep a sharp eye on those functions that can be optimized by tracking its costs in real-time.

AWS Lambda Cost Optimization Strategies

A series of combinations of usage and configuration affects the entire pricing game of Lambda functions. This creates problems often. To avoid such scenarios, a few strategies are recommended below to keep your Lamdba cost in control.

Optimizing Function Frequency

Various factors influence the invocation frequency of the Lambda function. This is completely dependable on triggers. You need to monitor your triggers and find what can you do to deduct the number of invocations over time.

Writing Efficient Code

Those functions that execute in half of the entire time is the one that costs half the money. Execution direction is directly proportional to the amount of money you will be charged. So it becomes very important to keep a watch on the Duration metric inside the CloudWatch. Also, modify and iterate your function if is taking a long time to execute.

Monitoring Data Transfer

While you consider the Lambda charges, most of the time, it gets out of our site that there are charges for data transfer at standard EC2 data transfer rates. So, here you must keep a watch on the amount of data you are transferring out to the internet and other regions of AWS.

The internal data transfer and for the Cloudwatch, you cannot do much about it. For such cases, what we can do is:

  • Keenly monitor your AWS Cost & Usage Report. Filter it by Resource (your Lambda function) and find different values in the Transfer Type column. Further, get the usage amount. This might come off as slightly time-consuming.
  • Log the size of data transfer operations in your Lambda code and then configure a CloudWatch Metric Filter as a reference that becomes a CloudWatch Metric.

Final Thoughts

To conclude, tracing your requirements and finding how much it will cost to bring forth your idea into reality, this first step is very crucial towards getting started with serverless architecture.

Remember, No one size fits all! Some organizations mean serverless as the helping hand in cutting down the costs with a huge margin. And some other organizations are probably looking for more integrated options that can help you find the hidden costs of serverless.

Every serverless provider will offer you free tiers that are more than sufficient for you to have a smaller workload. But, serverless can offer a huge potential in cost and time saving if is known how to do it right.

Stay away from surprises and get enough knowledge of the potential of your serverless application. We would love to hear from you about your experiences with serverless costs. Write to us or contact us to share your thoughts.
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Cloud Pricing Comparison 2021: AWS vs Azure vs Google Cloud


The most dynamic and popular cloud providers in the digital world are AWS, Azure, Google Cloud, and IBM. They are gaining wide eminence due to continuous price drops of cloud instances, adding discounting options and instances, and calling off the billing increments in specific instances. The below blog takes you through the virtual instances’ pricing comparisons enabling you to choose the one which is the cheapest yet highly productive.

Price changing frequencies have led to difficulty making cloud pricing comparisons between AWS, Azure, and Google cloud. These variations may provide monetary value to what you pay today for availing cloud services. However, it showcases some of the most valuable observations in terms of cost differences that may not have been identified so far. Nevertheless, this will help you out to make a perfect budget estimation for your next software development project.

More than 75% of the organization’s cloud is represented by computing resources. Of course, what you pay for is all about the compute instances. Further, you will be diving into a brief explanation on the pricing of Virtual machines, Instances: in short comparison between AWS EC2 vs. Azure VMs and Google Clouds VMs. As you glance over the cost of instances, you come across various price ranges. The reason why it becomes so crucial to check the instances is that it helps to reduce the billing amount of the subscribers. The slightest of the difference in the instance pricing leads to saving several thousand dollars into the cloud bill.

To have a clear understanding of the price differences, we will take the same region for CPU and OS comparisons for AWS instances, Azure Virtual Machines, and Google Cloud machines. We shall consider

  • US East-North Virginia as a Region
  • Linux and Windows as Operating System
  • 4 as CPUs/Cores

Types of Instances or Virtual Machines

There are four types of instances as follows:

  • General Purpose
  • Compute Optimized
  • Memory-Optimized
  • Accelerated Computing

To give you clear comparisons between AWS EC2 vs. Azure VMs vs. Google VMs, we selected a few instances that have similar RAM with 4vCPUs for each type of instance. Although, you must keep in mind that the memory-optimized and accelerated-computing instances for Google cloud are an exception. The reason is their limited vCPUs at present that do not start 4vCPUs, just AWS and Azure do. Instead, they start at 40vCPUs for memory-optimized systems and 12vCPUs for accelerated computing systems, which illustrates more advanced computation possibilities and seems over-the-top when looked at from a pricing angle. Over and above, it must be remembered that memory-optimized and accelerated instances of Google Cloud offer superior memory.

The table shows some selected instances/VMs for cloud-pricing comparisons of computing.

Pricing On-Demand Comparisons

In the below table you can find hourly demand pricing comparisons of each service for each of the four instance typer scenarios as mentioned above.

Note: The red figures in the above table indicate the highest price and the green indicated the lowest price.

Key Observations:

  • AWS and Google Cloud have nearly similar pricing strategies for systems. They operate on general-purpose and on the instance types of memory-optimized cloud.
  • There is a negligible price difference between AWS and Azure with reference to their respective compute-optimized cloud instances. Whereas, Google Cloud is priced the highest in this service. Google has scalable processors and all-pervasive turbo performance
  • Google Cloud memory-optimized and accelerated computing instances have higher prices as they provide 40vCPUs and 12vCPUs respectively. On the other hand, AWS and Azure have 4vCPUs.

Pricing Discounted Comparisons

All three cloud providers offer business discounts and on-demand instances if they commit to use for 1 or more years. These discounts are represented with various names such as for AWS is “Reserved Instances”, Azure is “Reserved Savings”, and “Commitment Price” for Google Cloud. These types of discounts encourage businesses to commit themselves to a preset level of usage for a fixed period in return for a discounted hourly rate on some instances and VMs.

We consider a one-year commitment period with zero upfront cost for the calculation of discounted pricing among the AWS, Azure, and Google Cloud. At present, there is also a three-year commitment plan offered by various cloud technologies for businesses that are confident they will run longer.

Azure and Google Cloud provide 3-year commitment plans similar to AWS. AWS offers a fixed average discount of 40% for all the 1-year commitment plans irrespective of the instance type.

Note: The red figures in the above table indicate the highest price and the green indicated the lowest price.

Key Observations:

  • The on-demand pricing of memory-optimized Google Cloud was the highest. Whereas, the 1-year commitment price is the lowest compared to AWS and Azure. The reason is Google Cloud offers a 1-year commitment to memory-optimized instances on two divisions- vCPU/hour and GBs/hour used separately as per the requirement.
  • Google Cloud is way cheaper than AWS and Azure in terms of computing optimized cloud-based instances. But when it comes to the instance types of accelerated computing, it becomes more expensive
  • The general-purpose instances for AWS and Azure are almost similar for 1-year committed/reserved plans

Pricing Per-Second Comparisons

Per-second billing – AWS

In the year 2017, AWS declared possibilities of per-second charges on EC2 Linux-based instances and EBS volumes. At present, per-second billing still remains applicable to them and extends to other services as well. Although there is no official indication of second-wise charges for Windows or RHEL, yet there are countless users in developers’ forums sharing the ways AWS services are billed per second for Windows.
There are speculations that there could be a bug while some believe it might be a trial run by AWS. But the official announcement confirms either theory is yet to be framed. However, AWS officially announces that each partial instance consumed in an hour by a single user will be invoiced per second for all the Linux instances.

Per-second Billing – Azure

Azure now allows second-wise charges since late 2019 but does not give any long-term commitment. They are to make it available for all instances. They are still identified to be billed by the minute. The reason is their per-second billing still focuses largely on container-based instances.

For regular virtual machines, Sanders states that Microsoft aimed to focus on containers as they believed that it’s containers where per-second billing can make the most sense. He says, “We’re always looking to improve billing constructs across our platform and to make it easier and more agile for our customers to use”

Per-second Billing – Google Cloud

Previously, Google Cloud used to bill per hour, followed by per minute just as AWS does. When AWS announced the possibility of per-second billing, Google Cloud followed suit. The second-wise billing offered by Google Cloud is much better than that of AWS as the latter provides this service only on Linux-based instances. And with Google Cloud, per-second charges are applicable to all VM-based instances. They have now improved their invoicing capacities with a reduction in user burden and focusing on the granularity of seconds.

For Google Cloud, all the instances are measured in the number of seconds, and after each minute, the instances are billed in 1-second increments. Also, you need to keep this mind that if the virtual machine runs for anywhere in between 30 seconds and 1 minute, then the instance is considered to be terminated and yet is still billed for 1 minute of usage.

Pricing Serverless Comparisons

AWS Lambda, Azure Functions, and Google Cloud Functions are a few of the serverless services available in the present market. They charge you in 100-millisecond increments for the computing power you use. This keeps the developers focused on code building and event triggers. The rest of the part is taken care of by the serverless service providers

For reserving CPU cores and RAM of the underlying EC2 instances and virtual machines you no longer need to pay in terms of considering the cost. This means, you only pay for the time your code runs and not for those times where it remains idle.

Below few examples taken from the hypothetical scenario that will help you understand the calculations of the cost of serverless computing for the three service providers.


You have allocated 512MB of memory to your function, executed it 3 million times in one month, and ran it fro 1 second each time.

Amazon’s API Gateway price per million requests and HTTP invocation cost are not considered in the above scenario. The charges calculation will remain as below.

AWS Lambda Pricing

Monthly compute charges:

  • The monthly compute price is $0.00001667 per GB-second (GB-s), and the free tier provides 400,000 GB-second (GB-s).
  • Total compute (seconds) = 3M * (1s) = 3,000,000 seconds
  • Total compute (GB-s) = 3,000,000 * 512MB/1024 = 1,500,000 GB-s
  • Total compute – Free tier compute = Monthly billable-compute GB- s
  • 1,500,000 GB-s – 400,000 free tier GB-s = 1,100,000 GB-s
  • Monthly compute charges = 1,100,000 * $0.00001667 = $18.34

Monthly request charges:

  • The monthly request price is $0.20 per 1 million requests, and the free tier provides 1M requests per month.
  • Total requests – Free tier requests = Monthly billable requests
  • 3M requests – 1M free tier requests = 2M Monthly billable requests
  • Monthly request charges = 2M * $0.2/M = $0.40

Total monthly charges:

Total price per month for AWS Lambda = Compute charges + Request charges = $18.34 + $0.40 = $18.74 per month.

Azure Functions Pricing

Resource consumption (seconds):

  • Execution number = 3 million executions
  • Execution duration = 1 second
  • Total resource consumption = 3 million executions * 1 second = 3,000,000 seconds

Resource consumption GigaByte Second (GBs):

  • Resource consumption in GBs = 512 MB / 1,024 MB = 0.5 GB
  • Execution time (seconds) = 3 million seconds
  • Total consumed GBs = 3 million seconds * 0.5 GB = 1.5 million GBs

Billable resource:

  • Resource consumption = 1.5 million GBs
  • Monthly free grant = 4,00,00 GBs
  • Total billable consumption = 1.5 million GBs – 4,00,000 GBs = 1.1 million GBs

Monthly resource-consumption price:

  • Billable resource consumption = 1.1 million GBs
  • Resource-consumption price = $0.000016/GB
  • Total monthly resource-consumption cost = 1.1 million GBs * 0.000016/GB = $17.60

Execution billing:

  • Total monthly execution = 3 million executions
  • Monthly free executions = 1 million executions
  • Total monthly billable executions = 3 million executions – 1 million executions = 2 million executions

Monthly execution price:

  • Monthly billable execution = 2 million executions
  • Price per million execution = $0.20
  • Monthly execution cost = 2 million executions * 0.20 = $0.40

Final total monthly bill:

  • Monthly resource-consumption cost = $17.60
  • Monthly executions cost = $0.40
  • Total monthly cost for Azure Functions = $17.60 + $0.40 = $18

Google Cloud Functions Pricing

(512 MB / 1024 MB) * 1 second = 0.5 GB-s per invocation
For 512 MB, Google cloud offers an 800MHz CPU. So, GB-seconds would be:
(800 / 1000) * 1 seconds = 0.8 GHz-second per invocation

Per second usage:

  • 3,000,000 million executions * 0.5 GB-s = 1,500,000 GB-s per month
  • 3,000,000 million execution * 0.8 GB-s = 2,400,000 GHz-s per month

Google Cloud offers a free tier of 2,000,000 invocations, 400,000 GB-seconds, and 200,000 GHz-seconds per month. Hence, the billing would be as follows:


  • 3,000,000 – 2,000,000 = 1,000,000 million execution * $0.0000004 = $0.4


  • 1,500,000 – 400,000 = 1,100,000 * $0.0000025 = $2.75


  • 2,400,000 – 200,000 = 2,200,000 * $0.0000100 = $22

Total price per month for Google Cloud Functions = $0.4 + $2.75 + $22 = $25.15

Key Takeaways

The monthly prices of AWS and Azure are almost similar. The reason is the high free-tier offerings they provide Google Cloud Functions charge the lowest unit price for GBs consumed per second combined with free-tier offerings. The highest price/month comes due to the extra cost that is incurred from the MHz CPU performance.


With the aim of becoming a reputed organization, you need to consider several aspects before choosing a cloud service provider. The pricing part cannot be ignored at all. If millions of users use your application and you are going to use thousands of instances, this comparison will help you save many dollars.

Further, if you are planning to build a customized application embedded with cloud capabilities, or migrating to cloud services, or if you are struggling with the current infrastructure and want to check the costs and scalability, we can surely help you. Check out our Serverless services and head on to creating a next-gen cloud-centric application.

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