The challenge that we faced with Codefresh is that it is a complex setup. Kubernetes and Docker are somewhat complex. However, the cluster needs many computer resources to have Kubernetes and Docker running in the pipelines. Additionally, some design decisions made us move away from Codefresh to another vendor for pipelines. When we had Codefresh, it was a very nice solution to have, but partly, we have moved to another vendor for pipelines. The improvements needed for Codefresh include making it easier to create the clusters. Also, navigating to Docker-within-Docker and Docker-within-Kubernetes needs to be easy and well-documented. It is a very nice tool, but management has decided to save on some costs and follow the process.
Codefresh has a learning curve for teams, as the initial pipeline configuration may require some familiarity with YAML and container-based CI/CD processes; a junior engineer cannot configure these YAML files and processes, so it needs an experienced or knowledgeable person with a background in Kubernetes. The documentation is good, but the person using Codefresh needs to go through a learning curve; they should have knowledge of the things Codefresh offers, so it should be easy to use even for a non-Kubernetes person, and the writing of configuration files should also be easier for them. I gave it a nine because it has automated Kubernetes deployments, which are not easy to achieve through CI/CD, and it is centralized, integrating GitOps, Argo CD, and Docker-based containerized application deployment, making it a useful tool. The reason it is not a ten is because our developers who do not have Kubernetes and Docker knowledge cannot use Codefresh easily, and the configuration file we have to write is very complex, requiring prior knowledge of Kubernetes and Docker-based deployments.
Codefresh is a progressive tool tailored for enhancing DevOps teams, enabling swift deployments with its Kubernetes-native architecture while supporting GitOps control to provide a centralized view of Argo CD runtimes and clusters.Codefresh stands out by integrating real-time application health monitoring, efficient artifact management, and smart deployment strategies. Automation features reduce manual efforts allowing seamless version control integration. While users appreciate its...
The challenge that we faced with Codefresh is that it is a complex setup. Kubernetes and Docker are somewhat complex. However, the cluster needs many computer resources to have Kubernetes and Docker running in the pipelines. Additionally, some design decisions made us move away from Codefresh to another vendor for pipelines. When we had Codefresh, it was a very nice solution to have, but partly, we have moved to another vendor for pipelines. The improvements needed for Codefresh include making it easier to create the clusters. Also, navigating to Docker-within-Docker and Docker-within-Kubernetes needs to be easy and well-documented. It is a very nice tool, but management has decided to save on some costs and follow the process.
Codefresh has a learning curve for teams, as the initial pipeline configuration may require some familiarity with YAML and container-based CI/CD processes; a junior engineer cannot configure these YAML files and processes, so it needs an experienced or knowledgeable person with a background in Kubernetes. The documentation is good, but the person using Codefresh needs to go through a learning curve; they should have knowledge of the things Codefresh offers, so it should be easy to use even for a non-Kubernetes person, and the writing of configuration files should also be easier for them. I gave it a nine because it has automated Kubernetes deployments, which are not easy to achieve through CI/CD, and it is centralized, integrating GitOps, Argo CD, and Docker-based containerized application deployment, making it a useful tool. The reason it is not a ten is because our developers who do not have Kubernetes and Docker knowledge cannot use Codefresh easily, and the configuration file we have to write is very complex, requiring prior knowledge of Kubernetes and Docker-based deployments.