I find it mandatory to integrate monitoring and logging when using AWS CodeDeploy because it is crucial to have total knowledge of what is happening during the deployment of our code structure to a server or a static resource S3. For this purpose, we use CloudWatch, which is a pre-built option we can select during configuration. This logging and monitoring resource provided by AWS helps developers or DevOps engineers to troubleshoot issues with ease. After deployment, monitoring is also essential as we can check the availability of resources and ensure their health status. Integrating all kinds of monitoring features is mandatory and flexible for developers, DevOps, or anyone involved. One of the positive impacts I have noticed from working with AWS CodeDeploy is that when I recently needed to build a pipeline for iOS apps, it required a macOS server fitting within the company's budget. If it did not fit, we would have had to look for alternatives. AWS CodeDeploy had its own pre-built environment for iOS deployment, which allowed us to choose it over other alternatives as the iOS code build requirements were quite specific. AWS CodeDeploy handles everything, allowing us to simply provide commands in the form of a YAML file, taking care of all the back-end activity for us. I have not used the automatic scaling feature of AWS CodeDeploy myself because my primary focus has been on building applications and pushing them to TestFlight or the Apple Developer account. There has been no need for deployment in this case, just a build and product export. As such, we haven't required any high availability or scaling for our tasks. In terms of pricing and return on investment with AWS CodeDeploy, I reflect on the initial period where we faced numerous errors and troubleshooting challenges, resulting in significant bills. The auditing process was extensive as we had to clarify the reasons for these high costs, which the company had to absorb. For macOS fleets, documentation indicates that a server instance can be reserved for a minimum of 24 hours. This means if a person uses AWS CodeDeploy even once, they reserve that instance for the entire 24 hours and incur charges for that time. Therefore, if daily use occurs, it leads to continual billing. This aspect of pricing created some frustration, and I've participated in many meetings discussing these audits. I have not decreased my personal usage of AWS CodeDeploy, but in the context of my company's budget and environment, my company recently stopped utilizing AWS CodeDeploy. Specifically, this change occurred about a week ago. While it may seem I have stopped using AWS CodeDeploy, I still work hands-on with YAML regularly, utilizing it daily for Kubernetes and other tech stacks. Additionally, I have my own personal AWS account, where I continue to test various services to ensure I remain up-to-date. My overall rating for AWS CodeDeploy is 8 out of 10.
If all of your applications are using AWS Cloud platform, I recommend using CodeDeploy due to its seamless integration with multiple AWS services. It offers scalability and automation, making it easier to manage infrastructure and achieve serverless deployments. I rate CodeDeploy a seven because it is easy to use and integrates smoothly with other services without much configuration effort. The overall product rating would be a 7.
Digital Technology Analyst at a tech services company with 10,001+ employees
MSP
Top 5
Feb 12, 2025
I would recommend CodeDeploy depending on customer needs. If the source code is on AWS CodeCommit, CodeDeploy is suitable. If the code is on GitHub, GitHub Actions would be easier to set up and more integrated. Overall, I rate CodeDeploy a 10.
Systems Lead Developer at Columbia University Medical Center
Real User
Top 20
Jan 20, 2025
Start with the basics. Understand the fundamentals, try it out with a simple task, then expand to more complex tasks. The deployment process to servers is pretty straightforward with AWS. I rate this solution eight out of ten. I use AWS services for deployment.
Find out what your peers are saying about Amazon Web Services (AWS), Microsoft, Octopus Deploy and others in Release Automation. Updated: December 2025.
Our main priority is to follow best practices as much as possible. If we do that, we can achieve our goals with the help of DevOps. For example, we don't keep things hard-coded. Currently, our repository is on AWS CodeCommit. So, we are integrating everything dedicatedly into the AWS environment. We're building our codes with the help of AWS CodeBuild. We are providing all permissions for AWS CodeCommit and AWS CodeBuild. We are deploying those things with the help of AWS CodeDeploy. Recommending the solution to other users depends on their requirements. If someone wants a dedicated AWS environment, they can choose AWS CodeDeploy. Overall, I rate the solution a seven out of ten.
We have done many projects using CodeDeploy and CodePipeline. We deploy code in GitHub by generating and downloading an SSH Key to store the repositories in SSH and deploy the code from the repository. Finally, the repositories are successfully cloned. I only know basic concepts of AWS products. Overall, I rate the solution six to seven out of ten.
I recommend AWS CodeDeploy as it consistently ranks at the top in the market. I've worked with other cloud services like Azure and Google Cloud Platform, but AWS CodeDeploy always stands out with its features. Their continuous improvement of the user interface and documentation is commendable. It has also improved the front-end experience. I rate it a ten out of ten.
I rate CodeDeploy eight out of 10. I would encourage people to use AWS CodeDeploy, but you should consider things like pricing and what you want to achieve.
Release Automation streamlines software deployment, enhancing efficiency by automating repetitive tasks and reducing human error. It facilitates faster release cycles and increases software reliability, crucial for businesses aiming for quick time-to-market.With growing demands for rapid software releases, Release Automation supports continuous integration and delivery, providing a seamless transition from development to production. It reduces bottlenecks by automating manual tasks, offering...
I find it mandatory to integrate monitoring and logging when using AWS CodeDeploy because it is crucial to have total knowledge of what is happening during the deployment of our code structure to a server or a static resource S3. For this purpose, we use CloudWatch, which is a pre-built option we can select during configuration. This logging and monitoring resource provided by AWS helps developers or DevOps engineers to troubleshoot issues with ease. After deployment, monitoring is also essential as we can check the availability of resources and ensure their health status. Integrating all kinds of monitoring features is mandatory and flexible for developers, DevOps, or anyone involved. One of the positive impacts I have noticed from working with AWS CodeDeploy is that when I recently needed to build a pipeline for iOS apps, it required a macOS server fitting within the company's budget. If it did not fit, we would have had to look for alternatives. AWS CodeDeploy had its own pre-built environment for iOS deployment, which allowed us to choose it over other alternatives as the iOS code build requirements were quite specific. AWS CodeDeploy handles everything, allowing us to simply provide commands in the form of a YAML file, taking care of all the back-end activity for us. I have not used the automatic scaling feature of AWS CodeDeploy myself because my primary focus has been on building applications and pushing them to TestFlight or the Apple Developer account. There has been no need for deployment in this case, just a build and product export. As such, we haven't required any high availability or scaling for our tasks. In terms of pricing and return on investment with AWS CodeDeploy, I reflect on the initial period where we faced numerous errors and troubleshooting challenges, resulting in significant bills. The auditing process was extensive as we had to clarify the reasons for these high costs, which the company had to absorb. For macOS fleets, documentation indicates that a server instance can be reserved for a minimum of 24 hours. This means if a person uses AWS CodeDeploy even once, they reserve that instance for the entire 24 hours and incur charges for that time. Therefore, if daily use occurs, it leads to continual billing. This aspect of pricing created some frustration, and I've participated in many meetings discussing these audits. I have not decreased my personal usage of AWS CodeDeploy, but in the context of my company's budget and environment, my company recently stopped utilizing AWS CodeDeploy. Specifically, this change occurred about a week ago. While it may seem I have stopped using AWS CodeDeploy, I still work hands-on with YAML regularly, utilizing it daily for Kubernetes and other tech stacks. Additionally, I have my own personal AWS account, where I continue to test various services to ensure I remain up-to-date. My overall rating for AWS CodeDeploy is 8 out of 10.
If all of your applications are using AWS Cloud platform, I recommend using CodeDeploy due to its seamless integration with multiple AWS services. It offers scalability and automation, making it easier to manage infrastructure and achieve serverless deployments. I rate CodeDeploy a seven because it is easy to use and integrates smoothly with other services without much configuration effort. The overall product rating would be a 7.
I would recommend CodeDeploy depending on customer needs. If the source code is on AWS CodeCommit, CodeDeploy is suitable. If the code is on GitHub, GitHub Actions would be easier to set up and more integrated. Overall, I rate CodeDeploy a 10.
Start with the basics. Understand the fundamentals, try it out with a simple task, then expand to more complex tasks. The deployment process to servers is pretty straightforward with AWS. I rate this solution eight out of ten. I use AWS services for deployment.
I will recommend CodeDeploy easily. Overall, on a scale from one to ten, I would give it an eight.
I would hardly recommend it, maybe fifty percent of the time. Overall, I would rate it a six out of ten.
Our main priority is to follow best practices as much as possible. If we do that, we can achieve our goals with the help of DevOps. For example, we don't keep things hard-coded. Currently, our repository is on AWS CodeCommit. So, we are integrating everything dedicatedly into the AWS environment. We're building our codes with the help of AWS CodeBuild. We are providing all permissions for AWS CodeCommit and AWS CodeBuild. We are deploying those things with the help of AWS CodeDeploy. Recommending the solution to other users depends on their requirements. If someone wants a dedicated AWS environment, they can choose AWS CodeDeploy. Overall, I rate the solution a seven out of ten.
We have done many projects using CodeDeploy and CodePipeline. We deploy code in GitHub by generating and downloading an SSH Key to store the repositories in SSH and deploy the code from the repository. Finally, the repositories are successfully cloned. I only know basic concepts of AWS products. Overall, I rate the solution six to seven out of ten.
I recommend AWS CodeDeploy as it consistently ranks at the top in the market. I've worked with other cloud services like Azure and Google Cloud Platform, but AWS CodeDeploy always stands out with its features. Their continuous improvement of the user interface and documentation is commendable. It has also improved the front-end experience. I rate it a ten out of ten.
I rate CodeDeploy eight out of 10. I would encourage people to use AWS CodeDeploy, but you should consider things like pricing and what you want to achieve.