Amazon Elastic Compute Cloud (EC2)

Amazon Elastic Compute Cloud (EC2) are being used for our production, sandbox, and development ERP environment, PRTG network monitoring and Veeam and Commvault backup solutions. It addresses the application performance issue comparing to other Cloud service provider which despite being configured with higher CPU and RAM resources still underperform Amazon Elastic Compute Cloud (EC2). Additionally, AWS regularly releases new Amazon Elastic Compute Cloud (EC2) with higher performance but at a lower cost.

Suitable for companies that are looking for performance at a competitive price, flexibility to switch instance type even with RI, flexibility to add-on IOPS, option to lower running cost with the regular introduction of new instance type that comes with higher performance but at a lower cost.

Our organization uses Amazon EC2 to operate highly scalable machines for various machine learning services. We use a fleet of EC2 cluster groups combined with an auto scaling group and reserved / spot instances configuration for high performance.
The service addresses the problems of manual maintenance by automatically scaling machines based on demand and traffic.

Suitable: For running monolithic applications. EC2 offers very good services for those kinds of use cases. Cloud-native applications focused on AWS exclusively can be deployed on EC2.

Not Suitable: Microservices-based applications are not suitable for EC2 as they take up more memory and are slow. Alternative services include using Docker, Kubernetes, etc to deploy lightweight applications.

For hosting our web platform on multiple Amazon Elastic Compute Cloud (EC2) instances with the use of launch templates and auto scaling groups. This helps whenever we get a spike in the requests during peak season to handle the platform. The availability of multiple instances types according to cpu, memory also helps in right fitting of the infrastructure. Also the availability of public AMI with updated packages also removes the installation overhead.

Amazon Elastic Compute Cloud (EC2) instances works well for hosting applications that can be monolithic or microservices. Amazon Elastic Compute Cloud (EC2) are easy to be provisioned with the features of launch templates and auto scaling groups which provides a way to handle load at peaks or spikes on platform. These can also be customized with the use of userdata for running user specific scripts.

We use our EC2 instances to host our backend app. It runs our web server that allows users to interact with our app via a browser. The product helps us deliver/process our user's designs and emails, send out automated emails and notifications, and run periodic security checks to ensure data integrity.

It works well when you work at scale. For example, if you are hosting a web application, having EC2 in auto-scaling groups would make it very easy to handle all traffic volumes, given that the scaling configuration is set up accordingly. It can be used with a load-balancing layer to improve availability.

EC2 allows us to scale our server usage as needed and automatically. There are a ton of price points and options for every need. We host several client sites on EC2 and can depend on it to be reliable and work. Upgrades and downgrades to server sizes are also straightforward.

It is great for large-scale computing needs that need to scale up and down. However, it's not great for running virtual computers for Remote Desktop and similar tasks.

We use EC2 for almost all our operations, they are an excellent option for workloads like Web Servers like Apache and nginx, Application Servers like WebLogic, Java Servers, and even as Database Servers with Oracle and Microsoft SQL Server running on them. There plenty of choices to choose on how the EC2 is so they accommodate almost any software on them.

AWS EC2 is well suited for almost any scenario, you can run any software that is compatible (x86_64, arm, mac), and you have plenty of instances types to choose with little or massive memory, the same for CPU and disk space.
You even can run in a non-virtual EC2, that is a physical server we you have all the resources available to you, perfect for some very strict industries or by some licenses rules.

I can't think of an scenario where EC2 is not appropriate.

We primarily use Amazon Elastic Compute Cloud (EC2) instances for running our Splunk deployments on AWS, Amazon Elastic Compute Cloud (EC2) gives us the flexibility to choose from a wide range of server types at an hourly rate, or for even lower prices with upfront commitment - it allows us to quickly make changes to the instance types we are interested in and perform testing before then making long-term savings by committing to instances for the next 12 months.

Amazon Elastic Compute Cloud (EC2) is particularly good for running monolithic workloads which are not easy to containerise or run on microservices with other AWS services. This includes off-the-shelf applications from vendors who supply closed-source software and expect a full server for installation. There are other benefits depending on instance types which are an advantage over function-as-a-service type resource, such as direct attached fast storage, high memory, network or graphics requirements.

We use EC2 to rapidly and efficiently scale up or down as needed for our customers while still providing a secure and dependable environment. It enables us to swiftly supply new instances and adjust resource allocation as necessary.We also use EC2 to provide high-performance computing, process data, and do analytics.

Good Scenario : EC2 is a wonderful option when you need to execute a lot of compute-intensive tasks, like producing videos or performing scientific calculations. It is the best option for applications that call for a lot of processing power due to its fast CPUs and potent GPUs.Bad Scenario : The best option for conducting lengthy tasks might not be EC2. Since EC2 instances are made to be quickly spun up and down, they might not be the greatest option for jobs that call for prolonged compute cycles.

I use it for training machine learning models. I use the Deep Learning AMIs available to spin up Amazon Elastic Compute Cloud instances and then use that to train large models.

I use the GPU-based Amazon Elastic Compute Cloud instances, as for training large models good GPUs with high memory are required. I connect the instance to the S3 bucket where I store that data.

It is good for training big models when getting access to GPU is not possible. If you've access to GPU, then training them on the computer makes more sense as it helps in reducing the cost.

Prefer it when you just can't manage on-premise machines as they require a lot of maintenance and get down quite often. Amazon Elastic Compute Cloud takes off that burden from head, i.e. no worry of maintenance and is always ON. Also provide the functionality of scaling up when required.