Docker on CentOS 8

My main use case for Docker on CentOS  is that it can be very useful for QA and performance testing because it gives the team a consistent and repeatable environment. One scenario where I would use it is running automated API or UI test suites inside Docker  containers on a CentOS-based server. Instead of depending on each engineer or local setup, we can define the test environment with a Dockerfile and Docker  Compose, including the test framework, browsers, dependencies, environment variables, and reporting tools.

I decided to use Docker on CentOS  for my testing environments because, from a performance perspective, Docker helps reduce setup time and improve test execution consistency. For example, we can run tests in parallel containers, isolate services, and compare results more reliably between local, staging, and CI environments. However, it is important to monitor CPU, memory, network usage, container startup time, and disk input and output because poor configuration can create false performance issues that are not related to the application itself.

The best features Docker on CentOS offers include stability, repeatability, automation, and performance control. I think that for testing teams, Docker makes it easier to package the full QA environment, run test suites in parallel, control dependencies, and reproduce performance results. It integrates well with CI/CD pipelines and allows teams to scale test execution without manually configuring each CentOS  server. My main recommendation is to use and maintain CentOS  stream versions, define resource limits, avoid running containers as root when possible, and include logs, reports, and monitoring as part of the implementation.

Automation and performance control specifically have helped my team mainly by making execution more predictable, repeatable, and easier to scale. For example, in a recent project, we needed to run automated regression tests against multiple environments. Before using Docker on CentOS, every machine or server had small differences, such as different node versions, browser versions, drivers, dependencies, or missing packages. That created false failures, wasting time debugging the environment instead of the application. By moving the test execution into Docker containers and CentOS, we packaged the full test environment: framework, dependencies, browser configuration, test script, reporting tool, and environment variables. This made the automation much more stable, so every execution used the same baseline. An example of a challenge it solved was an unstable regression execution where tests were failing randomly because the host machine was under heavy load, especially when several suites were running at the same time. After containerizing the execution, separating services, and monitoring resource usage, we gained better visibility into bottlenecks, enabling us to identify when a container needed more memory, when parallel execution was too aggressive, or when the application response time was actually slow. The main benefit was that Docker on CentOS gave us a controlled testing layer. Automation became easier to maintain, performance results became more trustworthy, and at the end of the day, the team spent less time fixing environment issues and more time improving test coverage and product quality.

Docker on CentOS has positively impacted my organization because I know that many projects are using Docker on CentOS. The impact is positive because it provides us with a more stable and repeatable way to run automation, testing, and supporting services. One of the biggest benefits has been environment consistency. Before using Docker on CentOS, different servers or local machines could have various versions of Node.js, Java, browsers, drivers, or system packages, causing false test failures and making debugging slow. With Docker on CentOS, we were able to package the required dependencies into images, so our execution was the same.

I mentioned many benefits, but I have noticed some areas for improvement.

Some needed improvements include clearer installation and version compatibility. Docker's official documentation currently lists maintained CentOS Stream  9 and 10 as supported for Docker Engine. Teams using older CentOS versions need to be careful with compatibility and support planning, so clearer migration guidance for older CentOS versions would be beneficial. Another area is in troubleshooting; it could be made easier. When Docker fails due to networking permissions, CI, Linux storage driver, or daemon configuration, the error messages can be too technical. A guided diagnostic tool for CentOS would be very useful, checking repositories, kernel compatibility, firewall rules, the overlay, Docker daemon status, and container resource usage. Additionally, performance visibility could be improved as Docker already provides resource control, but for QA and performance testing, better built-in dashboards for CPU, memory, disk, input/output, network latency, container startup time, and test execution would help in understanding performance issues. Security defaults could be stronger and easier to apply because features like rootless mode are available, but clearer recommendations and simpler setup flows for running containers with least privilege, managing secrets, scanning images, and avoiding risky volume permissions are needed. Lastly, container integration and delivery in QA could be better documented, as Docker works well with automation pipelines, but more official examples for CentOS-based Jenkins , GitHub Actions , self-hosted runners, GitLab , browser testing, API testing, and performance testing documentation would help QA teams adopt it faster.

I have around ten and a half years of experience in my current field.

Docker on CentOS is stable, which is why we chose it, as it requires fewer resources than virtual machines.

Docker on CentOS is highly scalable for our use cases. It allows us to move from a single machine or manually configured test execution to a more flexible model where we can run multiple containers in parallel, isolate workloads, and scale tests based on available CPU or memory.

From our side, customer support has been great, especially when we encountered some issues.

Before choosing Docker on CentOS, we evaluated options including virtual machines and manually configured setups.

Other options we evaluated included Jenkins , manually configured CI/CD agents, and manual Kubernetes .

For our private or hybrid deployments, we use various cloud providers, including AWS , Google Cloud , and Azure . My experience with pricing, setup cost, and licensing has generally been positive. The initial setup cost was more related to infrastructure, configuration, and monitoring. Once the Docker images and pipelines were standardized, the long-term costs became easier to manage because we spent less time fixing environment issues and more time executing tests. From a licensing perspective, Docker Engine on Linux is licensed under Apache, while Docker Desktop has a separate subscription. Overall, I would say that the pricing and licensing experience is reasonable, but it requires good governance to clarify the distinction between server-side Docker Engine usage and Docker Desktop usage, track who needs paid subscriptions, and include cloud compute, storage, networking, image registry, and monitoring costs.

We did not purchase Docker on CentOS through the AWS Marketplace . In our case, Docker was deployed on our VMs and CentOS-based servers or cloud virtual machines, mainly as part of our internal infrastructure. Before using Docker on CentOS, we mainly relied on more traditional setups, such as dedicated virtual machines, manually configured Jenkins agents, and local development or QA environments with dependencies installed on the host machine. The primary reason we switched to Docker on CentOS was to reduce environment inconsistency. Different servers could have various versions, which led to issues during execution. Docker on CentOS provided us with a more repeatable and controlled execution environment.

For example, in one project, we saw several positive and measurable outcomes, especially around test execution stability and environment consistency. One major improvement was reducing the time spent preparing and fixing test environments. Before Docker on CentOS, a lot of time was lost because different machines had different versions of Node.js. After containerizing the test execution, the setup became much more predictable because the same Docker image was used across local, staging, and CI environments. In terms of outcomes, we saw improvements such as less troubleshooting time, faster onboarding, more stable automation, better parallel execution, and cleaner CI/CD execution logs. We now have logs, reports, and artifacts that are easier to collect from each container. A practical example is the regression testing, where previously, when a test failed, the team often had to check whether the problem came from the application, the server, the browser version, or the local configuration. With Docker on CentOS, we had a standardized execution layer that reduced false failures and made debugging faster. From a performance control perspective, Docker on CentOS also helped avoid overloading the host server by defining memory and CPU limits, allowing us to better understand whether a slow execution was caused by the application, the test framework, or the infrastructure. This made performance results more reliable.

My experience with pricing, setup cost, and licensing has generally been positive. The initial setup cost was more related to infrastructure, configuration, and monitoring. Once the Docker images and pipelines were standardized, the long-term costs became easier to manage because we spent less time fixing environment issues and more time executing tests. From a licensing perspective, Docker Engine on Linux is licensed under Apache, while Docker Desktop has a separate subscription. Overall, I would say that the pricing and licensing experience is reasonable, but it requires good governance to clarify the distinction between server-side Docker Engine usage and Docker Desktop usage, track who needs paid subscriptions, and include cloud compute, storage, networking, image registry, and monitoring costs.

Before choosing Docker on CentOS, we evaluated options including virtual machines and manually configured setups.

Other options we evaluated included Jenkins, manually configured CI/CD agents, and manual Kubernetes .

I have some additional suggestions regarding my main use case or how Docker on CentOS fits into my testing workflows. My main suggestion for implementation is to use official Docker repositories and maintain CentOS version. Docker currently documents CentOS Stream  9 and 10 as supported for Docker Engine, while CentOS Linux 7 reaches end of life on June 30, 2024. Therefore, I would avoid using old CentOS versions for new implementation to avoid issues.

I have one suggestion for teams implementing Docker on CentOS. It may be best to start with a simple Docker image for the test framework, then add Docker Compose if multiple services are needed. After that, I recommend defining the CPU and memory limits, collecting logs and reports from each container, and integrating the execution into the CI/CD pipeline because that workflow creates a clean and scalable foundation for both automation and performance.

My advice for others looking into using Docker on CentOS is to start simply but implement it with good governance from day one. Docker can bring significant value, but only if the team standardizes how images contain logs, resources, and security are managed. The most important recommendation is to use a supported CentOS version, standardize your images, integrate with CI/CD early, control CPU and memory usage, monitor container metrics, and think about security from the beginning.

Regarding Docker on CentOS's AI capabilities, I think governance and security are critical. Docker on CentOS can serve as a strong foundation for AI workloads because it provides isolated, repeatable, and scalable environments. However, AI use cases usually involve sensitive data, dependency models, credentials, and automated decision-making, so organizations need strong controls around image creation, access permissions, secrets, and vulnerability scanning. From a governance perspective, I recommend clear standards for approved base images, image versioning, access control, and audit logs. From a security perspective, I suggest running containers with less privilege, avoiding root execution when possible, scanning images, generating S-BOMs, and keeping the CentOS host updated. Docker supports rootless mode to reduce risks from the Docker daemon and container runtime, while Docker Scout can analyze images using S-BOMs and vulnerability data. It is also essential to be cautious with CentOS version support, as Docker Engine documentation currently lists maintained CentOS Stream 9 and 10 as supported. Using outdated CentOS versions creates governance and security risks. Overall, I view Docker on CentOS as a positive foundation for AI and automation workloads, provided it is implemented with strong governance, controlled images, secure measures, vulnerability scanning, resource limits, and clear ownership of the container.

Regarding Docker on CentOS's AI capabilities, I see accuracy and reliability as different aspects. Docker on CentOS itself does not make an AI model more accurate; accuracy depends on the model, training data, prompts, configuration, and validation process. However, Docker on CentOS can strongly improve the reliability and repeatability of the output by providing a controlled environment where the same model, dependencies, libraries, and resource limits are used each time. In an AI testing or automation scenario, Docker on CentOS can help ensure that the same model version, Python libraries, CUDA, or CPU configuration, and environment variables are used across local, staging, and CI environments, reducing inconsistent behavior caused by dependency differences. Additionally, the Docker model runner also supports managing and running AI models locally, configuring model parameters, and displaying prompt response details, which can help with traceability and repeatable validation. From a QA perspective, I would not trust AI output solely because it runs in Docker on CentOS. I would still recommend automation, automated validation, expected output checks, prompt versioning, model versioning, logs, and human review for critical cases, along with monitoring for hallucination or unstable responses. Overall, I would say that Docker on CentOS is reliable as an execution platform for AI workloads, especially when properly configured, but the accuracy of the AI output must be measured separately through testing benchmarks and business validation. Since Docker Engine officially supports maintained CentOS Stream 9 and 10, I would also avoid outdated CentOS versions for AI workloads that require strong reliability and security.

My main use case for Docker on CentOS  is building a four-tier project on my PC.

I use Docker on CentOS  by installing Docker  to manage the Docker  files and also to manage my applications, websites, and MySQL  from CentOS .

The best features Docker on CentOS offers in my experience are its speed and smooth operation, along with the fact that there is no need to add a repository, and it is free. I can use the repository to download any repository, which is why I use those features. CentOS  is free, and I have used it to practice for my exams and to build my four-tier project.

I chose a seven out of ten because Docker on CentOS is very fast and smooth. However, it also needs to improve its security, upgrade the packages, and fix bugs, which is why I deducted three points. It should also provide more updatable features.

Regarding Docker on CentOS's AI capabilities, if I am using it for a banking project, I think we need higher security to prevent hacking and direct attacks on servers. That is why we need to upgrade security on CentOS 9 and develop CentOS 10, an upgraded version, for more feature support and ease of use.

I think it would be very helpful to bring in AI to know more about CentOS 9 and the hidden features it offers.

I have been using Docker on CentOS for the past two years.

Docker on CentOS has positively impacted me by allowing me to upgrade to CentOS 9 to build more security and also manage subscriptions, which sometimes are free but not for organizations. I need to keep the subscription to access more packages and features in the subscription manager, as they do not always provide everything for free.

Docker on CentOS is deployed in my organization using both private and public clouds, as we normally use CentOS 9 for the UAT servers and proxy servers. We are using AWS  and Azure  for our public and private cloud deployments. I purchased Docker on CentOS through the AWS Marketplace .

I recommend that others looking into using Docker on CentOS consider that I have also recommended CentOS 9 to my colleagues for learning for their exams at no cost to build their skills.

It is important to build on CentOS and to bring in new versions, such as CentOS 9 and CentOS 10, for higher capabilities and features. I would rate Docker on CentOS overall as a seven out of ten.

My main use case for Docker on CentOS  is containerizing microservices in local environments on CentOS  and then deploying them to the cloud. I essentially use Docker  to package the application with all its dependencies, ensuring that what works locally works the same in production without the typical environment differences.

One of the most concrete cases of how I containerized an application with Docker on CentOS  was the containerization of the client controller scenery that I developed at NT Comunicaciones. It was an application built in React on the front-end, Node.js on the back-end, and MySQL  and Firebase as the database.

Complementing the above, another relevant case was in the context of CapRover on a CentOS  VPS at Saltamontes Records Creative, where I used Docker  Swarm to orchestrate multiple application containers from different clients.

I can share concrete results I experienced directly in the projects. I reduced deployment times at NT Comunicaciones. Before containerization, a manual deployment including environment preparation, dependency installations, and validations could take between two and three hours. With Docker and the automated pipeline using Jenkins  and GitHub Actions , that time drops to minutes. The image is already built and validated. Deployment was simply a matter of downloading the image and starting the container.

Another outcome is infrastructure consolidation and cost reductions. With Docker Swarm and CentOS, I consolidated multiple client applications onto a single VPS that previously required separate servers, resulting in a direct reduction in monthly infrastructure costs because we went from paying for multiple instances to optimizing resource usage on a single, well-sized server. Additionally, there was a reduction of post-deployment incidents and on-team adoptions.

The most concrete impacts regarding the reduction in post-deployment incidents are three. First, deployment times dropped from two to three hours to minutes by eliminating manual preparation of the environment. Second, I consolidated multiple applications on a single VPS with Docker Swarm, directly reducing the monthly infrastructure costs. Third, incidents due to environment differences between development and production practically disappeared, which reduced my post-deployment troubleshooting time and allowed me to focus on pipeline improvements.

The best features Docker on CentOS offers are true portability between environments, process insulation, resource efficiencies compared to VMs, and seamless pipeline integrations in the foundation for scaling.

I can be quite specific on both points because I experienced them firsthand. On portability, it accelerates deployments. On efficiency, it reduces infrastructure costs. The clearest example was at CapRover on CentOS VPS. Before containerization, each client application required its own server or at least its own VM, which multiplied costs. With Docker Swarm, I consolidated several applications onto the same VPS. The impact on the team was that the development team gained confidence in deployments because the environment was no longer a variable. There were no more surprises in production due to configuration differences. This reduced the time I spent troubleshooting post-deployment and allowed me to focus on pipeline improvements instead of putting out fires.

From my experience, there are a few areas where things got tricky working with Docker on CentOS: SE Linux conflicts, other networking configurations, storage driver compatibility, and deployment tools.

I can go deeper on both. On networking, a concrete case at NT Comunicaciones involved a situation where after installing Docker on CentOS, the containers could communicate internally but could not reach external services. The issue was that Docker creates its own iptables and rules, but firewalld was overwriting them on every reload. Every time firewalld restarted, container connectivity broke silently. The fix was to configure Docker to work alongside firewalld properly and set specific zones to allow Docker bridge traffic.

It took me a while to diagnose because the error was not obvious; containers appeared to be running fine, but network calls were just timing out. On storage, a concrete case with device-mapper on an older CentOS setup had me hit a situation where the storage pool ran out of space silently. Docker did not throw a clear error. Containers just started behaving unexpectedly, some failing to write logs, others crashing on startups.

The diagnostic was not straightforward because on the surface, it looked like an application issue. Once I identified it was the device-mapper thin pool hitting its limit, I had to extend it manually, which required stopping services temporarily. After that, I migrated to overlay2 with a kernel update, and the storage management became much more transparent and easier to monitor. The common pattern in both cases is that on CentOS, Docker does not fail loudly. Issues with networking and storage tend to manifest as application misbehavior rather than clear infrastructure errors, which makes the troubleshooting cycle longer if you do not know where to look first.

I have been working in my current field for one year.

There are two additional points that I think are relevant to mention based on practical experience. First is container-level security management. In CentOS, especially the integration of Docker on CentOS with SE Linux caught my attention because it adds an extra layer of security at the operating system level. SE Linux controls what each container can do at the kernel level, limiting the impact if a container is compromised. Initially, it created conflicts that had to be resolved with specific policies, but once configured correctly, it gave me greater peace of mind in production environments. The second point is volumes and data persistence.

Docker on CentOS itself does not produce AI output; it is the runtime environment. What I can speak to is how the container environment impacts the accuracy and reliability of the AI workloads running inside it. On reliability, in my experience at Pacifico Seguros running custom agents with a Copilot inside containerized environments, the big gain is consistency. The model or agent always runs in the exact same environment regardless of where the container is deployed. That eliminates a whole category of reliability issues caused by dependency drift or environment differences that could affect how the AI component behaves.

On accuracy being affected by the container, I did notice a challenge around resource constraints. If the container running an AI workload had tight memory or CPU limits, inference times, and in some cases, responses were incomplete or timed out. Tuning the resource limits in the deployment manifest was critical to ensure the AI component had enough headroom to produce reliable output consistently. On observability, another challenge was monitoring what was happening inside the container when the AI agents are executing. I addressed this by integrating Azure Monitor  and Application Insights to capture logs and metrics from inside the container, which gave me visibility into response times, failure rates, and resource consumption patterns of the AI workload.

Based on my experience, I would give three concrete pieces of advice. First, understand CentOS before Docker on CentOS. Second, invest in your pipeline from day one. Third, plan for observability before you hit production. One final thought is that if you are starting fresh today, evaluate whether CentOS is still the right choice given its end-of-life situation. I give this review a rating of seven out of ten.

My main use case for Docker on CentOS  is that I deployed some IBM DB2  database containers.

A specific example of how I use Docker on CentOS  with those IBM DB2  database containers is that if you install DB2 on on-prem virtual machines, it takes a long time, but with Docker , it is very fast and easy to recover.

I find that Docker on CentOS is flexible, scalable, and easy to install.

The best features Docker on CentOS offers in my experience are that it is very easy to maintain.

This helps in my day-to-day work because I can watch if my containers are running, if they have any errors or any need for maintenance. I can see them.

Docker on CentOS has positively impacted my organization as it saved us so much time about database installations.

I estimate that if you install databases on generic virtual machines, it takes approximately 30 minutes, but on Docker , it takes one or two minutes.

I think file size management on Docker on CentOS should be improved.

A specific management feature I wish was better or easier to use is that it should maintain its stored files by itself. It should check if there are so many unused files, and Docker should clean them by itself.

I have been using Docker on CentOS for about three years.

Docker on CentOS is quite stable.

Docker on CentOS is quite scalable. You can scale it for small businesses or large environments. It depends on you. Docker allows you to do that.

I haven't needed any customer support on Docker on CentOS.

I haven't previously used a different solution because Docker helps me with all the jobs I have to do.

My advice to others looking into using Docker on CentOS is that they should read carefully the documentation, and they should have done everything right on first installation.

I have seen a return on investment as I save money and time because I can run many DB2 applications in the same virtual machine, so I don't need any extra machines.

My experience with pricing, setup cost, and licensing is that Docker is free to install, so the pricing was acceptable.

Before choosing Docker on CentOS, I did evaluate other options, but they are more expensive and so complicated to maintain.

I don't have any additional thoughts about Docker on CentOS before we wrap up.

Regarding Docker on CentOS's AI capabilities, I didn't use any AI capabilities of Docker on CentOS.

The AI capabilities are something I haven't explored yet.

I give this review a rating of 8.