Platform as a Service in 2021: 9 PaaS Providers You Should Consider
This is a guest article by Gilad David Maayan from AgileSEO
Platform as a Service (PaaS) is taking the world by storm. As more organizations migrate to the cloud, they find that the traditional infrastructure as a service (IaaS) model, while highly flexible, requires an enormous investment in terms of ongoing monitoring, maintenance, and upkeep. Far from the dream of transitioning all operating costs to the cloud provider, organizations are setting up entire departments to manage their cloud infrastructure.
The remedy is PaaS — an all-in-one platform that serves a business need. PaaS solutions, requiring minimal effort to set up, provide value almost immediately. They also provide unique capabilities that would be very difficult to set up in house, such as large-scale database infrastructure, artificial intelligence (AI), and desktop virtualization.
In this article I’ll discuss the PaaS phenomenon and review nine services from leading cloud providers, which can make a major impact for many organizations.
What is PaaS?
The proliferation of cloud computing has been accompanied by a range of “as a service” (aaS) solutions. These offerings are intended to provide fully managed business infrastructure, including IT infrastructure, software, and additional elements such as backup and disaster recovery.
Most cloud offerings are platforms, so it may seem redundant to refer to a product as “platform as a service” (PaaS). However, PaaS solutions perform a specific function, allowing organizations to build, deploy, and maintain their software in the cloud.
Developing in the cloud means companies don’t have to own or manage proprietary infrastructure: They can use the PaaS provider’s infrastructure. PaaS software is designed to perform as well as or better than on-premises equivalents, providing easy accessibility, powerful capabilities, and cost efficiency throughout the software development lifecycle.
Companies that lack the necessary infrastructure to build software or that are expecting to significantly scale their products, can leverage PaaS solutions for a more flexible and cost-effective way to achieve their business objectives.
Some of the central features of PaaS include:
Ready-made backend infrastructure. PaaS solutions can significantly reduce the time it takes to set up software development operations. PaaS provides all the prebuilt infrastructure required, allowing users to jump straight into coding.
Development tools. These include a wide variety of offerings, such as code libraries, text editors, development frameworks, and more. When shopping for a PaaS product, companies should make sure the software can meet their language requirements and is compatible with the technology skills of their development teams.
Development capabilities. PaaS solutions support the development of virtually any type of system, including web applications, mobile applications, big data, AI, and even hardware based solutions like internet of things (IoT) devices.
Management. PaaS software must be able to manage all stages of the development lifecycle. Management capabilities may include tracking, reporting, workflow automation, version control and source code management.
Deployment. Software is only valuable if it can be deployed. PaaS solutions enable companies to deploy the software they developed to the platform they will use to deliver it to users. PaaS management features also allow users to maintain and upgrade the software once deployed.
AI as a Service
What Is AIaaS?
AIaaS or MLaaS stands for Artificial Intelligence (Machine Learning) as a service, which refers to AI solutions offered by an external provider. Artificial Intelligence leverages algorithms that can perform complex cognitive tasks, which have traditionally been reserved for human intelligence. This includes reasoning, learning from experience and finding novel solutions to problems.
AIaaS allows any organization to access AI technology without the need to develop its own AI infrastructure, which would be time-consuming and require extensive resources. Users can take advantage of advanced AI algorithms—both pre-trained, and custom algorithms created through experimentation—through APIs and easy-to-use tools.
1. IBM Watson
IBM Watson offers a range of AI tools to help manage large amounts of data. These include pre-built applications such as Watson Natural Language Understand, used to perform advanced text analysis, and Watson Assistant, used to create virtual assistants.
IBM Watson Studio allows developers to construct, train and deploy machine learning models across the cloud environment of their choosing without requiring machine learning or data science skills.
2. Microsoft Azure Machine Learning (Azure ML)
Azure Machine Learning (Azure ML) enables the building and management of machine learning solutions in the cloud. The service is intended to augment existing technical capabilities such as data processing and model development.
Data scientists and machine learning engineers can use Azure ML to deploy their workloads in the cloud, distribute training across cloud resources, deploy machine learning models to production, and scale them as needed.
3. Google Cloud ML
Google Cloud ML Engine enables the creation and deployment of machine learning solutions. It is Google’s central AI platform, intended for developers and data scientists handling big data. A major advantage of the platform is that it allows you to easily integrate your ML models with the Google Cloud ecosystem.
One of Google’s AI tools is AutoML, which allows you to train custom machine learning models for tasks such as image classification, text analysis, image classification and translation. A what-if tool allows you to visualize your datasets and see how your model functions, while metrics help you assess performance.
Desktop as a Service (DaaS)
What is DaaS?
Desktop as a Service (DaaS) solutions offer a cloud-based remote deployment of virtual desktops. The entire infrastructure is hosted in the cloud, including a console that allows administrators to serve virtual desktops to remote users. There is no need to set up on-premises resources—DaaS is accessible on the cloud, as a subscription service.
DaaS comes with the typical cloud computing benefits—including access through an intuitive interface, quick and easy scaling options, and flexible usage-based payment models. Additionally, DaaS solutions usually come with integrations that enable you to connect with existing tooling. DaaS use cases include enterprises, universities, and more.
4. Amazon WorkSpaces
WorkSpaces is a DaaS solution by Amazon Web Services (AWS), the world’s leading cloud provider. It lets you remotely deploy virtual desktops, which you can build and customize using either Windows or Linux operating systems. You can bring your own licensing (BYOL) to WorkSpaces, provided the license is compatible with a cloud environment.
WorkSpaces offers a wide range of hardware and software configurations. You can choose from several types of bundles, which come with specifications that define the storage, OS, and compute resources of the desktops. Each bundle has its own capabilities, maximum performance, and costs. WorkSpaces is available in multiple AWS regions.
5. Microsoft Azure Windows Virtual Desktop
Windows Virtual Desktop (WVD) is a DaaS solution offered as part of the Microsoft Azure cloud. WVD offers either client-based or server-based solutions, which are available for free for Microsoft 365 Enterprise and Windows 10 Enterprise customers.
WVD provides access to both Windows 7 and Windows 10 operating systems, which you can use to securely deploy remote virtual desktops. The service offers multi-session deployments of Windows 10, based on a version of Windows 10 Enterprise specially designed for the WVD platform. This type of deployment offers the flexibility of virtual desktops with the capabilities of regular Windows operating systems familiar to end users.
6. VMware Horizon Cloud
VMware Horizon Cloud is a DaaS solution that offers server-based and client-based services, which can be accessed via a control plane designed to centralize the deployment and management of virtual desktops. Underlying computing resources can be managed using VMware’s familiar tools such as VMware vSphere.
Horizon Cloud offers several options available for fully-managed desktops. Session desktops enable multiple users to share resources that are available on one server. Dedicated desktops provide each user with persistent resources.
“Floating desktops” provide each user with nonpersistent resources. To provide a consistent experience to floating desktops users, you can use User Environment Manager—a Horizon Cloud feature that enables you to persist user data and settings.
SQL as a Service
What is SQL as a Service (SQLaaS)?
SQL as a service (SQLaaS) is a cloud-hosted database, offered in a pay-per-use or subscription-based model. SQLaaS is an inexpensive and effective alternative to internal databases.
Managing multiple databases in an organization requires a large ongoing effort and special expertise. Achieving high availability is complex and requires specialized equipment, whereas SQLaaS services provide high availability out of the box. Using databases as a service shifts this responsibility to the cloud provider, reducing overhead and improving resilience of database resources.
7. Microsoft Azure SQL Database
Azure SQL Database is a Database as a Service from Microsoft that provides cloud-based databases in the Azure cloud. It is a relational SQL database, hosted in the Azure cloud, which can be used without installing any hardware or software. Azure SQL Database also offers a number of advanced features, including:
- Database resource scaling—this is the most powerful feature, enabling the scaling up or down of database resources
- 18 deployment models—including single database, elastic pool, and managed instance.
- Automatic tuning—an AI-based tool for automatically tuning and fixing performance issues
- Automated backups— to facilitate database management and data storage
- Long-term backup retention—up to 10 years
- Geo-replication—for distributing readable secondary databases across multiple data centers
8. Amazon Relational Database Service (RDS)
Amazon RDS is better suited for users with experience handling data. It is a good option for developers, data scientists and data administrators who are already familiar with AWS.
Databases can be built according to the user’s specific needs. Users have control over the type of database used, where the data is stored, and can write code or create templates. Amazon Relational Database Service supports a range of database formats, including Amazon Aurora, MySQL, PostgreSQL, SQL Server, MariaDB and Oracle Database.
9. Google Cloud SQL
Google Cloud SQL is a fully managed service that supports PostgreSQL and MySQL. It provides strong performance, scalability, and availability with automatic updates and backups. The service is protected from potential failures in the user’s IT infrastructure by automatic failover protection.
Google Cloud SQL encrypts all data and complies with standards such as HIPAA, PCI DSS v3.0, and ISO 27001. The use of Google’s private global network also enhances security.
In this article I reviewed nine PaaS services that can provide immediate value to organizations, in fields that previously required a massive effort to set up and maintain:
- IBM Watson – a huge library of ready-made machine learning and AI algorithms, which you can readily use in your applications.
- Microsoft Azure Machine Learning (Azure ML) – end-to-end management for machine learning models, from experimentation to deployment.
- Google Cloud ML – a machine learning platform based on Google’s powerful and AI-ready infrastructure.
- Amazon WorkSpaces – Amazon’s solution for remote delivery of virtual desktops to employees.
- Microsoft Azure Windows Virtual Desktop – the successor to Microsoft’s popular Remote Desktop Service (RDS), based on a specially-designed version of Windows 10
- VMware Horizon Cloud – VMware’s cloud offering for desktop virtualization at enterprise scale.
- Microsoft Azure SQL Database – the managed version of Microsoft SQL server, providing a huge variety of deployment options for all scenarios.
- Amazon Relational Database Service (RDS) – a managed service that lets you run open source databases like MySQL, and commercial ones like MS SQL and Oracle, easily at any scale.
- Google Cloud SQL – Google’s version of RDS, which is quickly catching up in terms of capabilities and database support.
I hope this will be of help as you explore the exciting world of platform-powered cloud computing.
Gilad David Maayan is a technology writer who has worked with over 150 technology companies including SAP, Samsung NEXT, NetApp and Imperva, producing technical and thought leadership content that elucidates technical solutions for developers and IT leadership.
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