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AWS CodeDeploy is a deployment service that automates application deployments to various compute services. My main use cases include automated application deployments to Amazon EC2 instances, on-premises servers, AWS Lambda, and Amazon ECS.

In my projects, I have deployed backend services through AWS CodeDeploy to EC2 instances. For every release and pre-development release planned for production, I use AWS CodeDeploy to deploy to all four AWS EC2 instances, which allows me to achieve the desired output.

The primary use case for AWS CodeDeploy is automatic application deployment, which I consider the best aspect of the service.

AWS CodeDeploy offers several valuable features, including deployment to fleets of EC2 servers, on-premises servers deployed to data center infrastructure, AWS Lambda for serverless function updates, and Amazon ECS for containerized applications.

The feature I use most frequently is deployment to Amazon EC2 instances, where I deploy backend services to EC2 servers, enabling zero-downtime deployment. AWS CodeDeploy supports rolling updates, blue-green deployments, and canary deployments to minimize or eliminate downtime. Additional standout features include consistent deployments across environments, the ability to deploy to development, staging, and production environments, centralized control, integration with CI/CD pipelines, and automatic rollback.

AWS CodeDeploy has positively impacted my organization by significantly reducing deployment times to achieve zero downtime during deployments. Generally, it takes very little time for backend services to be deployed to EC2 instances.

AWS CodeDeploy can be improved by optimizing deployment strategies using blue-green deployments, configuring appropriate minimum health hosts to balance speed with reliability, enhancing the AppSpec file, keeping lifecycle hooks efficient and lightweight, improving monitoring and feedback, speeding up deployments, strengthening CI/CD integrations, and improving reliability while using best security practices.

Deployments can be accelerated much more by reducing artifact size through excluding unnecessary files, using S3 Transfer Acceleration for large deployment packages, pre-installing dependencies in AMIs rather than during deployment, and running deployment scripts in parallel where possible.

I have been using AWS CodeDeploy for the past six years.

AWS CodeDeploy is quite stable.

AWS CodeDeploy's scalability is quite good. I can definitely scale it to a much higher extent whenever required and can downgrade when needed, as its features support scalability with infrastructure.

The customer support has been excellent. I have faced some issues and they were resolved very quickly.

Previously, I was using Azure for deployments, but I switched to AWS CodeDeploy because of the pricing, features, and performance it provides, including improved monitoring and feedback available in AWS CloudWatch.

I have definitely seen a return on investment. The time saved is the main advantage, and I can realize many benefits such as accelerated deployments and reasonable costs overall.

The time saved is the main advantage of AWS CodeDeploy, and the costs have been quite reasonable.

The pricing for AWS CodeDeploy has been quite reasonable. It is better compared to peers like Azure, and the licensing cost and setup cost are both quite good for AWS.

Before choosing AWS CodeDeploy, I evaluated Azure as an alternative. However, AWS CodeDeploy's performance and its features, including best practices for security, reliability, and CI/CD integration as well as deployment speed and strategies, have been quite strong.

My advice to others looking into using AWS CodeDeploy is to use it to maximize capabilities in terms of reducing deployment times. It scales with infrastructure so you can deploy to one instance or thousands without issues.

I have already explained numerous features and aspects of AWS CodeDeploy, including scalability benefits, multi-environment support, concurrent deployments, blue-green deployment at scale, autoscaling integration, and core scalability features. I would rate this product a 9 out of 10.

My main use case for Red Hat Ansible Automation Platform is managing servers through automated deployment, automated management, security patching, and everything in between.

For automating deployments or security patching with Red Hat Ansible Automation Platform, we use it for Docker Swarm clusters. We install Docker and automate Docker deployment inside the Docker Swarm, checking which are the master nodes and which are the server nodes and connecting them to each other. All of these things are maintained by Red Hat Ansible Automation Platform.

I have around 100-200 servers that we need to maintain through security patching. We need to perform sudo apt update or install packages inside them and maintain those servers. For that, we use Red Hat Ansible Automation Platform. We maintain an inventory file and execute everything from there.

The best features that Red Hat Ansible Automation Platform offers is that it does not require any additional resources inside the servers. Python is the only requirement, and since Python is already present inside the servers, we can run it from our location and it automatically deploys things and does the work for us.

The minimal requirements and easy deployment have definitely impacted my daily work and my team's efficiency. Red Hat Ansible Automation Platform is one of the best features that we depend on. We have evaluated other options, but Red Hat Ansible Automation Platform was the best choice because it has saved us a tremendous amount of time. We do not need to manually intervene in the servers or install third-party software to maintain these things.

It is very easy to write playbooks for Red Hat Ansible Automation Platform. Ansible Galaxy contains many playbooks that are readily available and ready to be used. It is highly configurable with Jinja templating, making it easy to maintain.

Red Hat Ansible Automation Platform has positively impacted my organization. Previously, we needed to go into the servers and maintain them manually, which used to take a lot of time. For 200 to 300 servers, the maintenance took about one to two months. New patches would arrive and we would have to repeat the process. Now, it is a one-night work or a 10 to 15 minutes task. We write a playbook, maintain an inventory, and roll out the updates and it starts working for us. Red Hat Ansible Automation Platform uses conditional clauses and has rollback options, functioning like a standard coding language that is simple to use.

There is definitely a reduction in errors with Red Hat Ansible Automation Platform because we have playbooks written with all the necessary clauses and rollback options. Manual work automatically creates more errors, whereas in automation, we have written sets that we do not forget every time we run it. We have protected written sets that we execute consistently.

Red Hat Ansible Automation Platform could benefit from improvements in certain areas. Red Hat Tower is available for enhanced functionality, though it operates on a paid model. I found that acceptable.

Otherwise, Red Hat Ansible Automation Platform is very easy to use with Jinja templating capabilities that make it really straightforward. I do not believe there is anything that could be improved with Red Hat Ansible Automation Platform that we have not already discussed.

I have been using Red Hat Ansible Automation Platform for the past eight years.

In my experience, Red Hat Ansible Automation Platform is very stable.

Red Hat Ansible Automation Platform is highly scalable. We are currently using 200 servers and we are adding more servers each day. Red Hat Ansible Automation Platform is easily maintainable and highly scalable.

We are not using Ansible Tower for customer support, so we do not receive official support. However, Red Hat Ansible Automation Platform has a large community with many written blogs and resources available, making it easy to maintain.

My experience with pricing, setup cost, and licensing for Red Hat Ansible Automation Platform was very simple. There is no pricing and no licensing required, as Red Hat Ansible Automation Platform is always free to use. If you do not use Ansible Tower, then it is definitely free and easy to maintain. Writing playbooks is just like writing a simple Bash script or writing code with clauses and everything.

I have seen a return on investment with Red Hat Ansible Automation Platform. We need fewer employees and have saved significant time. The work that previously took two months now takes 10 to 15 minutes. Red Hat Ansible Automation Platform is automated, so we do not need as many employees to perform manual work. We have fewer employees, which also results in cost savings.

My experience with pricing, setup cost, and licensing for Red Hat Ansible Automation Platform was very simple. There is no pricing and no licensing required, as Red Hat Ansible Automation Platform is always free to use. If you do not use Ansible Tower, then it is definitely free.

Before choosing Red Hat Ansible Automation Platform, I evaluated other options such as Chef and Puppet, but I did not use them.

The advice I would give to others looking into using Red Hat Ansible Automation Platform is that it is one of the best Ansible management tools available for servers. You should start using it. Red Hat Ansible Automation Platform offers Jinja templating where you can write your own templates and NGINX configurations. It is very simple to use and you do not need any extra server or additional infrastructure. I give this product a rating of 9 out of 10.