Azure Functions Vs AWS Lambda Vs Google Cloud Functions

 

Serverless computing has been gaining popularity. According to the report, it is the fastest growing extended cloud service, at 75% rate year over year. And so led to more curiosity and the use of serverless architecture. The days of storing information over an in-house database are far gone. Serverless is one of the trending technologies today. There are some serverless platforms to store data, some of these are, AWS Lambda, Google Cloud Functions and Microsoft azure. Selecting the best platform depends on factors like performance, functionality, pricing etc. Because of the availability of lots of platforms, you should know the clear difference between those so let us compare AWS Lambda vs Azure Functions vs Google Cloud Functions.

Azure Functions Vs AWS Lambda Vs Google Cloud Functions-

1. Language Support & Deployment-

Azure Functions–

Azure provides 2 levels of language support- Generally Available (GA) level and Preview level. It supports C#, JavaScript, Python, Powershell, F#, Java version 8 and 11, and Typescript based on the runtime versions 1.x, 2.x and 3.x. Azure also supports direct deployment from .zip file packages. It promotes deployment through FTP, Cloud sync, Local Git, JSON templates and Continuous deployment. 

AWS Lambda–

AWS Lambda provides native support for Golang, Java, PowerShell, Node.js, C#, Python and Ruby. It’s runtime API allows the use of any programming language to develop customized functions. Simple, lambda supports various languages by creating container images through the said Runtime APIs. Simply creating .zip file archives of functional code and dependencies, eases the deployment of Lambda package. Integrating Amazon Storage Services, you can deploy libraries other than AWS SDK with Lambda.

Google Cloud Functions–

Google Cloud Functions eases writing in Node.js, Golang, Java, Python, and .NET frameworks like C#, F#, and Visual Basic. In cloud functions, runtime plays an important role as it does in AWS Lambda. The main difference is that the environment executions of Cloud Functions vary based on the runtime model chosen. Packages from cloud functions are deployed from Cloud Source Repos, Local machine, source control and APIs.

2. Persistent Storage-

Azure Functions–

With advanced programming, handling storage gets a little overwhelming with Azure, but it has upgraded the storage services. One can get about 40 TB to 500 TB of offline data storage transfer through its cloud solution Data Box.

AWS Lambda–

AWS Lambda sets limits for computing and persistent storage resources. This method is applied to all functional layers, deployment packages, container images, execution processes, test events among others. By default, it provides 1k concurrency executions, 75 GB storage for functions and layer storage. In spite of the limit, the storage quota is expanded to a few terabytes upon submitting a manual request to AWS Console support for functional scaling. Lambda provides storage options as Amazon’s S3, EFS, and DynamoDB.

Google Cloud Functions–

Google cloud functions provides simple control over the storage through their in-house Cloud Storage while it also offers the possibility to use Firebase. 

3. Dependencies Management-

Azure Functions–

Azure employs an extensible application performance management service to measure, control and manage dependencies. The SDK of APM services permits azure to automatically track dependency calls for Http/Https, WCF, SQL, Cosmos DB etc.

AWS Lambda–

Lambda’s execution environment has various libraries like AWS SDK that includes new packages and functions. Code organization and use of Dependency injections methods and Inversion of Control(IoC) frameworks like Dagger, Guice, Spring reduces the complexity of dependencies.

Google Cloud Functions–

Monitoring, handling and accessing dependencies in Google Cloud functions depends on the language chosen and the runtime module integrated.  Whereas, with cloud functions, you could request and include any system package to handle the dependency via Go modules’ execution environment conveniently. 

4. Identity & Access Management-

Identity & Access Management system enables authorization layer to exercise fine-grained access management for functions. With Aws Lambda you can create your own custom IAM policies and assigns specific roles as per the user behavior. IAM of lambda has 3 policies: Role, Read-Only Access, Full-Access. Whereas, for IAM computing services, Lambda allows only partial access to service-linkedin roles and doesn’t give authorization-based tags. One can control your function policies through Resource Based Access Control. Now it provides three essential services, IAM for cloud and hybrid-based environment, Consumer Cloud IAM, and Virtual Machines for effective IAM.

GCF provides enterprise-grade level access control and it uses Google’s cloud resources to create security policy according to your requirement and compliance processes. Also, it offers a granular level of IAM via context-aware access, protecting attributes in each resource and IP address with continuous update on security status.

5. Scalability & Availability-

Azure Functions–

Azure function is available with two different plans- Consumption plan and App service plan. Consumption plan scales your function automatically when a function execution times out after a configurable interval. App service plan runs functions on a dedicated VMs allotted to each function app, that implies the host of functions are always up and running.

AWS Lambda–

AWS Lambda supports dynamic scalability according to the increased traffic. To effectively manage traffic increase, AWS Lambda predetermines the number of functions to be carried out according to the region on which it is executed. Near about 500 to 3K functions executed per region.

Google Cloud Functions–

With Google Functions, the background functions scale gradually, and it relies upon the function duration. Likewise, most extreme adaptability depends on as far as possible. Maximum scalability is based on the traffic limits.

6. Concurrency And Execution Time-

Concurrent execution count varies according to the type of event source you have used. Based on the incoming request rate, functions scale automatically, but not all resources in your application’s architecture work the similar way. So concurrency depends on downstream resources.

Azure Functions–

Azure supports various functions simultaneously provided operations take place simultaneously within a single data partition. The number of simultaneous activity and executions is covered at 10X depending on the number of cores in the VM level. The execution time limit is 600 seconds or 10 minutes.

AWS Lambda–

AWS Lambda limits the total concurrent executions across the functions to 1000. One can limits the concurrency in two ways: account level or individual functional level. Functional execution timeout is 900 seconds or 15 min.

Google Cloud Functions–

Google cloud function receives only upto 80 concurrency executions and the execution time between 60seconds and 540 seconds. 

7. Logging And Monitoring-

Azure Functions–

Azure has in-built Azure Application insights which monitors functions. It is generally replaced by built-in lagging system.

AWS Lambda–

AWS lambda monitors functions by reporting metrics through Amazon CloudWatch which includes various requests, latency per request and number of requests resulting in an error. It integrates with Amazon CloudWatch Logs and pushes them from code to a CloudWatch group with Lambda function. One can make use of AWS X-Ray to provide end-to-end monitoring for functions. 

Google Cloud Functions–

Google Stackdriver is a suite of monitoring tools which helps you to understand what’s happening in the Cloud Functions. It has in-built tools for logging, reporting errors and monitoring. One can see execution counts, execution times and memory usage in GCP Console also.

AWS Vs GCP- Secret Tips To Choose For Your Business

How to select the best cloud computing service provider?

Cloud computing services replaced the hardware technology as Mobile phones replaced the alarm clock. They are organizations that provide Information technology (IT) services over the internet. In a small amount of time, this technology took control over the market as businesses favored remote access to data. These cloud technologies enable multi-user systems and remote operation. 

They can be a greener technology that avoids unnecessary hardware for storage. Many service providers are available who provide cloud technology as a service. Let us find the most appropriate cloud solution provider for your business. First of all study the types of cloud computing service provider.

Types of Cloud Computing Service Provider-

There are three types of cloud models or service capability and they are: SaaS, IaaS, PaaS. Let us see one by one.

Also know- SaaS vs PaaS vs IaaS- Know the difference!

1. SaaS-

Software as a Service, also known as cloud application services. It represents the most commonly used option for businesses in the cloud market. SaaS uses the internet to deliver applications, which are managed by a third-party vendor, to its users. Most of the SaaS applications run directly through your web browser. This means they do not require any downloads or installations on the client side. 

2. IaaS-

Cloud infrastructure services also known as Infrastructure as a Service (IaaS). These are made of highly scalable and automated compute resources. IaaS is completely self-administration for accessing and also monitoring computers, networking, storage, and other services. It enables organizations to purchase resources on-demand and as-needed instead of having to buy hardware outright.

3.PaaS-

Cloud platform services, also known as Platform as a Service (PaaS). It provides cloud components to certain software while being used mainly for applications. PaaS delivers a framework for developers that is used to build and create customized applications. All servers, storage, and networking are managed by the enterprise or a third-party provider while the developers can maintain management of the applications.

Different Cloud Computing Service Providers-

1. Amazon Web Service (AWS) – 

The Amazon cloud system is the fastest and trusted by numerous businesses. They are popular for their security system. Scalability and flexibility are the significant advantages of the Amazon cloud. It provides a foundational element service that can be used for creating and also deploying any cloud software. It is the first cloud to reach the Computer room. AWS is the most widely recognized cloud solution.

2. Microsoft Azure-

Read more at- http://provider.news/cloud8-src_blogger

Microsoft Azure IoT hub vs AWS IoT

The enormous cloud duo have entered the Internet of Things (IoT) space. Each one is offering new services and investing in their IoT platforms. The smart cities will have a strong effective future in which IoT and analytics plays an important role in both private and public services. Let us compare Microsoft Azure IoT hub vs AWS IoT.

Microsoft Azure IoT vs AWS IoT

What is IoT?

IoT is an abbreviation of Internet of Things. It refers to the continuous growing network of physical objects that feature an IP address for internet connectivity. It also feature the communication that occurs between these objects and other Internet-enabled devices and systems. Microsoft Azure IoT and AWS IoT are the most famous IoT services among all. IoT has a potential to manage lighting, parking, waste management. Smart city revolution is about connecting people, process, data and things through technology to enable more efficient, smarter living. IoT has potential to develop cities with technology.

Microsoft Azure IoT-

Microsoft’s cloud solution is Microsoft Azure. It offers a substantial number of cloud services, including mobile app services, storage offerings, messaging platforms and virtual machines. It enables not only reliable but also secure bi – directional communication between millions of IoT devices and a solution back end. Azure Con 2015 launched Azure IoT on Sept 29, 2015. It offers plenty of products targeted for use in IoT product development. According to price, Azure IoT is cheaper than AWS IoT platforms.

Features of Azure IoT-

Azure IoT Hub –

IoT Hub also supports numerous open- source SDKs, including .NET, Javascript, Java, C and Python. IoT Hub is the focal territory for developers to develop IoT applications and to connect, monitor and also to analyze. It can manage billions of edge devices with the help of bi – directional messaging. It is a flexible cloud platform as a service (PaaS) that supports multiple protocols (AMPQ, MQTT and HTTP). If a device does not support any one of these protocols then incoming and outgoing traffic can be adjusted using the Azure IoT protocol gateway.

Some of the central features include the use of authentication control per device, bidirectional communication between IoT devices using device- to- cloud telemetry data, and the ability to accelerate the deployment and scaling of IoT services by registering and provisioning devices using zero touch. It allows developers to pack their cloud workloads and run them locally on devices using Azure IoT edge. According to prize, it is grouped into 4 categories including free tiers which has up to the high throughput S3 tier, capable of  supporting up to 300,000,000 messages per day. Each tier added by extra units for more throughput. Messages are sent in 4 KB blocks, for billing purposes. A message upto max 512 KB is count as each block.

Azure IoT Edge –

IoT Edge stretches cloud intelligence and analytics to edge devices at a local level. It expands the deployment of AI, Azure services, and custom logic directly to cross-platform IoT devices. With AWS, the IoT when deployed via Azure IoT Edge can run securely and efficiently even when the devices are offline. IoT Edge has extremely low latency and near real-time response potential, thereby optimizing performance between edge and cloud. It works with the AI Toolkit for Azure IoT Edge. This means that, complex AI and ML tasks such as image recognition and complex event processing can easily run without the need to write them in-house. One of the biggest risks for both customers and enterprises is to secure the data. Hence, Azure has consistently been at the forefront of cyber-security as compared to AWS.

Azure IoT central-

Azure IoT Central is built on the enterprise- grade Azure services that allow integration with existing business processes and systems. It is a software-as-a-service (SaaS) for IoT. And also is a fully managed solution. This means is that, the users without any coding or development skills can build production-grade IoT applications. It is very quick to deploy production-grade IoT applications and ensures to eliminate the need for management of infrastructure and hiring developers with specific skills.This makes Azure IoT Central one of the best platforms for IoT development. It is operated with an interface that allows developers to connect, manage and control access to millions of connected products remotely across their entire life-cycle.

Azure IoT Solution Accelerators –

It is a customizable set of templates for common IoT scenarios such as remote monitoring, a connected factory and predictive maintenance. There is a connected device simulation accelerator, which allows developers to test- run their chosen IoT solution with tens of thousands of simulated devices over the development cycle.

AWS IoT-

Amazon Web Services (AWS) is the public cloud leader and it has been putting a lot of effort into cloud computing, artificial intelligence (AI) and IoT for a long time now. It provides IoT services through its AWS IoT platform, which comes with a broad set of tools. It offers many solutions that span edge to the cloud, allow developers for most of the use cases over a wide range of devices. These solutions consist of local data collection and analysis, devices and cloud services to store and analyze device data.

The AWS IoT stage is more costly than its adversaries, however in general, AWS is essentially in ahead of the others in terms of market share of the cloud computing space. Devices can communicate with applications running in the cloud over HTTP, MQTT and also WebSockets. This communication can secured with the use of TLS. Device specific SDKs are available for Embedded C, JavaScript, Python, iOS, Android and Arduino Yun. Device shadow is a device that connects with AWS. It maintains the identity and last known state of a specific device and acts as a channel to send and receive messages.

AWS IoT Services-

Amazon FreeRTOS –

Amazon FreeRTOS is a microcontroller operating system. This focuses on allowing easy programming and management of small, low-power edge devices. It is based on the FreeRTOS kernel, an open source operating system for microcontrollers. AWS’ service extends the free version with software libraries which allows easy connections between small and low-power devices (such as appliances, fitness trackers and automobiles) with AWS’ wider set of cloud services, such as AWS IoT Core or to other bigger edge devices running AWS Greengrass.

Microcontrollers (MCUs) are single chips consisting of a simple processor. They have limited memory capacity and compute power and so they can only perform simple, functional tasks. Generally microcontrollers can not run on operating systems with built in functionality to connect to the cloud or local networks. This makes IoT services more challenging. Amazon FreeRTOS was built to solve this problem by allowing to connect the core operating system with software libraries. It makes a secure connection to the cloud easy.

AWS Greengrass –

It is software that securely allows the running of local compute, data caching and messaging for connected devices. Connected devices can run AWS Lambda functions via AWS Greengrass. It also enables the keeping of device data in synchronized and securely communicating with other devices even though not connected to the internet.  This means that Greengrass connected IoT devices can still respond quickly to local events, interact with local resources (via Lambda functions), stay updated and minimize the costs associated with transmitting IoT data to the cloud. Devices can then act locally on the data they are generating in addition to using the cloud for backup storage, management and analytics.

AWS IoT Core –

It is AWS’ overseen cloud stage that allows the clear and secure interaction between connected devices, cloud applications and other devices, the thought being to permit the simple structure of IoT applications, for example, associated home or industrial solutions. Also, IoT Core has the functionality to support billions of devices and trillions of messages. This also includes the processing and routing of those messages to AWS endpoints and other devices. IoT Core also connects to AWS IoT Analytics, which empowers the building of analytics and machine learning models.

Final Words-

The IoT market will grow over the years, and demand for IoT developers and IoT development services will also increase. There are various IoT technologies and platforms are available to build IoT projects. Therefore, it becomes complicated for developers and enterprises to select the best one. So we have compared the AWS IoT and Azure IoT to make things easier.

If you’re interested in migrating to the cloud, then we’re more than happy to help you get started. Solace is there to help you for cloud platform. We believe in benefits of using Microsoft azure and AWS IoT. We will provide the means to bring your company the success it deserves. Contact us for incorporating azure or AWS in a business.

AWS vs Firebase – Is It Even a Fair Fight?

When you choose to go through serverless route, you will have two options- AWS or Firebase. Which one will you choose ? Here we will compare AWS vs Firebase. First of all, 

What is Serverless architecture?

A traditional server has a single server which performs multiple responsibilities in the database. This single server performs different functions such as authentication, writing files, keeping track of users etc. When a request is strike to the server, it executes some processes and gives back a response.

Serverless architecture is a microservice architecture. Whenever an event occurs, a code will get executed. The server runs when something happen. The code will come to the active state to execute when server receives a request. It is not persistent. It uses resources only when there is a need. The main advantage of this is, every function has its responsibility,  none of it will overlap and thus it makes things cleaner and less complex.

AWS vs Firebase-

Amazon Web Service-

Amazon Web Services is a global cloud computing platform provided by Amazon. It provides combination of Infrastructure as a service(IaaS),  platform as a service(PaaS) and packaged software as a service(SaaS)offering. AWS has many services all together and it is like a solution getting from a single step. It is easy to set up. And it provides good customer service. As it is a subsidiary of Amazon it always maintains its quality. It has a pay per use service which is helpful to minimize the burden on a customer. Sometimes it is complicated for new startups to understand and manage. AWS provides services from multiple data centers all over the world.

Firebase-

Firebase is owned by Google. It is a cloud server and its services are good at starting stage. It is not well developed like AWS.

Good and Bad Points of AWS-

AWS has many different services so it is capable to provide all-in-one solution. AWS Lambda is strong in performance. Cloud solutions are beginner friendly and it makes easy to set up. It provides reliable service and customer support. It has a pay-per-use service, means the costing depends on the traffic you are getting.

AWS requires a slight learning curve so sometimes it could be a difficult choice to recommend. Their framework is additionally equipped towards larger teams. If you have made some mistake, AWS could finish up costing you more cash than you initially foreseen.

Good and Bad Points of Firebase-

Firebase is new as compared to Amazon Web Services, but is more advanced in its technology. It has unique services about image, voice APIs and text. It has a feature for traffic management service. Firebase includes services that makes its scaling easier. It offers low IT costs. If you have a small team to set up, firebase will be the best choice than AWS setup. It doesn’t require skillful learning, thus easy to use for those having less knowledge.

However, Firebase is complicated to query for large datasets. It’s database don’t provide relational data and this may trouble for some new companies. Let us see AWS vs Firebase.

AWS vs Firebase, Which one to choose?

AWS is owned by Amazon whereas Firebase is owned by Google, so both are the products of top companies, so the support and updates provision service is best in both. And they maintain their reputation. Both are best cloud servers and provides good user experience to the users. In short we can conclude that, AWS is suitable for large teams whereas Firebase is suitable for small teams and startups.

When to choose AWS?

• When you know perfectly well that you are going to scale horizontally.
• You need a large storage. Organizations that need to deal with large amount of images or videos or things like this.
• You know that you need to start and stop services as demand increases and decreases.

When to choose Firebase?

• You need less storage.
• New startups.
• Dealing with less data.

If you’re interested in migrating to the cloud, then we’re more than happy to help you get started. Solace is there to help you for cloud platform. We believe in benefits of using Firebase and AWS. We will provide the means to bring your company the success it deserves. Contact us for using Firebase or AWS in development.

Three Modern Technologies Software Stacks: Firebase vs. AWS vs. MongoDB

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Technology is progressively updating day by day. So, modernizing software to be more cloud based can be the best solution to keep ahead in the industry. For that, you have two options, you can choose a way of using individual components like building blocks or you should choose a more turnkey, all in one solution. You can use a mix of two, likewise a hybrid cloud. There are three backend solutions that are top choices of developers- Google’s Firebase, Amazon Web Services(AWS), MongoDB. Also, all these three have different domains, different functionality and scale. To choose the best one among them, let us see, Firebase vs. AWS vs. MongoDB .

Firebase vs. AWS vs. MongoDB-

1. Firebase-

Firebase is a ready- to- use mobile and web application development solution by Google. It is a cloud server. Also, its services are good at starting stage. It is not well developed like AWS. Firebase allows developers to quickly spin up mobile app prototypes from scratch. Similarly, its extra features allows developers to grow and improve apps over time.

Features of Firebase-

• App messaging and push notifications.
• Insights and Analytics.
• It supports IOT.
• Real time database design and file storage.
• Server Management.
• Scaling and also Load balancing as demand increases.

Pros and Cons of Firebase-

Pros-

1. Ready-made API
2. Realtime data
3. Static File hosting
4. File storage with support of Google
5. traffic management service
6. Treat data as streams to build highly scalable applications

Cons-

1. Traditional relational data models are not applicable to NoSQL; therefore, your SQL chops will not transfer.
2. Limited query abilities due to data stream model
3. No on-premise installation

If you have a small team to set up, firebase will be the best choice than AWS setup. It doesn’t require skillful learning, so it is easy to use for those having less knowledge.

2. Amazon Web Services-

Amazon Web Services is a global cloud computing platform provided by Amazon. It provides combination of Infrastructure as a service(IaaS),  platform as a service(PaaS) and also packaged software as a service(SaaS) offering. Because of, it is a subsidiary of Amazon, it always maintains its quality. Instead of creating their own infrastructure to support internal systems and applications, high traffic businesses with more requirements can host their apps or sites with AWS, with extensive list of computing services.

Features and functions of AWS-

• Serverless components by AWS Lambda
• Sending transactional emails to a large list, with Amazon’s SES
• Storage and file sharing with Simple Storage Service(S3)
• Deploy software containers with Docker, then run and manage those containers with Amazon’s EC2 Container Service(ECS) and EC2 Container Registry (ECR).

Pros and Cons of AWS-

Pros-

1. Global reach and scalability
2. Eliminating Capacity Constraints & Mitigating Cost
3. Easy to use
4. No capacity limits
5. Provides speed and Agility
6. Secure and Reliable

Cons-

1. Requires technical support fee
2. General cloud computing issues
3. Requires slight learning curve

You can choose AWS, when you need to deal with large volumes of data,want to add a CDN to your system, or create a shared file system internally.

3. MongoDB-

It is a document – oriented NoSQL database for unstructured data and real-time apps and analytics. MongoDB is compatible with .NET applications, Java Platform and even more. It is used for high volume data storage. Also, MongoDB has built – in features that makes it best for businesses that need fast and flexible access to their data. It is well suited for the cloud due to its horizontal scalability.

Features and Functionality-

• Dynamic schemas
• Location-based data analytics and operations
• Flexible
• Document based
• Free to use
• MongoDB is a distributed database at its core, so high availability.

Pros and Cons of MongoDB-

Pros-

1. Flexible Database
2. Sharding
3. High speed
4. High Availability- Replication
5. Ad-hoc query support
6. Easy environment support
7. Full Technical support

Cons-

1. High memory usage
2. Limited listing
3. Limited data size
4. Complex transaction

You can use MongoDB for mobile and social networking sites, real time analytics and high speed logging, caching and high speed scalability. Also, it can be the best choice for e-commerce product catalog.

Conclusion of Firebase vs. AWS vs. MongoDB-

Keep in mind, all these are developed to perform a different back-end function. Also, these 3 have different pros and cons based on their functionalities. Hence, the selection is depends on requirements and integration needed before migrating or committing to a solution.

Please contact us if you have any Web Development projects. We have got plenty of experience in Web and mobile application Development service.

AWS Services and Cloud Computing

Cloud Computing storage is a future for the IT business industry and why AWS services stood as valuable choice for the customer’s users. Amazon Web Services (AWS) started to offer IT services to the market in the form of web services, which is nowadays known as cloud computing. Taking every database and evolving data mining has made the secure nature of cloud computing. The idea of utility computing is that like electricity you can plug into the circuit and it operates on-demand and regardless of geography. On top of that, companies can pay for the services as they use them, which can be cheaper than signing a big, up-front contract; companies simply calculate what they need in the way of storage, cloud capability and content delivery with options including security, backup, DNS, database, storage, load-balancing, workflow, monitoring, networking and messaging. Since every company has started to adopt the cloud computing in one way or the other, and AWS services being a major player in the cloud services industry, it makes sense to learn about AWS Cloud services.

The idea of AWS services is that you should only provide what you have to do at any given time. This, of course, is the opposite of what happens with so many companies now which tend to provide for the worst case scenario. However, it’s worth noting that, as with many cloud services, Amazon is more cost-effective for spiky or one-off workloads whereas if you’re running workloads where demand is predictable day-to-day it may be cheaper to run it in-house.
Four terms that show why AWS services is reliable customers choice:

• On and Off. This is where you may just have to spin up for a one-off workload like month-end.
• Variable workload to build an infrastructure that knows what a particular business looks like.
• Fast growth. Start small but be ready to build capacity quickly.
• Predictable peaks and high perks.

Amazon Web Services (AWS) delivers a set of services that form a reliable, scalable, and inexpensive computing platform solution. AWS services users improve the performance and end-user experience of their business-critical applications. Businesses also no longer have to scramble to backup files or move items into the archive to make space for new projects when a physical server nears capacity. Instead, computing power and data storage are easily adjusted via a web dashboard from AWS services.

3 terms show why using Amazon Web Services (AWS) as your cloud computing service can ensure your IT infrastructure remains robust and secure.

Reliable capacity

Eliminate guessing on your infrastructure capacity needs. When you make a capacity decision prior to deploying an application, you often end up either sitting on expensive idle resources or dealing with limited capacity. With cloud computing, these problems go away. You can access as much or as little capacity as you need, and scale up and down as required with only a few minutes’ notice.

High speed and agility

In a cloud computing environment, new IT resources are only a click away, which means that you reduce the time to make those resources available to your developers from weeks to just minutes. This results in a dramatic increase in agility for the Org.

Fast Deployment

Easily deploy your application in multiple regions around the world with just a few clicks. This means you can provide lower latency and a better experience for your customers at minimal cost.

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