Tekton Reviews

I am a PHP developer, and I create software for my clients. In the IBM cloud, I manage all my source code by GitLab. Every project I develop is ultimately deployed into a container on Kubernetes. Toolchains on IBM Cloud manage all the steps to deploy my projects, and all of these toolchains are made with Tekton.

One of the best things I appreciate is that Tekton runs on the Kubernetes platform. This is very useful when working on a project with other team members. For instance, suppose we have a very big project that requires building a large amount of source code or running many unit tests. During the build and unit test phases, Tekton on Kubernetes allows us to have a scalable machine to handle these tasks. This scalability is the most useful aspect for me.

On the other hand, if you have a simple project but many programmers continuously commit code, having a scalable machine to manage the entire toolchain is also very beneficial. The tool is easy to understand and comes with documentation. 

One feature I appreciate in Tekton is the ability to segment your toolchain into different tasks. For example, before building my source code, I can personalize a task to check the syntax with tools like Linter. Additionally, before running unit tests, I can check for updates to an SQL database or generate documentation using Javadoc style. Personalizing each task and segmenting the toolchain is extremely useful for me.

The tool should improve in terms of output flexibility. It runs on a specific Kubernetes machine, and the persistent memory storage is on a single partition. Improving this aspect could make analyzing logs and toolchain outputs from external tools easier.

I have been using the product for a couple of years. 

In my specific case, when I started a couple of years ago, I experienced some problems in the toolchain. When some steps of the toolchain failed, the errors were not correctly managed. However, after a couple of months, all the features became stable and efficient. I'm very satisfied now.

The solution is scalable because it runs on the Kubernetes platform. My company has three users. 

I have only contacted IBM Cloud support and not Tekton's. 

I used Jenkins before Tekton. 

Setting up Tekton in the IBM cloud environment is easy. It's very useful and straightforward. I tried using it in a different environment, and I must admit that while there is good documentation, you need a strong base of skills in Linux and other areas because Tekton uses several tools with Python, Java, etc. It's not too easy, but it's not too difficult either.

The solution is open-source. 

I chose IBM Cloud because my first experience with it was very positive. IBM Cloud allows you to choose a target deployment platform, a container you can scale and configure with resources. It’s extremely easy to create a toolchain, starting with your Git project and automatically deploying it on this application container every time without configuring the environment.

Before using Tekton, you should evaluate several aspects of your project: its size, the frequency of commits, and the target of your deployment. From a technical perspective, you need basic skills with the Linux command line and a minimum understanding of Kubernetes.

I rate the overall product a ten out of ten. 

At our company, we use Tekton for the deployment pipeline, and it's also implemented in the Kubernetes cluster. Tekton manages the CI/CD pipeline processes. 

The flexibility of Tekton is the most valuable feature. Multiple pipelines can be built using Tekton, and the solution acts as an operating system for the CI/CD operations. 

Tekton lacks sufficient documentation, which makes it quite difficult for startups to handle the product. 

I have been using Tekton for one and a half years. 

In our organization, we have faced some issues with the stability of the solution. It's unclear to our company team that how the product handles the APIs, because resources are available for Tekton with varying API versions. The aforementioned fact creates confusion about which API version to use in Tekton which can be solved only by thorough reading of the documentation. But at our company, we haven't faced any bugs in the solution. 

It's a scalable product. There are about five Tekton users in our organization. 

Our organization's issues with the solution were solved using the documentation and the GitHub repository. 

At our company, we are planning to switch to a simpler solution than Tekton due to its lack of documentation and simplicity. 

At our company, we are able to install Tekton seamlessly without any issues. 

At our company, we used the open-source version of Tekton to manage the infrastructure. 

The main trouble with Tekton is that you need to understand the native concepts and the instructions that can be used to manage the pipeline. I would advise others to use Tekton only for complex deployment processes and also if there is a need to integrate the CI/CD software within the Kubernetes cluster for cost-effective reasons, otherwise simpler solutions like GitHub Actions can be used.

Everyone should gain sufficient knowledge and training on Tekton before using the product. Once a team has adopted Tekton, switching to another product can be difficult. 

Tekton allows integration to make the deployment process smoother. In our company, Tekton is implemented within the cluster and thus actions like AWS resource modifications, addition of images to the container registry and permission updates can be carried out to improve organizational capabilities and efficiency in deploying applications. 

A beginner with a proper understanding of Tekton documentation, implementation and knowledge of CI/CD processes will probably not face any difficulty with the product. I would overall rate Tekton a seven out of ten. It's a high-end solution and if a user has enough time and resources, then Tekton can be used to solve every issue regarding deployment processes. Organizations with small teams should rather choose an easier solution than Tekton. 

I installed Tekton as an operator in OpenShift. Tekton is a pipeline tool. It operates within OpenShift, which is similar to how Kubernetes resources are managed. With Tekton, we utilize CRDs to define pipelines. Using the Tekton dashboard, we can create CI integrations for our applications. For instance, I used Tekton to manage CI for a Cloud-based Java application and an ongoing frontend application. Tekton's form-based approach is convenient for declaring variables and commands, handling secrets, and authentication. Overall, Tekton's ease of use and competitiveness compared to tools like Jenkins or GitHub Actions made it the preferred choice.

I use Tekton for pipelines in its original form, leveraging all the information and documentation available.

Tekton boasts multiple integrated features, making it a versatile tool. Tekton simplifies tasks by providing a beautiful interface and clear interactions. It seamlessly integrates with GitHub, enabling direct interaction. It is convenient to declare comments and variables within the same interface, eliminating the need to search for them before executing commands. In essence, Tekton functions as a comprehensive dashboard, encompassing all necessary features.

The deployment could be more accessible.

I have been using Tekton as a partner for one year and two months.

I rate the solution’s stability a nine to ten out of ten.

The documentation is very good.

The first time I connected to Docker Hub through Tekton, I encountered an issue with authentication parameters. Initially, I couldn't find where to declare the authentication credentials. These files had to be declared as a config map in OpenShift. Once configured, this config map had to be used as a parameter with Tekton. Although I spent some time searching for this information, eventually, It became straightforward, especially after using it in multiple projects.

Tekton was installed with OneClick. It's very fast to install this operator on OpenShift. It takes about fifty seconds to deploy entirely

Tekton integrates seamlessly with GitHub, creating three distinct environments: pre-production, development, and testing. Each environment is efficiently managed using Spectrum. It maintains its variables and comments before launching pipelines in any environment, ensuring clear separation. Installing it on OpenShift is considered one of the most valuable tools for DevOps engineers, providing a comprehensive view of the application.

Tekton was utilized within OpenShift and deployed on an on-premise server. We employed Argo CD for deployment. Kubernetes manifests were authorized and stored in GitHub. Tekton is tailored for the GitOps approach and is recommended to be integrated directly with GitHub repositories. With Tekton, CI pipelines were initiated. After completing the CI process, an image was generated and stored in GitHub. Subsequently, this image was utilized by Argo CD as a container for deployment, and OpenShift deployed it using Kubernetes manifests.

I recommend Tekton to my friends who work on a per-shift basis instead of Jenkins because, with Tekton, you don't need to install plugins or configure them. All the necessary plugins are already installed in the base image of the tool you want to use. You must install that base image, which is readily available in the developer hub or from another image provider. 

Tekton is easy to use in the pharmaceutical industry. This ease of use stems from its versatility in supporting two main types of workflows: working with AML files or utilizing forms with cases to manage and specify parameters and comments.

We are an organization focused on automating infrastructure and creating CI/CD pipelines for application development.

My advice is to leave it for open source rather than having the technical enterprise handle it, allowing students and universities to engage with it. As an instructor, It is advantageous for them to learn and adopt Tekton because it's relatively easy compared to Jenkins. Many students and Jenkins users struggle to learn about CI/CD and default pipelines, so I strongly recommend using Tekton.

Overall, I rate the solution a nine out of ten.

I used Tekton to integrate data functionality within a major automobile industry. I created Tekton pipelines in Kubernetes clusters to complete the CI/CD end-to-end from the source code repository in the Google Cloud Platform. This involved taking the ML training model and deploying it in the Kubernetes clusters, Cloud Run, and App Engines.

Tekton has provided flexibility and robustness in our processes. However, I faced challenges with the integration with the source code repository. The tool is very robust and provides great flexibility once you understand how to use it, but it is initially difficult to learn and understand.

The most valuable feature is the pipeline. When you run a pipeline, all the jobs are called. It is a very valuable feature that enhances job maintenance overall. 

Another valuable feature is the standalone task that sticks to isolation. This feature is very good for our needs.

I faced significant challenges with integration, particularly with Git or any source code management and Workload Identity Federation. The documentation is also not mature, making it hard to use. Improvement is needed in the documentation and the overall integration process. 

Additionally, I am not sure if Tekton can be used efficiently in multi-cloud scenarios such as Anthos.

I have been working with Tekton for the last 12 months.

The stability of Tekton heavily depends on the developer's knowledge of Kubernetes deployments and managing Tekton parts. For instance, handling the logs, maintaining the load, and managing resource allocation are essential skills required for ensuring stability.

Scalability isn't perfect yet; Tekton is not very mature in the market with only a few companies using it. The ability of Tekton to adapt to needs is somewhat limited. Overall, I would rate its scalability as a seven. The ability to create different pipelines and reuse jobs seems to be the most robust scalability feature.

I have not communicated with technical support for Tekton. I followed the official documentation, created my own YAML files, and started deploying pipelines and operators.

Neutral

I used different tools for similar use cases based on the requirements of my engineers and the clients. Tekton was introduced to me in 2022, and I used it accordingly. I did not make the decision to switch; I just used what was provided.

The initial setup is simple and involves a few commands. The configuration process, however, might cause some challenges, particularly with integrating third-party tools or handling CI/CD pipelines.

I was the only person in my team handling the deployment and application of Tekton. A single person with knowledge of tools like Ansible, CI/CD tools, DevOps, and cloud services can manage this process.

It is essential to have a straightforward, end-to-end sample for implementation, including steps involving job, trigger, and pipeline settings. Syntax and URL validation validators are important for the effective use of Tekton. They will help check the correctness of the code and improve efficiency.

I'd rate the solution six out of ten.

We use Tekton to build and deploy business apps, which include analytics programming in their strategy. Our DeepLab setup involves routers that trigger Tekton within our clusters to build and deploy their strategy as needed.

Tekton is also used in Flutter, eliminating the need for GitLab CI for our setup. While GitLab Runners can handle build and deployment tasks, Tekton offers strong integration with EKS and Kubernetes clusters. 

Tekton is useful for us, especially since we use it for all other deployments and in the cloud. In terms of build and deployment capabilities, it can function similarly to GitLab.

The Helm Chart for Tekton doesn't come with all the manifests needed for installation; you must apply them manually. I use ArgoCD to upgrade and manage Tekton, but manual updates to the Helm charts are still required.

I have been using Tekton for one year.

We have analytics that utilize a negative tone. Around 50 people use this in the cloud. When they need to deploy or release a strategy, they tag it and create a new Tekton task. Tekton then handles building and integrating its strategy within our clusters.

It is open source, like DeepLab Community Edition.

Tekton is easy to use, but updating from older versions can be challenging due to method changes with each release. I've spent a lot of time upgrading it. Once you start using Tekton, you can manage it with just a few files and apply them easily. 

The best documentation for Tekton is their CRD for the operator.

Overall, I rate the solution a six out of ten.

I use the platform to manage development pipelines. Our infrastructure is integrated with Kubernetes, and we utilize it within an OpenShift environment for automation. I have developed various pipelines and plugins for both front-end and back-end applications, including Angular for the front end and Quarkus, a Java framework for microservices, for the back end. The platform supports tasks such as code cloning, testing, and deployment by integrating SonarQube, Argo, and Nexus tools.

The product's best feature is its ease of implementation. It is well-suited for microservices architecture and integrates seamlessly with Docker and other tasks. The orientation towards microservices makes managing different tasks and projects efficient.

Initially, working with YAML configuration can be challenging using the solution. 

I have been using Tekton for two years.

I have not encountered any stability issues with the platform.

Approximately four people in our organization use Tekton. It is a scalable platform. 

The setup process is simplified by its integration with Kubernetes and its examples for various tasks.

I suggest utilizing available tasks and resources, such as copying and using tools like ChatGPT, to write and configure tasks. 

The platform is user-friendly and provides good support for managing different pipelines.

I rate it an eight out of ten. 

The CI solution of the team I worked for was based on Tekton. Since Tekton is a Kubernetes native framework, we can easily configure it within a Kubernetes cluster, which has many benefits.

I would say the customization ability that Tekton provides is good. As an example, if we look at the pipeline structure of Tekton, a Tekton Pipeline was built with Tekton Tasks. We can specify a Tekton Task within the pipeline YAML. Otherwise, we can specify the task in a separate YAML and add it as a reference within the pipeline so that we can use the Tekton Task as a reusable component in other pipelines as well. Another thing is that we can execute a Tekton Task based on conditions such as "when" expressions. Furthermore, Tekton provides Tekton Triggers. We use Tekton Triggers to dynamically execute a pipeline based on an event, which is also a very useful feature for us. These customization features of Tekton made it more valuable and flexible to use for us.

The product's documentation is an area of concern, making it an area where improvements are required.

Apart from the documentation, there is one more thing that I believe needs to improve in Tekton. In the Tekton pipeline structure, there is an optional feature called finally section, where we can add all the core Tekton Tasks, which have to be executed even in a pipeline failure scenario. But the issue is that we cannot give a sequence for the tasks in the finally section, so all the tasks are running simultaneously. I believe if we had a feature to help users add a sequence for the tasks in the finally section, then it would make our lives easier.

The scalability could improve a lot in the future.

I have been using Tekton for a year. I am a user of the tool.

Sometimes, we face some version compatibility issues, but when we upgrade the version, it gets rectified. Stability-wise, I rate the solution an eight out of ten.

I know that there are more than 20 people using Tekton within our company. As the CI solution in our company is based on Tekton, many people, including new joiners, also have to learn Tekton. Scalability-wise, I rate the solution a seven out of ten.

When our company faced challenges with the tool, we had to do our own trials and errors to find a solution. Sometimes, we had to go through some GitHub issues related to the challenges we faced, but most of the time, we couldn't find an exact solution by looking at the GitHub issues. So we had to try to resolve the issues we faced in Tekton with the knowledge we had. We did not contact Tekton's support team.

Tekton's integration capabilities have helped our company's development workflow. Tekton is a Kubernetes native framework, so most of the command lines that we use in Kubernetes could also be used within Tekton framework. Basically, configuring Tekton Pipelines within our Kubernetes cluster was very easy compared to using other solutions. Overall, the development workflow was easy when using Tekton.

As a beginner who is trying to use Tekton, it will take some effort to learn it because the documentation in Tekton has to improve a lot. There are no tutorials to learn about Tekton because I personally learned it from my seniors and my experience and even when we faced challenges in the development, we couldn't find a solution on the internet. We had to do our own trial and error to find a solution, which was one of the main challenges while using Tekton.

I recommend the tool to others as using Tekton makes our lives easier. As far as I know, many companies don't know about Tekton. If they knew that there is something called Tekton, they would definitely adjust to it. 

I rate the tool a nine out of ten.

We use the solution in CI/CD pipelines for application deployment. 

The tool's most valuable aspect is its compatibility with the cloud-native environment. It can be easily installed on Kubernetes and leverages its resources to run CI/CD pipelines.

Some of the tool's cons include its minimalistic dashboard, which lacks detailed information and control compared to other tools like Jenkins or GitLab. Additionally, it's primarily used by Japanese companies.

Another drawback is its dependency on the Kubernetes cluster for deployment, which could be a limitation. One feature I would like to see included or enhanced in Tekton is the ability to reuse specific pieces of pipelines as reusable modules.

I didn't choose this tool because they already used it when I joined this company. I don't like this tool because it has many limitations. I believe many other tools are better than Tekton.

Integration with other solutions involves mainly API calls. Tekton lacks a mature dashboard, making integration more challenging than tools like GitLab or Jenkins.

I encountered some challenges, particularly with compatibility between different versions of Tekton and Kubernetes. For example, the Tekton version couldn't be installed on Kubernetes version 1.24 due to compatibility issues. Some versions of Tekton don't support certain Kubernetes features, leading to installation issues. It depends on Kubernetes, and matching Tekton versions with compatible ones is essential.

The tool has increased our operational costs due to the need for repetitive tasks and code duplication. Unlike other tools like GitLab or Jenkins, it often requires duplicating resources across clusters or regions. This duplication elevates maintenance costs, as any code changes must be applied in multiple locations.

I have been working with the product for ten months. 

The solution is stable, and I haven't encountered any performance issues. 

I find Tekton to be scalable. It leverages Kubernetes as its underlying system, allowing for scalability. For instance, if you need to run numerous concurrent jobs, Tekton can accommodate this requirement. My company has 70-100 users. 

We have deployed Tekton across multiple locations, departments, and cross-tenants. It has been used for about three or four years and is utilized by various teams to deploy applications in different clusters and locations.

Since the tool is open-source, we resolve issues ourselves. 

The installation process is straightforward, which stands out as one of Tekton's notable advantages. Deploying a YAML file onto your Kubernetes cluster is all it takes, and the installation on Kubernetes is quick. I believe it's achievable in less than a minute. In contrast, other tools like GitHub or Jenkins often require significant setup time.

There is minimal ongoing maintenance required. However, upgrading to the latest version can sometimes be challenging due to compatibility issues between Tekton and Kubernetes. Upgrading Tekton may also necessitate upgrading the Kubernetes cluster, which can incur additional maintenance costs.

The tool increases our operational costs because it requires repetitive tasks. Other tools like GitLab or Jenkins can reduce costs, as they eliminate much of this repetition. Currently, we often need to duplicate resources. For example, if we want to install based on manifests in a new cluster, we must copy all the base modules to another cluster and keep them in the repository. This duplication can increase the maintainability costs, as any changes to the code must be made in multiple places.

The tool is open-source and free to use. 

I wouldn't recommend the product to others. I recommend GitLab for anyone looking to work on mature CI/CD pipelines. I rate the overall product a four out of ten. 

Tekton is the orchestration engine within OpenShift, which is our on-premise platform. Since we are not on the cloud yet, OpenShift plays a strategic role, and Tekton is a significant part of it. It serves as an orchestrator.

In my experience of the last two years using Tekton and OpenShift pipelines, I haven't encountered many issues. As an orchestrator, Tekton works best. It's just one component of the larger OpenShift platform. Tekton consists of multiple components like events, trigger bindings, and more. However, when it comes to the overall OpenShift platform, being a platform as a service, most aspects are taken care of. 

Tekton plays a primary role as an orchestrator. When we receive a webhook from any Git repository, such as Azure Git or GitLab, Tekton triggers the pipeline and performs tasks like code retrieval, running SonarQube or Fortify tasks, and creating and deploying images to multiple environments.

So we can have multiple promoted environments, starting from dev to SIT, then to UAT, and finally to production. We follow a continuous flow branching approach, allowing us to promote changes from smaller environments to larger ones like dev to SIT, SIT to UAT, and UAT to production, which is our master branch. This helps us maintain a smooth workflow and ensures reliable deployment.

Tekton is an orchestrator. It provides seamless integration for our pipelines. It offers robust support for executing tasks within the pipeline, allowing us to set up and run pipelines quickly.

Additionally, Tekton's underlying architecture with OpenShift enables us to create, implement, and run end-to-end pipelines. We can integrate various automation tools like Fortify or SonarQube for testing, code scanning, regression testing, and more. All these tasks can be executed within the pipeline using Tekton.

There might be occasional issues with storage or cluster-level logging, which can affect production. But as a component, Tekton performs flawlessly.

As an orchestrator, Tekton effectively executes most tasks. However, there are instances where we feel that YAML files, which Tekton reads, could benefit from increased flexibility. You see, in OpenShift, everything revolves around YAML. We have different components specified in YAML files, and when we put them together in an OpenShift pipeline, it generally works fine. However, occasionally we encounter difficulties when editing these YAML files.

I have been working with Tekton since we implemented it in 2020, so it's been almost two years.

We haven't encountered any stability issues with it. It has been reliable and available.

Tekton is scalable.

Red Hat's support has been quite excellent. We have a close partnership with Red Hat, as our DevOps strategy heavily relies on OpenShift as a core component. 

Since our entire architecture is on-premise, we have made significant investments in OpenShift. Setting up the OpenShift cluster and configuring different components, including Tekton, has been smooth and hassle-free for us, thanks to Red Hat's support.

It's not solely about Tekton itself. We chose OpenShift as a platform as a service because we opted for on-premise implementation instead of the cloud. The implementation of OpenShift includes the incorporation of Tekton.

The initial setup is actually easy. Tekton is just one of the underlying components in OpenShift pipelines. It's a technology and engine with a straightforward architecture, so the setup process is quite simple.

We have a command-line setup where we use the OpenShift client to connect to Tekton. It's like talking to the cluster, and Tekton executes the tasks on that specific cluster. It's an efficient and streamlined process.

The entire OpenShift platform is supported by just two DevOps engineers.

But we might need to expand the team in the future. Two resources are not sufficient considering the workload and stress we handle.

The pricing is based on OpenShift's vCPU licenses. We pay according to the number of virtual CPUs, which can be costly. 

However, it's important to note that Tekton is just one of the underlying components in OpenShift. Therefore, the pricing and licensing considerations are more related to OpenShift as a whole rather than Tekton alone.

We have evaluated multiple vendors, including Red Hat, whose DevOps architecture includes Tekton as an underlying component. However, other vendors also offer similar orchestration components in their architectures. 

So, there are various tools available from different vendors that serve the same purpose as Tekton.

There are several vendors in the market who provide their own versions of orchestration components for DevOps architectures, apart from Red Hat. They implement their own approaches and name their components accordingly, but the purpose is similar to Tekton.

I would recommend Tekton as an orchestrator because it works well within the OpenShift environment. While there may be similar orchestrator components offered by other vendors in different DevOps architectures, Tekton's integration with other OpenShift components makes it a strong choice.

I would rate Tekton a seven. The only drawback I've experienced is the difficulty of modifying YAML files on the fly and making changes, as it doesn't work well in that aspect. However, apart from that, Tekton performs well in other areas.

We use Tekton for CI/CD automation, continuous integration, continuous deployment, and general-purpose automation.

The platform's most valuable features are the cloud-native approach and atomicity. Unlike other monolithic solutions, it is early in adopting a cloud-native platform for CI/CD, which enhances scalability. Additionally, its ability to operate at a granular level allows us to create small automation jobs and combine them into larger tasks, making it very useful.

One project involved automating our deployment processes across multiple environments. By leveraging the solution, we reduced deployment times significantly and increased reliability, allowing for more frequent releases and improved overall productivity.

The product's scalability can be challenging when multiple users access it simultaneously. We have seen some resource crunches, typical for any product supporting a large user base. 

One of the main challenges has been managing complex pipeline dependencies. As the scale of our projects, ensuring that all dependencies are correctly defined and executed in the right order can become cumbersome.

Integration is an area where it lags compared to other CI/CD tools like Jenkins, which has a rich plugin ecosystem. It lacks sufficient vendor support for plugins, requiring users to write their integration scripts, which is less convenient.

Enhancements in documentation and user onboarding would be beneficial. Clearer guides and tutorials could help reduce new users' learning curves and facilitate smoother integration with existing workflows.

The support team is knowledgeable and responsive when resolving issues, though improvements in onboarding and documentation could further enhance the overall experience.

Neutral

The initial setup was somewhat complex, requiring a thorough understanding of Tekton's structure. Once configured, it provided a flexible and scalable solution for our CI/CD needs.

We implemented the product with the help of our in-house team. 

We evaluated other CI/CD tools before choosing Tekton. Tekton's flexibility and cloud-native approach were key factors in switching.

The platform's custom tasks feature allows organizations to create standardized team tasks. It enables enterprises to build a flexible framework atop Tekton, making it easier to define workflows with standard inputs and outputs.

It does not differentiate between continuous integration and deployment, offering a unified framework. It provides tooling to run code that builds or deploys applications, allowing for flexible task configurations and parallel processing.

We manage version control through Git repositories, where we store our Tekton pipeline definitions. It allows us to track changes and revert to previous versions, maintaining a robust versioning system for our CI/CD processes.

It has the potential to be a long-term solution due to its scalability and flexibility. However, continuous improvements in user experience and community support will be key factors in its sustained adoption within our organization.

Security is managed through role-based access controls (RBAC) and secret management features. We ensure that sensitive information, such as API keys, is stored securely and that only authorized users can access critical pipeline functions.

Overall, I rate it a six out of ten.