

Find out in this report how the two Build Automation solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
Before Codefresh, we had to plan the strategy, write the configuration file, and run everything; it used to take two to three days to plan and implement, but now it is a one-time job, so it can be done in ten to fifteen minutes.
In terms of dollars, considering both engineering cost and infrastructure cost, I would say the savings are more than at least $10,000 to $15,000.
The AI features that they have and with which we can rewrite the pipeline and troubleshoot issues significantly saved time.
By adopting templates and various different pipelines across our own IDP platform, we have saved upwards of 30 to 40% of development time.
With Harness, the release process decreased from three or four hours to one or two hours, making deployments much quicker.
They actually understand Kubernetes and container architecture, which makes a huge difference.
One of the team members had a few configurations that we suggested to Codefresh, and they took it and applied those configurations within Codefresh's product.
We have rarely faced issues with Harness tech support.
We have not faced any customer support issues, with tickets resolved in less than a four-day SLA.
There was an instance when I faced issues with third-party plugins, and after raising a support ticket, they responded in a few hours with a documentation link that resolved my issue.
Unlike our old Jenkins setup where adding more builds often meant the master node would struggle and we would run out of executors, Codefresh is Kubernetes-native, so it scales horizontally by design.
Codefresh's scalability is 10 out of 10; it is very scalable.
Codefresh handles scalability as my workloads grow by allowing us to implement techniques such as HPA.
Our entire organization uses it with hundreds of applications, and it supports this scale effectively.
It is able to work on our infrastructure side, which is EKS, and we are able to handle our organization growth effectively for an enterprise use case.
When I integrated Harness to more than 20 applications in one place, it becomes less stable.
Codefresh is generally very quick, and the experience is very pleasant and good.
My environment is very secure and stable, and the accuracy needed during a process of AI capabilities does not disappoint me.
In my experience, Codefresh is stable with not many challenges in hiccups or in clusters, but it is somewhat complex.
Harness is completely stable, and we are using it in production without facing any stability issues at all.
We have rarely faced issues with Harness tech support.
Harness is decently stable.
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.
Although the visibility into Kubernetes is excellent, I would love to see out-of-the-box cost optimization metrics.
Some design decisions made us move away from Codefresh to another vendor for pipelines.
There is not a lot of good support for pipeline as code, and I often find myself not using pipeline as code the way other platforms such as GitHub Actions or Jenkins integrate pipeline as code.
Improved documentation and onboarding tutorials would help accelerate adoption.
Harness can be improved by providing more clarity on the credits it issues for Harness Cloud, as it has a tiered pricing structure involving license and credit costs, which can get confusing.
Since we are an enterprise-level team, we moved past the basic tier onto a custom contract.
Codefresh is nice because we used to share the licensing, cluster creation, and those accounts around products.
From what I understand with respect to Harness, licensing and setup costs were relatively low for an enterprise, and the pricing was more catered toward enterprises who would invest in the technology.
The licensing cost is a little bit too high.
Codefresh eliminates the manual process and provides a centralized platform for continuous integration, continuous delivery, and GitOps-based release management.
The best feature of Codefresh is the GitOps control plane, which provides a single unified view of all Argo CD runtimes and clusters on the dashboard.
In my opinion, the best features Codefresh offers are extensibility, flexibility, a lot of features, and it is also very fast.
Harness uses AI to suggest errors in case of deployment failures.
The platform also supports cloud-native environments and Kubernetes deployments, making pipeline management easier, and its automation capabilities significantly improve speed and reliability.
If something goes wrong, I can use AI troubleshooting to build or test my fails and analyze the logs, suggesting the fixes.
| Product | Mindshare (%) |
|---|---|
| Harness | 4.8% |
| Codefresh | 1.0% |
| Other | 94.2% |
| Company Size | Count |
|---|---|
| Small Business | 4 |
| Midsize Enterprise | 1 |
| Large Enterprise | 10 |
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 extensibility and quick third-party tool integrations, there is room for improvements in UI performance with large logs and the promotion process between environments. Suggested enhancements include more features for cost optimization and capabilities within GCP. Taking advantage of Kubernetes and YAML proficiency for setup can be challenging, despite comprehensive documentation.
What are the key features of Codefresh?In industries relying on microservices and Kubernetes, Codefresh serves as a control plane integrated with Argo CD, facilitating the management of CI/CD pipelines. It automates Docker image builds and updates, decreasing manual errors and delays. Widely utilized for deploying containerized applications across environments like production and staging, it aids in infrastructure management and enhances the developer platform experience.
Harness offers a comprehensive toolset for automating deployment processes and enhancing software update efficiency. It's lauded for its CI/CD capabilities, feature flagging, and real-time deployment monitoring. Key features include an intuitive UI, secret management, and robust rollback functionalities, all contributing to improved productivity and reduced errors in DevOps environments.
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