Cloud Computing in Healthcare: Benefits, Use Cases, & Challenges

Cloud computing has become an important part of modern businesses, and the healthcare industry is no exception. Healthcare providers must use cloud-based solutions if they want to keep up with the fast changes in the industry and give patients the best care possible. When used in the medical field, it significantly affects how information is stored, retrieved, and shared. It can also help hospitals save money, increase speed and agility, and provide better care to patients.

By 2027, the global market for cloud computing in healthcare will be worth $42.21 billion. This growth is likely driven by the growing need for healthcare organizations to store and analyze data and the growing number of healthcare organizations that use cloud solutions.


In some way, more than 83% of healthcare organizations are already using cloud computing, according to a survey done by HIMSS. The survey also found that medical firms are using cloud-based data storage solutions more to improve patient care, lower operational costs, and make their work more efficient. According to a study, global spending on cloud services will increase from $494.7 billion in 2022 to nearly $600 billion by the end of 2023. 

But why are healthcare organizations moving to the cloud? Keep reading this article; we will discuss cloud computing, its use in healthcare, its benefits, various types and platforms, and how it impacts the healthcare industry. Let’s jump right into it!

What is Cloud Computing?

The term “cloud computing” is often used to make computing resources, such as data storage and processing power, available on demand through a network of remote servers. The “cloud” is a network of remote data centers that can be accessed through the internet and used by many people simultaneously.

cloud based healthcare solutions

Cloud computing makes it easy to store documents in a central place where people can access them from any device at any time. In the past, you had to be at a certain place to use software and apps stored on a computer or server. With the help of cloud solutions, users can now access their data and apps through the web. 

Ins and Outs of Cloud Computing in Healthcare

When we talk about “cloud-based healthcare,” we’re referring to the practice of using solutions based on the cloud to develop and administer healthcare services. In contrast to the traditional method of setting up data centers on-site to store data on individual computers, this method gives healthcare stakeholders various ways to access the data servers from a distance. Also, this is very helpful for big and small health organizations because it lets them store data safely away from their main office.

Real-World Illustrations of How Cloud Computing is Transforming Healthcare

how cloud computing helps in communication and data transfer for healthcare providers


Cloud computing can help providers improve patient care by assisting them in keeping up with the latest advances in medicine and technology. Here are ten examples of how cloud computing is used in healthcare:

Thanks to cloud and telehealth services, patients can get clinical care no matter where they are. Telemedicine projects, like telemedicine apps, telesurgery, etc., can use cloud computing as the backbone of their information and communication technology. Also, doctors and healthcare stakeholders can talk to each other and share their knowledge to treat more complex and challenging conditions. In the field of telemedicine, cloud-based solutions can be used for the following:

  • Real-time, cross-border exchange of patients’ medical records
  • Accessing the stored information at a time and location of their choosing
  • Saving time and money by reducing pointless doctor visits

In the drug discovery and recovery process, much computing power is needed to sort through trillions of chemical structures and find promising compounds. The many IaaS services available in the cloud make this process much easier and faster. Several joint ventures, like the one between Newcastle University, Molplex, and Microsoft Research, have used IaaS to help find new drugs, thus saving a lot of time and money.

The healthcare sector has adopted management information systems to improve internal and external communication and serve patients better in several ways. Some of these ways include improving querying services, billing and finances, and the management of human resources. 

Because the data in this system is sensitive, the developers build, test, and put it into use using cloud-based platform services. With the help of cloud technology, the system can be constructed quickly and encourage teamwork. It can also be more easily connected to other healthcare systems.


Health firms can also use the cloud to manage patient health records (PHR) and electronic health records (EHR). Patients usually keep their health records (PHR), while managed-care organizations and single hospitals each keep their electronic health records (EHR) and electronic medical records (EMR). Thanks to cloud computing, it is now easier to control who has access to which resources and how.

healthcare professionals can use EHRs to improve patient data recording

The clinical decision support system (CDSS) is a highly developed program that acts and thinks like a doctor to help doctors analyze patient records. It is an expert system that models its recommendations on the knowledge and actions of a practicing doctor who has reviewed the patient’s medical records. 

With the development of fitness trackers and smartphones with biometric sensors for monitoring vitals like heart rate, blood pressure, and glucose levels, these cloud-based systems are helpful for real-time diagnosis. In addition, health providers can use CDSS for making diagnoses and writing prescriptions.

The adaptability and cost-effectiveness of cloud technology have made it a useful tool in the classroom. Medical students, doctors, professionals, and researchers are increasingly turning to cloud-based libraries to keep up with the most recent developments in their field.  


Digital libraries ensure that teachers, students, doctors, and scientists can always find the most up-to-date resources when they need them. In addition, doctors can learn about developments in the healthcare system and find resources to help them become more efficient in their work.

With cloud systems, healthcare organizations can better connect with patients because they can access patient data quickly and easily. Also, doctors and nurses can check on patients from a distance and collect real-time data.


Cloud computing helps healthcare organizations manage their supply chain more efficiently. They can also better manage patient populations more effectively, as they can access real-time data and make better decisions faster. 

Cloud solutions make it easier and faster for healthcare organizations to make and use mobile health apps. Health firms can also store and manage medical images quickly, securely, and efficiently.


Cloud-based solutions can help healthcare organizations prescribe medications to patients safely and efficiently. In addition, the big data analytics feature of cloud computing makes it easier for healthcare providers to analyze patient data and make informed decisions.

Types of Cloud Computing in Healthcare

Cloud computing in healthcare is classified based on its deployment environment and distribution service. 

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In the deployment environment, there are three main types of cloud computing in healthcare: public, private, and hybrid. We’ll briefly describe them below:

  1. Private Cloud Services

Private cloud services run on the organization’s servers and are managed by the IT department of the organization. Private cloud services are better for healthcare organizations that need to store and access sensitive data because they give more control and security than public cloud services. 

  1. Public Cloud Services

Public cloud services are hosted on the public internet and managed by a third-party hosting provider. Public cloud services are typically the least expensive and most widely used in healthcare. Public cloud services are ideal for healthcare organizations that don’t have the resources to manage their cloud infrastructure. 

  1. Hybrid Cloud Services

Hybrid cloud services combine public with private cloud services so that businesses can get the best of both worlds. Hybrid service is ideal for healthcare organizations that need to store sensitive and non-sensitive data. Hybrid cloud services combine the flexibility of public cloud services with the security and control of private cloud services.


Under distributive service, there are three major types of cloud computing in healthcare: SaaS, PaaS, and IaaS. We’ll briefly describe them below:

  • SaaS

SaaS is a cloud-based technology that offers web-based applications that are already set up, such as medical records management systems. With software as a service (SaaS), healthcare organizations can use cloud-based applications, but an outside party handles the hosting and management of those applications.

  • PaaS

Platform as a Service (PaaS) is a type of architecture in which development environments are hosted and managed by a remote cloud service provider. It also deals with software and hardware tools, such as a debugger, compiler, and source code editor. With a PaaS, HealthTech developers can quickly build, test, and launch their apps in production environments.

  • IaaS

IaaS, which stands for “infrastructure as a service,” is when an outside cloud provider hosts a network’s servers and storage spaces.


9 Benefits of Cloud Computing in the Healthcare Industry

The introduction of cloud solutions has greatly benefited the health sector. Let’s look at some of the significant benefits of cloud computing in the healthcare industry.

benefits of cloud computing in the healthcare software domain

With the help of cloud computing, the managed healthcare system can easily combine data from many different sources, like other facilities, data repositories, healthcare apps, wearables, etc. As a result, it helps spread information about patients, lets doctors make quick diagnoses, and ensures that people get the proper treatment as soon as possible.

Cloud solutions are usually cheaper than on-premise solutions, which can help healthcare organizations save money. Also, they can help lower operational costs by automating data storage and computing power and eliminating the need for physical storage.

Cloud solutions can help healthcare organizations streamline processes to do more work in less time. Healthcare providers can do their jobs more effectively by making it easier to access and analyze data and by using less expensive and time-consuming IT infrastructure. 


Cloud providers typically use advanced encryption technologies and access control to protect sensitive medical data while in transit and at rest. Also, because cloud service providers regularly update their data security measures, health firms can keep sensitive medical information from getting into the wrong hands and prevent data breaches. 

By using cloud computing, organizations can take advantage of the security measures that providers have in place rather than developing and maintaining their security infrastructure, which can be costly and difficult to manage.

Cloud computing makes it easier for healthcare providers to collaborate and share data with other providers more quickly and securely. Also, healthcare providers can quickly and easily access patient information, which lets them give timely, personalized care.

Cloud solutions can make it easier and faster for organizations to grow their operations because they can get more resources when needed. Also, you are only charged for the resources like data storage and computing power that you use. Thus preventing business owners from spending money upfront for expensive in-house hardware and servers that they may or may not use to their full potential.


Cloud storage for medical records makes storing and finding data more accessible, reducing the need to re-enter data and making it more accessible. When patients have access to their records whenever they need them, they feel more in charge of their health care and have a more significant say in the decisions that affect them. It helps with self-care and boosts engagement.

Cloud adoption can help healthcare organizations respond more quickly to changing market conditions as they can access more resources as needed. It also helps healthcare organizations rapidly adapt to evolving regulations and technologies.

Cloud computing helps healthcare organizations reduce medical errors by providing real-time access to data. Organizations can also reduce their risk of data breaches, as cloud solutions are typically more secure than on-premise solutions.

The Daunting Sies of Using the Cloud for Healthcare

In the same way that there are benefits to using the cloud for medical care, the innovation also has potential drawbacks and dangers to look out for. As a result, many businesses are still determining whether or not to adopt the technology. Some potential risks of using the cloud for medical care include the following:

Moving from an aging system to a cloud-based one necessitates a comprehensive rethink of how work is done. Healthcare institutions need to let everyone know what their daily work means.

One of the main ways this technology is used is to store sensitive medical information in the cloud. However, because of this, it’s vulnerable to assault. In addition, in a typical configuration, the data of many healthcare organizations is stored on the same server, leading to a risk of security breaches. Also, the isolation mechanisms put in place to prevent cross-contamination may fail.


Like other industries, healthcare won’t become more efficient by only adopting cloud computing. Instead, to get the most out of the technology, healthcare organizations must connect it to IoT, AI, and data management systems.

When it comes to healthcare software, it can be hard to find skilled developers who know how to use the latest innovations. Similarly, it can take a lot of work to track down cloud experts with experience in the health system.

how to choose a cloud provider

Choosing the Best Cloud Platform for Your Healthcare Business

Microsoft Cloud, Amazon Web Services (AWS), IBM Cloud, and Google Cloud Platform (GCP) are all well-known platforms for healthcare. However, the best cloud platform for healthcare depends on the needs and budget of the healthcare organization. 

For example, a company might need to store and manage many data in a HIPAA-compliant way. Therefore, a public cloud platform like Amazon Web Services or Microsoft Azure may be the best. On the other hand, if an organization needs a more secure solution, a private cloud solution such as OpenStack or VMware vCloud may be the best option. 

Google Cloud Platform offers healthcare organizations various services, including big data analysis and AI. In comparison, IBM offers cloud-based AI and blockchain solutions.


How does Cloud Computing Impact Your Healthcare Practice?

Cloud Computing is disrupting the Healthcare Industry. Here are the five significant Impacts of Cloud Adoption in Healthcare:

  • Integration: Organizations can easily integrate and securely access patient data across multiple points of origin and storage. This enables healthcare providers to deliver timely, personalized care while also reducing operational costs.
  • Access to high-powered analytics: Healthcare organizations can compute relevant patient data from multiple sources and automate processes, providing real-time access to data and helping to reduce medical errors.
  • Scalability: Cloud Computing offers affordable and quicker scalability solutions that can be highly customized to healthcare needs. 
  • Regularly updated information: Cloud Computing helps healthcare organizations stay updated on the latest Medical Research and Treatments. Cloud computing is changing how data is stored, accessed, and shared in the healthcare industry. This can help them provide the best care and improve patient outcomes.
  • Increased collaboration: Cloud solutions have become a crucial part of healthcare organizations’ digital transformation, enabling them to securely share healthcare data and collaborate with other organizations more efficiently.


Cloud computing has significantly impacted the healthcare industry, and the technology is expected to keep growing in the coming years. It has become an important part of the digital transformation of the healthcare system because it lets healthcare organizations store and manage their data more efficiently and securely while giving their customers better services.


When choosing a cloud healthcare platform or engineers, organizations should consider what they need and how much they can spend. Cloud solutions can help healthcare organizations stay competitive and give their patients the best care possible.

If you still have questions, contact us. The APP Solutions has deep expertise in the field, so don’t hesitate to get us about this or any other issue.  

Do you want to take your healthcare business to the cloud? Check out this article to find out all you need to know about cloud computing solutions in healthcare.

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The role of AI and machine learning in digital biology

Digital biology, also known as biotechnology and digital biotech, gives bioengineers, medication producers, agricultural companies, and industrial businesses excellent opportunities. Biotechnicians can turn biomaterials – living systems and organisms – into a digital data format, organize it, discover hidden patterns, and store it in databases. 

Why does it matter? 

Such an approach streamlines the research, development, and test stages of biology projects that previously took bio technicians months or years. Moreover, medical specialists apply digital biology to diagnose health conditions, such as cancer and sepsis, within several hours and suggest the most appropriate treatment based on patient samples. 

Digital biology took a leap in development by applying Artificial intelligence and machine learning algorithms that automate biological data analysis and research. Thus, bioengineers generate more data in shorter terms, compared with the analog study methods they used previously.

In this article, you’ll find the current state of digital biology and the fields it serves. You’ll also read about biotechnology areas that benefit the most from other intelligent technologies, such as AI, machine learning, and cloud computing. 

The current state of the Digital Biology market 

Digital biology is a cross-disciplinary field that combines both biological and technological components. It includes exploring and analyzing living organisms with new intelligent tools. 

Recognizing the considerable potential of biotechnology, governmental organizations, such as the National Institute of Biomedical Imaging & Bioengineering in developed nations and the National Center for Biotechnology Information, increased their investments into the research and development activities in biotechnology fields. 

The market research from Global Market Insights (GMI), a global market research and management consulting company, says an increased interest from governmental organizations is expected to make biotechnology the largest and the fastest-growing market, projected to reach $729 billion by 2025, compared with $417 billion in 2018.

digital biology market overview

The research also includes a forecast of revenue increase for the following technology segments:

  • Fermentation 
  • Tissue engineering and regeneration 
  • PCR technology 
  • Nanobiotechnology 
  • Chromatography 
  • DNA sequencing 
  • Cell-based assay

And others. 

In particular, the fermentation segment is the most prominent sub-niche of biotechnology that received an 11% revenue share of the whole biotechnology market in 2018. 

The report predicts substantial progress for fermentation technology during the next few years. Fermentation is a process that changes organic substrates on the chemical level by enzyme action and micro-organisms. 

Such growth of fermentation technology is explained by excessive use in the food and beverage industry. The food and beverage industry’s key business players will increase investments in biotechnology to improve the research and development activities to produce more fermented products. 

The expected growth of biotechnology opens new opportunities for biotech startups, well-established companies, and research institutions. Another reason for biotechnology’s rise is various applications in medication, agriculture, and other industries. 

Biotechnology Application Outlook

Digital biology, or biotechnology, includes several categories of applications. Biological technicians and other scientists apply digital biotechnology for solving scientific problems with living organisms across various industries – from healthcare and agricultural to industrial processing and bioinformatics. 

digital biology applications in food and agriculture

Let’s see how each category benefits from artificial intelligence and machine learning. 


In medical biotechnology, scientists receive information from living cells to get a clearer picture of human health, thus producing the most appropriate drugs and antibiotics. 

Bio technicians dig into the smallest details to achieve these goals – study DNA and manipulate cells to predict beneficial and vital characteristics. 

The most useful tech solutions used in medical biotechnology are Artificial Intelligence and machine learning that enable scientists to improve their drug discovery process by reaching small molecules and their target structures they need to treat. 

Machine learning algorithms also perform great for patient testing and diagnostics. The algorithm can detect damaged tissues and other abnormalities via medical images, patient samples, and even sounds. For example, intelligent algorithms can detect cancer cells in X-rays, sepsis via DNA sequencing, and define whether the patient has COVID after hearing one’s cough. 

In this way, doctors provide more timely and accurate treatment for better outcomes. 

Moreover, artificial intelligence and machine learning are used in electronic health record (EHR) systems and clinical decision support systems to help doctors suggest a patient’s personalized medical treatment and accurate medication management. 

Food and agriculture

Agricultural biologists apply biotechnology to increase crop yields, genetically modified plants, and identity infected crops before the harvest. For these purposes, scientists use DNA sequencing devices and databases with DNA samples of already sequenced genes. Once new DNA samples are sequenced, scientists can change their structure, learn more about the plant origins and potential issues typical for one or other plant. 

biotechnology applications growth

[Increasing application of cell line engineering will drive the overall market expansion]

Food and agriculture biotechnology companies apply AI-algorithms to harvest crops, watch crop health, and find AI-powered tools more effective than humans. 

Such an application requires food and agriculture businesses to integrate autonomous robots or drones, computer vision algorithms, and deep learning technologies. While drones and robots carry cameras, algorithms analyze crop pictures they receive, compare data captured with crop images in their database, and define whether crops and soil are healthy or not.   

Industrial processing

Industrial biotechnology includes research on biopolymer substitutes, inventions of vehicle parts, alternative fuels, new chemicals, and the process of production. In this area, intelligent technologies and the Internet of Things (IoT) devices help industrial producers analyze their machinery to predict outages, optimize equipment, and even reduce human worker numbers with automated warehouse management. 

One example is Ocado Technology, an online grocery retailer that automated its warehouse with 3500 robots to process 220,000 online orders a week for grocery delivery.

To learn more about AI and machine learning applications in industrial processing and supply chain, check out our previous article about top AI applications in supply chain optimization. 


Bioinformatics is a subdiscipline of digital biology that combines biology and computer science to acquire, store, analyze, and disseminate biological data, such as DNA and amino acid sequences. Scientists understand biological information using mathematics, data science, and different digital biology tools by organizing it in large biological pools. 

Bioinformatics also receive benefits from AI and machine learning. Artificial intelligence and machine learning help biologists sequence DNA from the massive data crunch, classify proteins, protein catalytic roles, and their biological functions. Leveraging intelligent technologies, scientists can automate gene expressions and gene annotation and identify the location of genes required for computer-aided medication design. 

In digital biology, biotechnologists base their research on digital data, generated from life samples or DNA sequencing devices and stored in a thousand databases, both private and public.  

So we can conclude that the growing biotechnology industry will heavily rely on AI-algorithms, machine learning, and data analytics. But the development of biotechnology across all segments depends on biotechnology researchers’ ability to master their skills for the useful contribution of their findings and researcher results. Not only AI makes biotech engineers more efficient, but also for a bunch of other reasons.

Let’s check them out.  

Top 3 advantages of using AI in the biotechnology industry

PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution says AI will contribute to the global output of $15.7 trillion by 2030. By this time, 44% of pharma and life sciences experts expect to adopt Artificial Intelligence in their laboratories and R & D centers and replace analog tests. 

But why do scientists prefer digital biology to the old-but-gold analog approach?

Development and research projects often require scientists to deal with numerous amounts of data and large sample sizes, such as genome sequencing. In such cases, biological test digitalization allows researchers to produce more data than analog study methods. And by applying digital biology, scientists can receive real-time insights into biological functions which have taken them days and weeks when using an analog approach. 

The adoption of AI and machine learning by biology specialists make the digital biology approach even more useful. And here is how: 

Crucial predictions 

Artificial intelligence and machine learning algorithms help bio technicians make more precise predictions than standard approaches used for decades. Successfully applied in supply chain and logistics, predictive analytics drastically reduce the time biotech companies spend to launch new products to market. 

To make data-based decisions and forecast outcomes, data scientists train algorithm models with historical databases. Then, such algorithms are effectively used for pattern recognition, despite the data type. 

As Nature online resource highlighted, intelligent algorithms’ ability to analyze large amounts of data in datasets helps drug-producing companies make new pharmaceuticals quicker and more effective. Soon, medication specialists will provide more personalized treatments, based on the disease’s cause, hidden deeply in biological structures. In this way, pharmaceutical companies can reduce the medicine development process from the $2.6 billion price tag and decrease the 90% failure rate of new medication created. 

In her article, Melanie Matheu, Ph.D. and founder at Prellis Biologics, Inc. the human tissue engineering company predicts the new generation of therapeutics entering drug pipelines empowered by AI screening for selecting targets will reduce clinical trial failure rates for small molecules by 86%. 

Effective decision-making 

Clinical trials used to be manual and a very time-consuming process – they included inviting participants to the clinic during the in-person visit, recording their symptoms, prescribing them treatments, and analyzing side effects. Moreover, to get the right sample size, medication companies heavily invested in marketing resources for recurring right patients and treating rare conditions.  

Now, intelligent algorithms and cloud technologies digitized clinical trials and enabled biotech organizations to test medication on more patients within less time. 

One example is Invitae, a medical genetic company. In November 2019, the company launched a trial in collaboration with Apple Watch to bring together biometric data from wearables and genetic tests and determine genes that cause cardiovascular disease. In this way, the company made the trial available to many people and excluded Apple Watch users who didn’t meet the trial criteria. 

Biotech companies make clinical trials even more effective by leveraging machine learning algorithms that analyze data from current trials and use it for forecasting treatment effectiveness in the future, down to a molecular level. ML also helps scientists revise information from previous tests to find gaps and new applications for existing medications. 


Modern devices, cloud databases, data analytics pipelines, and machine learning algorithms reduced the cost of genome sequencing from $2.7 billion for the Human Genome Project to less than $300 by now. It is expected to cost even less – $100 in the future. Bioengineers receive more extensive screening of trial participants and targeting of interventions. They also see the future in personalized treatment plans and targeted therapies that provide therapies at genetic and molecular levels of patient genes. 

The main area for targeted therapy is cancer treatment – the treatment of blood cancer such as leukemia, where a treatment called CAR T-cell therapy, according to the National Cancer Institute, the immune system will “attack tumors,” so we’ll soon witness more cancer survivors. 

Biotech organizations also use cloud computing to host and run computations and no longer need to buy expensive computer hardware for their research. This fact is a substantial benefit for early-stage startups with limited funding to enter the market with their research and medications. Cloud computing is also handy for established medical corporations, allocating resources for new projects cheaper and more manageable. 

What is the future of AI and machine learning in the biotechnology industry?

Biotechnology is an innovative industry that effectively solves scientific problems with living organisms. But new issues continuously arise and require biotechnologists applying modern methods to be solved. 

Thus, to remain relevant, biotech specialists must make room for improvements. Fortunately, there are many solutions they can apply – AI, data analytics, deep learning, and others we’ve already listed in this article. 

Thus, AI, machine learning, and robotics play critical roles in pushing the boundaries of possibilities in medical, industrial, or agricultural biotechnologies, and will remain relevant for subsequent decades. 

The APP Solutions has experience developing and integrating AI functionality into biotech projects. You can learn more about our expertise in creating a real-time DNA sequence analysis application during our partnership with the Google Cloud Platform and the Queensland University of Technology. Don’t hesitate to contact us if you need experts to advise and develop intelligent software for your biotech project.

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Related reading: 

Calmerry Telemedicine Platform Case Study 

Nioxin Consultation App for Coty-owned Brand Case Study 

We are a Top Cloud Computing Company

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At The App Solutions, we are committed to offering outstanding and cost-efficient cloud computing solutions that create value for our clients and lead them towards the success path. We always strive to turn the ideas of our clients into effective business procedures and leave no stone unturned till the final objective is achieved. On-time project delivery and top-notch code standards are our signatures. Our consistency has been acknowledged by top as in their press release about the leading cloud computing companies, they have included us in the list of efficient cloud computing service providers for February 2021. 

the app solutions top cloud computing company

The App Solutions is specialized in offering bespoke cloud solutions to the needs and requirements of the clients and other business software solutions. Since our inception in 2013, The APP Solutions have been instrumental in rendering top-class services in mobile app development, Big Data Analytics, UI/UX designing, e-commerce, and other related services along with state-of-the-art cloud computing services. To know more about our work style and accomplishments in the past years, visit our portfolio.

Client satisfaction is our foremost focus and we will always be ready to go that extra mile to bring their smile of satisfaction. We believe in delivering a tad bit extra, more than the expectations of our clients. This plays an important role for us in maintaining high rates of client retention and offering flexible cloud computing solutions. Our application development services are competent and exceptionally scalable. In order to see what our clients have to say about us and our work ethics, service specialties, focus areas, and other important details, please visit The APP Solutions’ profile on

We have expanded at a rapid rate in the past eight years. Our exposure to different industries and rendering solutions to companies coming from a vibrant background enables us to understand the market clearly. Our experienced developers and designers use that experience in order to offer top-tier cloud computing and other software development solutions. We have provided our services to companies hailing from different industry backgrounds like transportation & logistics, financial & payment, telecommunications, healthcare & life sciences, business services, real estate, and many more. This is the reason why we find our name in the world’s top cloud computing service providers.

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Public vs. Private vs. Hybrid Cloud Computing

Cloud computing is gradually becoming an accepted standard option for data-driven business operations. No wonder, as the cloud brings a lot of value to the table. Among other things, it is used to streamline workflow, efficiently scale applications, manage machine learning algorithms and neural networks; the list goes on. 

But these are well-known things. What is not often understood is that there are several distinct types of cloud platforms and that they fit different operations. Public, private, and hybrid cloud solutions 

In this article, we will explain:

  • The difference between public, private, and hybrid cloud solutions
  • Where are these three types of cloud used?
  • How to choose the best suitable cloud option?

Let’s start with the basics. 

What is Public Cloud?

The term “public cloud” refers to a general understanding of what a cloud platform is. It is also the most common form of cloud computing used by companies of all scopes. 

In this configuration, your company shares hardware, storage, and network infrastructure with other companies, aka “cloud tenants.” 

The cloud resources (i.e., hardware, software, and related infrastructure) are owned and managed by a third-party vendor (folks like Google Cloud, Microsoft Azure, AWS, and IBM Cloud).

The services themselves are delivered through the internet and managed from a web browser interface.

The defining feature of a public cloud is cost-effectiveness. You get a fine package of high scalability and elasticity of computing capacities combined with relatively low costs of the services.

The traditional structure of the services is a combination of free and freemium (for more basic packages) and subscription-based with a “pay-for-what-you-are-actually-using” pricing structure.

As such, the Public cloud is the right solution for the following:

  • Data storage
  • Data Loss Prevention tools for Archiving and backups
  • Application Hosting and also Development of software applications and flexible testing environments
  • Data Mining, Data Analytics, and Business Intelligence applications with a vast scope of data to work on
  • Applications with high scalability requirements. Things like streaming apps, geolocation apps, file sharing, etc.
  • Applications with predictable computing requirements (internal tools for communications, analytics, etc.)

Public Cloud Advantages and Disadvantages

The advantages of Public Cloud solutions include:

  • Automated deployment. No need for investing in infrastructure. The cloud service provider handles the deployment and maintenance of the infrastructure. 
  • Superior reliability and boundless workload scalability. Public cloud infrastructure provides autoscaling features that allow to balance the workload accordingly and avoid downtime and crashes.
  • Relatively low cost of ownership – the pricing structure is flexible, covering only actually used resources. 
  • The lower costs of services are due to a flexible pricing model that covers only used resources.  
  • The versatility of solutions of public cloud platforms can address all sorts of business needs ranging from storage options to sophisticated predictive analytics neural networks. 

The disadvantages of the public cloud go like this:

  • The total cost of ownership tends to grow exponentially as the company’s cloud infrastructure expands. 
  • Due to its nature, security is always a sensitive issue for the public cloud. While you can do your part, there is no guarantee that the cloud provider will be up to speed.
  • The control over infrastructure is limited, and that may cause compliance issues with different regulations (GDPR and the likes).

What is a Private Cloud?

A private cloud is a form of cloud computing in which the infrastructure is deployed and used by a single organization exclusively. This kind of cloud platform can go as far as to be physically located at the company’s datacenter (or operated by a third-party vendor off-site).

The critical difference between public cloud and private cloud is a significantly higher level of control over the system by the company. The company itself handles the hardware and infrastructure maintenance. The system resources are isolated to a secure private network so that no one from the outside can access them.  

Control and security are the main reasons to use a private cloud. Because of this, it is the preferred option for government institutions, legal & financial organizations, enterprise companies – basically any organization with a high turnaround of sensitive information.

As it is, the private cloud is rarely used as a single cloud solution. Much more common is the use of the private cloud in combination with the public cloud as a place to host sensitive information and critical applications.

This makes a private cloud a reasonable option for companies whose business needs require high adaptability and flexible configuration. 

It also makes sense to go private cloud for organizations that have enough financial resources to handle the costs of maintaining their on-premise cloud data center.

Operation-wise, the public cloud is a preferable option in the following cases:

  • For systems that contain sensitive data that requires private hosting and tight security. For example, personally identifiable data that includes social security numbers, addresses in systems like cloud ERP, etc.
  • When application maintenance has predictable scalability and requires low storage spending.
  • When the requirements are strict security, latency, regulatory, and data privacy levels.
  • The hosting of critical or sensitive business data and applications (communication, analytics tools, etc.).

As such, a private cloud is the right fit for:

  • Industries with high regulations (construction, manufacturing, healthcare, IT) and also government institutions.
  • Tech companies that require full control and in-depth security policies for their data and cloud infrastructure.
  • Large enterprises that require advanced data center technologies to operate efficiently and cost-effectively.
  • Organizations that can afford to invest in high performance and available technologies.

Private Cloud Advantages and Disadvantages

The Advantages of a private cloud are as follows:

  • Full control over the infrastructure – since the whole thing is situated on-site, you have complete control over what is going on with the system.
  • Dedicated and secure environments that cannot be accessed by other organizations.
  • Infrastructural Flexibility – you can freely customize the private cloud to fit any business needs and requirements.
  • Better compliance – since you have more control over the system, it is easier to adapt it to current compliance requirements.
  • More efficient security. The other cloud tenants don’t share infrastructural resources with your company. Thus, there is no threat of external cloud misconfiguration or breach. In addition to this, you can fully implement and manage your security solutions. 
  • High scalability. Private cloud infrastructure retains the same autoscaling features as a public cloud without compromises of security. It should be noted that scalability relies on hardware capabilities. You can’t go broadway if your hardware doesn’t allow this.
  • High SLA performance and efficiency.

However, private cloud solutions also have some significant disadvantages over public cloud solution:

  • The high cost of ownership – you need to maintain the entire system on your own.
  • High IT expertise requirements – you need trained personnel to keep the thing going.
  • Scalability features are limited to on-premise resources (this is an issue if the scope of operation is unpredictable) 
  • Mobile users may have limited access to the private cloud considering the high-security measures in place.

What is Hybrid Cloud?

Hybrid cloud is a sort of a middle ground between public and private clouds. It is a type of integrated cloud infrastructure that includes both public and private options according to specific business needs and requirements. 

  • In essence, the hybrid cloud merges the superior security of the private cloud and the more efficient scalability of the public cloud.

A hybrid solution allows optimizing your cloud investment by providing more infrastructural flexibility and diversity. 

The key is in the distribution of the workload between public and private cloud solutions. In this configuration, the company can orchestrate the workflow so that sensitive information will remain in safety while resource-demanding operations will get what they need without compromises.

As such, hybrid cloud solutions are a good company with high security, regulatory, and performance requirements. In addition to this, the hybrid cloud may be the right choice for companies that operate in vertical markets. In this case, customer interactions are handled in the public cloud, while the internal operations are taking place in the private cloud without a threat of accidental data breach.

Hybrid Cloud Advantages and Disadvantages

The advantages of Hybrid Cloud for business organizations include:

  • Cost-effectiveness. Public and private clouds split the workload. Private cloud handles sensitive operations. Cheaper public cloud infrastructure maintains resource-demanding processes like streaming analytics or big data machine learning.
  • Distribution across different public and private data centers result in higher reliability of the system.
  • High security and performance – the combination of public and private cloud create an environment where you can enforce high-security standards while retaining workload scalability of the public cloud.

However, the hybrid cloud also has its fair share of disadvantages:

  • The maintenance costs can balloon without close monitoring and swift resource management. 
  • Private cloud infrastructure requires compatibility with its public cloud counterpart.
  • The complexity of the infrastructure increases due to the combination of two different types of cloud architecture into one system.

How to choose between public, private, and hybrid solutions?

The choice over which kind of cloud platform to use depends on three factors:

  • Performance & resources
  • Costs
  • Security

The key element of the equation is the business’ requirements. However, it should be noted that the choice between public, private, and hybrid solutions doesn’t mean exclusive use of one option at all times. As time goes by, the business needs may change, and that may reflect on the cloud solution of choice. 

Let’s go through three key factors:


  • Public clouds are more sensitive to security threats due to numerous customers using the same infrastructure and multiple access points to the system. In this case, the cloud provider shares the responsibility for the safety of the system. Infrastructural security is on the provider, while workload security is the company’s responsibility.
  • Because of increased control over the infrastructure, private clouds are more secure. In this case, the company bears full responsibility for the effectiveness of its security policies and protocols. 
  • A hybrid cloud is a mixed bag. As a combination of public and private clouds, you get a lesser case of split responsibilities. However, in this case, your company also has some control over private cloud infrastructure. The key point is that you can distribute the workload across public and private clouds according to compliance requirements, security policies, and other regulations.


  • Public cloud platforms usually operate on a “pay for what you use” model. In the majority of cases, it is flexible and cost-effective. Google Cloud, AWS, and Azure go for 1-3 year purchase terms
  • Private cloud comes with a hefty price tag. You need to purchase, rent, maintain hardware, and manage infrastructural resources to scale the system. A private cloud is worth it if the workload is reasonable, and security requirements are strict.
  • Hybrid cloud costs combine a public cloud “pay for what you use” model and private cloud expenses. In terms of cost-effectiveness, this is the best option, since you can manage the workload and aptly allocate resources according to the current business needs. 

How to make your IT project secured?

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Performance & resources

  • Public cloud resources are limited to the financial resources of the company – they can handle as much as you need. As such, operating expenses grow with the scope of operation.
  • Private cloud resources are limited to the capacities of your hardware. If you want more – you need to buy or rent it. 
  • Deploying more private cloud resources requires buying or renting more hardware—all capital expenses.
  • Hybrid clouds give you the option of using operating expenses to scale out on the public cloud, or capital expenses to scale up a private cloud—you choose based on the situation.

What’s next?

The effective use of cloud platforms completely depends on the understanding of the company’s business goals and requirements, and what different cloud options have to offer. 

In this article, we have explained the general differences between different types of cloud computing infrastructure and where different types of cloud best fit.

In our next article, we will compare different cloud platform providers (i.e. Google Cloud Platform and the like) and explain which of them is good for which kind of operations.

AWS vs Azure vs Google: Cloud Comparison

The choice of a cloud platform provider is one of the most important business decisions for a company. 

It is not just a bunch of technical specs that neatly fit the requirements, it’s about possibilities and making things happen in the long-term.

In this article, we will:

  • Explain how to choose the right cloud platform provider;
  • Compare Three Large Cloud Providers (AWS, Azure, Google Cloud) and their pros and cons.

How to choose a cloud platform?

In one way or another, the choice of which public cloud platform provider is determined by business and technical requirements.

Here’s how:

  • The choice is about what you NEED, not what you want. 
  • A cloud platform is a means of accomplishing a particular goal.

The key choice factors for cloud platforms are:

  • What kind of features do you need?
  • What are your application integration needs?
  • What is the estimated budget for a cloud solution?
  • Is there a need for future expansion or reduction of the features?
  • What is the level of expertise of your IT department? Can they handle the proceedings on their own?

For the most part, the choice is less a matter of taste and preference. In fact, it is more a matter of what kind of possibilities this or that cloud platform offers to accomplish a goal. 

Probably, the single most important decisive factor is cost-effectiveness. Yup, it’s all about the money. 

Cloud computing services are not cheap. If used unwisely – they can be a considerable burden on a company’s budget. While the cloud platform itself can provide an efficient service regardless, the way it is used might be draining and ultimately detrimental to the company’s business operation. Because of this, you need to be cautious about your cloud computing spending. 

Let’s take a look at the major factors which influence the choice of a cloud platform.

Cloud Platform Overview

AWS vs. Azure vs. Google: Pricing Models

Pricing is probably the trickiest thing about cloud computing services. Here’s why. The thing about cloud services is that you can measure the exact scope and calculate a unique price for a particular client. 

It is incredibly practical both for companies and cloud providers. You can estimate the budget and negotiate an appropriate deal based on that.

The way cloud providers charge for their services is based on a multi-layered approach. 

This includes: 

  • the scope of services (user-based and time-based), 
  • the configuration of the platform, 
  • related expenses (for example, cloud migration, etc).

Let’s look at how pricing models are organized on the three big cloud providers.

AWS Pricing

Amazon AWS is known for its borderline incomprehensible pricing policies. While on the surface it all seems clear and distinct, by the time you get to the actual price estimation, things get complicated. 

In order to keep things transparent, Amazon provides a cost calculator that includes numerous variables. Because of the sheer number of options, it is really hard to estimate anything in a realistic manner, so the actual prices may be drastically different.  

One of the viable solutions for this issue is the use of third-party cost management tools like Trigger or Hubstaff. 

Azure Pricing

Microsoft Azure’s pricing structure is similarly complicated, albeit more clearly defined. The complexity comes from the numerous software licensing options, variations of configuration and special offers. 

Just like AWS, the effective use of Azure will require a third-party cost management tool to keep things intact.

Google Pricing

Unlike AWS and Azure, Google Cloud keeps things as transparent and accessible as possible. It almost seems like Google Cloud pricing policies were designed in spite of Azure and AWS approaches. 

GCP prices for similar computing and storage services are significantly lower than AWS and Azure. In addition to that, Google provides a wide array of various discounts for their services. It goes as far as contract negotiations, which are considerably more flexible than with AWS and Azure who prefer a more cookie-cutter-styled contract format. 

Now let’s look at the features of AWS, Azure, and Google Cloud and how they compare to one another.

Compute Services

Amazon AWS 

  • The main AWS compute asset is Elastic Compute Cloud aka EC2. It is a kind of swiss army knife with different cloud features. One of its main advantages is the immense flexibility of configuration. You can shape it in any way you need it. 
  • Then there are computational instances. AWS provides a wide array of bare metal instances, GPU instances, high-performance computing, auto-scaling, et al. Basically, anything you might need in your cloud infrastructure
  • Elastic Beanstalk provides efficient autoscaling features for web and mobile applications.
  • The other big AWS asset is container services. Docker, Kubernetes, and Fargate provide automatic server management that relieves administrators of pesky headaches. 
  • In addition to that, AWS offers a virtual private cloud option titled Lightsail. It might be a good choice in the case of implementing hybrid cloud infrastructure.

Microsoft Azure

  • The main advantage of Microsoft Azure over GCP and AWS are Virtual Machines – an emulation of the computer system comprised of disparate parts. 
  • Similar to AWS Elastic Beanstalk, Azure’s Virtual Machine Scale Set provides everything the system needs to maintain a proper level of scalability. 
  • Virtual Machines are compatible with the majority of commonly used services and applications (including Windows Server, SQL Server, Oracle, IBM, SAP, and more).
  • Similar to AWS, Azure provides a large variety of functional instances for GPU and high-performance computing. The other big thing is Artificial Intelligence and Machine Learning features.
  • Then there is an exclusive distributed systems platform for applications with a microservice architecture called Service Fabric. It allows streamlining of the application structure while keeping its performance high and reliable.

Google Cloud Platform

  • GCP primary service, Compute Engine has custom and predefined machine types with per-second billing. This may come in handy when you need to perform specific workload-intense operations. 
  • Since Google was involved in the development of Kubernetes, there is a wide array of Kubernetes container features. 
  • The other good thing about GCP is that there are automatic discounts if you are using the service a lot. That comes in handy a lot. 
  • GCP Container Registry 

Read also: Cloud Orchestration vs. Cloud Automation

Cloud Storage: AWS vs Azure vs Google 

Amazon AWS 

  • AWS provides data storage for different purposes: 
  • Simple Storage Service (S3) – object storage;
  • Elastic Block Storage (EBS) – persistent block storage; 
  • Elastic File System (EFS) – file storage. 
  • There is a feature called Storage Gateway. It is used to create a hybrid storage environment
  • Then there is Snowball, a piece of hardware that the company can use to transfer immense quantities of data (petabytes and so on) in those cases when the internet transfer is simply not efficient enough. 
  • AWS databases include: 
  • Aurora for SQL-compatible databases. 
  • Relational Database Service (RDS), 
  • DynamoDB NoSQL database, 
  • ElastiCache in-memory data store, 
  • Redshift data warehouse, 
  • Neptune graph database 
  • Database Migration Service. 
  • For archival storage, AWS provides Glacier service. 
  • Storage Gateway can be used to easily set up backup and archive processes.

Azure Storage Services

Microsoft Azure Storage features are tailor-made for intrinsic manipulations and large-scale operations. 

  • Highlights of Azure storage services include: 
  • Blob Storage for REST-based unstructured data object storages, 
  • Queue Storage for large-volume workloads, 
  • Basic File Storage and Disk Storage. 
  • Data Lake Store for big data applications. 
  • Data Warehouse service, 
  • SQL-based options are wide and compatible with numerous integrations:
  • SQL Database
  • Database for MySQL 
  • Database for PostgreSQL. 
  • Cosmos DB 
  • Table Storage for NoSQL with Redis Cache as in-memory service 
  • Server Stretch Database is its hybrid storage service designed specifically for organizations that use Microsoft SQL Server in their own data centers. 
  • Unlike AWS, Microsoft does offer an actual Backup service, as well as Site Recovery service and Archive Storage.

Google Cloud Storage

  • Unlike Azure and AWS, Google Cloud provides Unified Storage.
  • Similarly to AWS Snowball, there is a Transfer Appliance for extra-sized data transmissions. 
  • GCP databases include SQL-based Cloud SQL, Cloud Spanner which is a relational database for mission-critical workloads. There also NoSQL solutions Cloud Bigtable and Cloud Datastore. 
  • No backup and archive services. You will need to use third-party solutions.

Cloud Tools

Looking ahead, experts say that emerging technologies like artificial intelligence, machine learning, the Internet of Things (IoT) and serverless computing will become key points of differentiation for cloud vendors. All three leading vendors have begun experimenting with offerings in these areas and are likely to expand their services in the coming year.

Amazon AWS

Amazon AWS has so many different features to offer it is easy to get lost. Among the highlights are services like:

  • Sagemaker for training and deployment of machine learning models;
  • Lex conversational interface (the one used in Alexa);
  • Greengrass IoT messaging service for edge computing and data analytics;
  • Lambda serverless computing service.
  • DeepLens framework for optical character recognition and image/object recognition. 

Microsoft Azure 

Microsoft Azure is great at all sorts of AI Machine Learnings operations. There are numerous tools that make machine learning model training nice and easy while retaining a decent performance.

Let’s look at the most prominent:

  • Bing Web Search API, 
  • Text Analytics API, 
  • Face API, 
  • Computer Vision API 
  • Custom Vision Service. 
  • Then there are IoT features, including management and analytics

The other good thing is Azure’s serverless computing service Functions. While from a technical standpoint it is similar to the ones on AWS and GCP, the availability of other features on Azure cloud can make it a very versatile and efficient tool.

Google Cloud Platform

Like Azure, Google Cloud also prominently features various Artificial Intelligence and Machine Learning operations. In fact, of all the Big Three of Cloud, Google has the most versatile AI Machine Learning tools of the bunch. While they are less polished than the ones from Microsoft, they provide more room for experimentation and innovation. 

The key ML component of Google Cloud is TensorFlow – a swiss army knife of all machine learning libraries (if you want to know more about Machine Learning tools – here’s an article). 

Google Cloud provides many powerful APIs for natural language processing, machine translation, computer vision, and speech recognition.

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The choice of cloud provider mostly depends on the business and technical requirements of a particular company. However, the way Cloud Providers position themselves tells a lot about their target audiences.

Here’s how it is laid down:

  • Azure is a good option for companies that use a lot of Microsoft products and have a need for reliable and effective cloud solutions.
  • AWS provides the broadest selection of different services and has the biggest reach with its data center all over the world. However, its pricing policies move it towards enterprise-level companies that look for versatile and expansive solutions. 
  • In comparison with AWS and Azure, Google Cloud seems almost grassroots. Because of its incredibly flexible pricing models, and a solid service package, Google Cloud is a perfect solution for companies that rely on web-based products, and need simple and efficient grounds for their operation.

Read also:

Cloud Migration Strategy


Cloud Elasticity vs. Scalability: Main Differences To Know About

Cloud computing is a kind of infinite pool of possibilities. In one way or another – anything is possible with cloud computing in the mix. This technology makes everything more convenient and less troublesome. Cloud computing helps development teams in solving the following issues:

  • Storing a massive amount of data 
  • Training machine learning algorithms 
  • Constructing a practical business framework 
  • Automating and orchestrate the routines
  • Handling large workloads

Cloud elasticity and scalability are amongst the integral elements of cloud computing. Despite its widespread use, there is a lot of confusion regarding what is doing what and how exactly. This article will explain what the difference between scalability and elasticity in cloud computing

What is Cloud Elasticity?

cloud elasticity example


Сloud elasticity is a system’s ability to manage available resources according to the current workload requirements dynamically. 

This is a vital feature of a system infrastructure. It comes in handy when the system is expected to experience sudden spikes of user activity and, as a result, a drastic increase in workload demand. 

Thanks to the pay-per-use pricing model of modern cloud platforms, cloud elasticity is a cost-effective solution for businesses with a dynamic workload like streaming services or e-commerce marketplaces. 

Various seasonal events (like Christmas, Black Friday) and other engagement triggers (like when HBO’s Chernobyl spiked an interest in nuclear-related products) cause spikes in customer activity. These volatile ebbs and flows of workload require flexible resource management to handle the operation consistently. 

Usages of this cloud infrastructure functionality include:

Streaming Services. Netflix is dropping a new season of Mindhunter. The notification triggers many users to get on the service and watch or upload the episodes. Resource-wise, it is an activity spike that requires swift resource allocation. Thanks to elasticity, Netflix can spin up multiple clusters dynamically to address different kinds of workloads.

netflix cloud elasticity architecture

[Netflix architecture leverages the elasticity of the cloud to scale up and down, source]

E-commerce applications. Amazon has a Prime Day event with many special offers, sell-offs, promotions, and discounts. It attracts a massive amount of customers to the service who are doing different activities. Actions include searching for products, bidding, buying stuff, writing reviews, rating products. This diverse activity requires a very flexible system that can allocate resources to one sector without dragging down others. 

amazon cloud elasticity example

[Amazon infrastructure event management, source]

What is Cloud Scalability?

cloud scalability


System scalability is the system’s infrastructure to scale for handling growing workload requirements while retaining a consistent performance adequately. 

Unlike elasticity, which is more of makeshift resource allocation – cloud scalability is a part of infrastructure design. 

Scalability is one of the prominent features of cloud computing. In the past, a system’s scalability relied on the company’s hardware, and thus, was severely limited in resources. With the adoption of cloud computing, scalability has become much more available and more effective.  

Automatic scaling opened up numerous possibilities for implementing big data machine learning models and data analytics to the fold. Overall, Cloud Scalability covers expected and predictable workload demands and handles rapid and unpredictable changes in operation scale. The pay-as-you-expand pricing model makes the preparation of the infrastructure and its spending budget in the long term without too much strain.

There are several types of cloud scaling:

  • Vertical scale, e.g., Scale-Up – can handle an increasing workload by adding resources to the existing infrastructure. It is a short term solution to cover immediate needs.
  • Horizontal scale, e.g., Scale-Out – expands the existing infrastructure with new elements to tackle more significant workload requirements. It is a long term solution aimed to cover present and future resource demands with room for expansion.
  • Diagonal scale is a more flexible solution that combines adding and removing resources according to the current workload requirements. It is the most cost-effective scalability solution by far.  

Scalability is an essential factor for a business whose demand for more resources is increasing slowly and predictably. 

Examples of cloud scalability include: 

Call Centers. The typical call center is continuously growing. New employees need more resources to handle an increasing number of customer requests gradually, and new features are introduced to the system (like sentiment analysis, embedded analytics, etc.). In this case, cloud scalability is used to keep the system’s resources as consistent and efficient as possible over an extended time and growth. 

cloud scalability call center example

[Application architecture for call centers with cloud scalability, source]

Chatbots are another example of cloud scalability in action. Advanced chatbots with Natural language processing that leverage model training and optimization, which demand increasing capacity. The system starts on a particular scale, and its resources and needs require room for gradual improvement as it is being used. The database expands, and the operating inventory becomes much more intricate.

nlp chatbots cloud scalability example

Consequently, cloud scalability is integral for cloud-based services such as:

  • Infrastructure-as-a-Service (IaaS) – Amazon EC2 or Google Compute Engine
  • Platform-as-a-Service (PaaS) – Magento Commerce Cloud or AWS Elastic Beanstalk
  • Storage-as-a-Service (STaaS) – Google Drive, Microsoft OneDrive, and the likes
  • Data-as-a-Service (DaaS) – customer relationship platforms like Salesforce and Hubspot, ERP applications
  • Database-as-a-Service (DBaaS) – AWS SimpleDB, Rackspace, Oracle, MongoDB

What is the difference between Elasticity and Scalability?

In the grand scheme of things, cloud elasticity and cloud scalability are two parts of the whole. Both of them are related to handling the system’s workload and resources.  

The fundamental concept of the two is adaptability. It refers to the system environment’s ability to use as many resources as required.

The difference between elasticity vs. scalability lies in their functions: 

  • Cloud Elasticity is a tactical resource allocation operation. It provides the necessary resources and capacity required for the current task and handles varying loads for short periods. For example, running a sentiment analysis algorithm, doing database backups, or just taking on user traffic surges on a website. 
  • Cloud Scalability is a strategic resource allocation operation. Scalability handles the scaling of resources according to the system’s workload demands. 

Advantages of Cloud Elasticity and Scalability

Both features occur behind the scenes and make the system workflow smooth and seamless. As you can see, it is similar to the “think global – act locally” approach of social activists. The main benefits of elasticity and scalability are the following:


Cloud scalability and elasticity features constitute an effective resource management strategy:

  • The pay-per-use model is the best solutions for sudden surges of workload demand (vital for streaming services and marketplaces)
  • The pay-as-you-expand model allows to plan out gradual capacity growth of the infrastructure in sync with growing requirements (convenient for ad tech systems)

Consistent performance

Elasticity and scalability features operate resources in a way that keeps the system’s performance smooth, both for operators and customers.

Service availability

Scalability enables stable growth of the system, while elasticity tackles immediate resource demands.

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Elasticity vs. scalability in cloud computing: The final word

Modern business operations live on consistent performance and instant service availability. 

Cloud scalability and elasticity handle these two business aspects in equal measure. 

  • Cloud scalability is an effective solution for businesses whose needs and workload requirements are increasing slowly and predictably. 
  • Cloud elasticity is a cost-effective solution for organizations with dynamic and unpredictable resource demands. 

These features make scalability and elasticity a viable instrument for the company to hold its ground, grow steadily, and gain a competitive advantage.

Related articles: 




Edge Computing Explained with Examples

The emergence of IoT devices, self-driving cars, and the likes, opened the floodgates of various user data. IoT devices brought-in so much data that even seemingly boundless computing capabilities of the cloud were not enough to maintain an instantaneous process and timely results. This is bad news in the case of data-reliant devices such as self-driving cars. 

Hopefully, there is a workaround solution – edge computing.

In this article, we will explain: 

  • What edge computing is?
  • The most prominent examples of edge computing;
  • Benefits and challenges of implementing edge computing applications.

What is Edge Computing?

“Edge computing” is a type of distributed architecture in which data processing occurs close to the source of data, i.e., at the “edge” of the system. This approach reduces the need to bounce data back and forth between the cloud and device while maintaining consistent performance. 

With regards to infrastructure, edge computing is a network of local micro data centers for storage and processing purposes. At the same time, the central data center oversees the proceedings and gets valuable insights into the local data processing.

The term “edge” originates from the network diagrams. In it, “edge” is a point at which traffic comes in and goes out of the system. Since its location is at the edges of the diagram – its name reflects this fact.  

Edge Computing vs Cloud Computing: What’s the difference?

Edge computing is a kind of expansion of cloud computing architecture – an optimized solution for decentralized infrastructure. 

The main difference between cloud and edge computing is in the mode of infrastructure. 

  • Cloud is centralized.
  • Edge is decentralized.

The edge computing framework’s purpose is to be an efficient workaround for the high workload data processing and transmissions that are prone to cause significant system bottlenecks. 

  • Since applications and data are closer to the source, the turnaround is quicker, and the system performance is better.

The critical requirement for the implementation of edge computing data processing is the time-sensitivity of data. Here’s what it means:

  • When data is required for the proper functioning of the device (such as self-driving cars, drones, et al.);
  • When information stream is a requirement for proper data analysis and related activities (such as virtual assistants and wearable IoT devices);

The time-sensitivity factor has formed two significant approaches to edge computing:

  • Point of origin processing – when data processing happens within the IoT device itself (for example, as in self-driving cars);
  • Intermediary server processing – when data processing is going through a nearby local server (as with virtual assistants). 

In addition to that, there is “non-time-sensitive” data required for all sorts of data analysis and storage that can be sent straight to the cloud-like any other type of data.

The intermediary server method is also used for remote/branch office configurations when the target user base is geographically diverse (in other words – all over the place). 

  • In this case, the intermediary server replicates cloud services on the spot, and thus keeps performance consistent and maintains the high performance of the data processing sequence.

Why edge computing matter?

There are several reasons for the growing adoption of edge computing:

  • The increasing use of mobile computing and “the internet of things” devices; 
  • The decreasing cost of hardware.
  • Internet of Things devices requires a high response time and considerable bandwidth for proper operation. 
  • Cloud computing is centralized. Transmitting and processing massive quantities of raw data puts a significant load on the network’s bandwidth. 
  • In addition to this, the constant movement of large quantities of data back and forth is beyond reasonable cost-effectiveness. 
  • On the other hand, processing data on the spot, and then sending valuable data to the center, is a far more efficient solution.

Some edge computing examples

Voice Assistants

Voice assistant conversational interfaces are probably the most prominent example of edge computing at the consumer level. The most prominent examples of this type are Apple Siri, Google Assistant, Amazon Dot Echo, and the likes. 

  • These applications combine voice recognition and process automation algorithms. 
  • Both processes rely on data processing on the spot for initial proceedings (i.e. decode the request) and connection to the center to further refinement of the model (i.e. send results of the operation).

Self-driving cars 

At the moment, Tesla is one of the leading players in the autonomous vehicle market. The other automotive industry giants like Chrystler and BMW are also trying their hand at self-driving cars. In addition to this, Uber and Lyft are testing autonomous driving systems as a service.

  • Self-driving cars process numerous streams of data: road conditions, car conditions, driving, and so on. 
  • This data is then worked over by a mesh of different machine learning algorithms. This process requires rapid-fire data processing to gain situational awareness. Edge computing provides a self-driving car with this.


Healthcare is one of those industries that takes the most out of emerging technologies. Mobile edge computing is no different. 

Internet-of-things devices are extremely helpful when it comes to such healthcare data science tasks as patient monitoring and general health management. In addition to organizer features, it is able to check the heart and caloric rates. 

  • Wearable IoT devices such as smartwatches are capable of monitoring the user’s state of health and even save lives on occasions if necessary. Apple smartwatch is one of the most prominent examples of a versatile wearable IoT. 
  • IoT operation combines data processing on the spot (for initial proceedings) and subsequently on the cloud (for analytical purposes). 

Retail & eCommerce

Retail and eCommerce applies various edge computing applications (like geolocation beacons) to improve and refine customer experience and gather more ground-level business intelligence. 

Edge computing enables streamlined data gathering. 

  • The raw data stream is sorted out on the spot (transactions, shopping patterns, etc);
  • Known patterns like “toothbrushes and toothpaste being bought together” then go to the central cloud and further optimize the system.

As a result, the data analysis is more focused, which makes for more efficient service personalization and, furthermore, thorough analytics regarding supply, demand, and overall customer satisfaction. 

Here’s how different companies apply edge computing:

  • Amazon is operating worldwide. As such, the system needs to be distributed regionally in order to balance out the workload. Because of that, Amazon is using intermediary servers to increase the speed of processing efficiency of the service on the spot.
  • Walmart is using edge computing to process payments at the stores. This enables a much faster customer turnaround with lesser chances of getting into a bottleneck at the counter. 
  • The target applies edge computing analytics to manage their supply chain. This contributes to their ability: 
  • to react quickly to changes in product demand; 
  • to offer customers different tiers of discounts, depending on the situation;

Benefits and challenges of edge computing

Edge computing Benefits

The benefits of edge computing form five categories:

  1. Speed – edge computing allows processing data on the spot or at a local data center, thus reducing latency. As a result, data processing is faster than it would be when the data is ping-ponged to the cloud and back.
  2. Security. There is a fair share of concerns regarding the security of IoT (more on that later). However, there is an upside too. The thing is – standard cloud architecture is centralized. This feature makes it vulnerable for DDoS and other troubles (check out our article on cloud security threats to know more). At the same time, edge computing spreads storage, processing, and related applications on devices and local data centers. This layout neutralizes the disruption of the whole network.  
  3. Scalability – a combination of local data centers and dedicated devices can expand computational resources and enable more consistent performance. At the same time, this expansion doesn’t strain the bandwidth of the central network.
  4. Versatility – edge computing enables the gathering of vast amounts of diverse valuable data. Edge computing handles raw data and allows the device service. In addition to this, the central network can receive data already prepared for further machine learning or data analysis. 
  5. Reliability – with the operation proceedings occurring close to the user, the system is less dependent on the state of the central network. 

Edge computing challenges

Edge computing brings much-needed efficiency to IoT data processing. This aspect helps to maintain its timely and consistent performance. 

However, there are also a couple of challenging issues that come with the good stuff.

Overall, five key challenges come with the implementation of edge computing applications. Let’s take a closer look:

  1. Network bandwidth – the traditional resource allocation scheme provides higher bandwidth for data centers, while endpoints receive the lower end. With the implementation of edge computing, these dynamics shift drastically as edge data processing requires significant bandwidth for proper workflow. The challenge is to maintain the balance between the two while maintaining high performance.
  2. Geolocation – edge computing increases the role of the area in the data processing. To maintain proper workload and deliver consistent results, companies need to have a presence in local data centers. 
  3. Security. Centralized cloud infrastructure enables unified security protocols. On the contrary, edge computing requires enforcing these protocols for remote servers, while security footprint and traffic patterns are harder to analyze.
  4. Data Loss Protection and Backups. Centralized cloud infrastructure allows the integration of a system-wide data loss protection system. The decentralized infrastructure of edge computing requires additional monitoring and management systems to handle data from the edge. 
  5. The edge computing framework requires a different approach to data storage and access management. While centralized infrastructure allows unified rules, in the case of edge computing, you need to keep an eye on every “edge” point.

In conclusion

The adoption of cloud computing brought data analytics to a new level. The interconnectivity of the cloud enabled a more thorough approach to capturing and analyzing data. 

With edge computing, things have become even more efficient. As a result, the quality of business operations has become higher.

Edge computing is a viable solution for data-driven operations that require lightning-fast results and a high level of flexibility, depending on the current state of things.

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