Tekton Reviews

Our primary use case is for cloud integration and cloud deployment. Tekton is a cloud-native tool that we utilize in Kubernetes and OpenShift. It helps us with code integrations and code deployment. We use it for automating various processes and integrating it with our DevOps environment.

Tekton has improved our performance by allowing us to define metrics and resource limits, enhancing the performance of our whole pipeline. The ability to customize tasks and use custom containers has also allowed us to tailor our workflows to our needs.

The customization is valuable. We can make a lot of customizations, and data sharing between tasks using workspaces is very convenient. Moreover, the flexibility and ease of use in creating and attaching new tasks make it very user-friendly. The Tekton UI and user interactions are very good.

Tekton could improve by adding better documentation. The documentation needs improvement to clarify supported features and required integrations. Additionally, while it is currently a cloud-native solution limited to Kubernetes, an option to run it locally on our own systems would be very useful. Security integrations and third-party integrations also need enhancement.

We have been using the Tekton solution for nearly two years.

Tekton is stable and performs well. However, it would benefit from adding new features quickly to stay relevant and helpful.

Tekton is highly scalable due to its cloud-native nature.

We have not escalated any queries to Tekton itself. Since Tekton comes as an integral part of Red Hat, we rely on existing documentation and haven't had any issues requiring direct technical support.

In the past, we used Jenkins for CI/CD. Each tool has its own use case and pros and cons, but Tekton's cloud-native capabilities and ease of deployment and maintenance make it a better fit for our needs.

The initial setup of Tekton is very straightforward. We install it as an operator in our cluster, making maintenance tasks like auto-upgrades and updates easier.

There is no specific cost related to resource optimization provided by Tekton. We need to manage the resource limits and requests ourselves to prevent resource exhaustion, especially when running on cloud platforms like AWS, Azure, or GCP.

I recommend Tekton to anyone closely interacting with Kubernetes. It is particularly useful for larger teams with complex application deployment requirements. However, for smaller teams or simpler use cases, it may not be necessary.

I'd rate the solution six 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 primarily as a CI/CD tool. We develop the product, and after developing the product, we deploy it to the cloud. Before deployment, Tekton is used as a CI/CD tool to validate and check our test cases, vulnerabilities, and licensing issues. If any test cases are written, they will first validate Tekton cases. Once the test cases pass, it moves on to check for vulnerabilities and licensing issues using Fosa, which is one of our QA and Check Point tools.

Tekton has significantly improved our organization, especially in the area of reusability. When we moved to Tekton from Jenkins, I felt that the tasks could be reused for any number of pipelines within the same cluster, which was not the case with Jenkins. This reusability is a major benefit. Moreover, it has streamlined our CI/CD pipelines, making the deployment process much more efficient. Tekton provides a clean UI interface and allows us to run multiple tasks simultaneously, which has enhanced our efficiency and productivity.

The most valuable features of Tekton include the reusability of tasks, the clean UI interface, and the binding of services and secrets. The tasks can be reused for multiple pipelines, making it very efficient. The UI interface appears very clean and user-friendly. The binding of services and secrets is also very clear and well-organized.

One area where Tekton can improve is log management. Currently, logs are often deleted after a day, making it challenging to refer back to logs from previous pipelines. It would be beneficial to have access to logs for at least the past two or three days. 

Additionally, stability needs to be improved. Sometimes Tekton crashes or freezes during important deployments, and this can cause delays. Enhancing stability and log management would greatly benefit users.

I have been using Tekton for almost three years, approximately from the time our organization migrated from Jenkins to Tekton.

The stability of Tekton needs improvement. Occasionally, Tekton crashes or freezes during important deployments, which requires waiting and troubleshooting. This instability can be problematic, especially during critical launches.

Tekton's scalability is generally good, but there is room for improvement. The resource quotas are somewhat limited, and increasing the resource quotas could enhance scalability further. The ability to run pipelines in parallel is a good feature, but expanding the resources would improve scalability even more.

Our organization has sufficient technical support for Tekton. However, there are times when more support would be beneficial, especially when dealing with production launches and critical deployments.

Neutral

Before Tekton, we used Jenkins. Our organization decided to switch due to the cost, performance, and the ability to leverage new tools and applications.

The initial setup of Tekton was somewhat challenging. It took a while to understand the various components, such as service binding, role binding, and how to create routes and bind services. It was a bit difficult at first because I was the first person in my team to explore Tekton, but after gaining an understanding, it became much easier.

The deployment and implementation were handled within our organization. A few team members, including myself, explored the functionalities and shared our knowledge with the rest of the teams.

The return on investment from Tekton is primarily seen in time savings. Once the initial setup is complete, the automated processes reduce the time needed for deployments and streamline the workflow.

I'd rate the solution seven 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.

Generally, we are using Tekton for deployments instead of Jenkins. We create pipelines in Tekton for a number of services, each with different logs and requirements, especially in our DevOps practices. I have been using this Tekton pipeline for nearly six months.

The main benefits we have seen from using Tekton include saving time compared to Jenkins. The user interface is easier to use, and there are no plugins required for installation, unlike Jenkins. Time savings and ease of use are significant advantages.

Tekton has a user interface that facilitates time savings, making it more user-friendly than Jenkins. It is easier to handle and eliminates the need for plugin installations.

The user interface of Tekton could be improved. Sometimes, it takes more time during production deployments than expected.

I have been using the solution for nearly six months.

Tekton is stable, easy to use, and currently provides good stability.

Tekton is scalable, and most of our team members are using the Tekton pipeline.

As of now, I haven't asked for technical support. I would approach technical support if I encounter any problems.

Positive

Before Tekton, we used Jenkins. Tekton is better than Jenkins in terms of user interface and ease of integration without the need for plugins.

The initial setup took about one week, and there were some doubts on how to operate and execute deployments. But after learning about the tools, the setup was smooth, and everything was configured properly.

Four teams were involved in the deployment process: the ECS team, tenant ops team, DevOps team, and development team.

There are efficiency gains due to time savings in pipeline setup compared to Jenkins. This is a major point of efficiency for Tekton.

I would recommend Tekton to other users who are considering its implementation. It's better compared to Jenkins.

I'd rate the solution eight out of ten.

I primarily use Tekton for continuous integration purposes. When we build applications, we use Tekton for CI. For deployments, we use ArgoCD. Most of the time, we use Tekton to facilitate the CI process.

Tekton is primarily a time-saving solution. While it does take some time to set up initially, it is very efficient once running. It helps manage deployments and separates processes accordingly, which benefits both developers and me. Developers don't have to worry about the deployment or integration part as Tekton takes care of those aspects, making it easier for me to manage the pipeline.

Tekton allows you to categorize tasks according to your needs and minimizes the amount of code needed. For instance, you can separate tasks to suit your requirements. Additionally, when integrating with Terraform, Tekton makes it easy to trigger only specific parts of the infrastructure, which is beneficial for ops engineers.

For infrastructure deployment, integration is somewhat complex, especially when using Terraform with Tekton. It would be beneficial if this process were simplified.

I have been working with Tekton for about one year now.

Sometimes, when there are updates and patches, it can cause issues. For example, building a Docker image may require changing the base image due to upgrade issues. However, understanding Tekton properly helps in resolving such issues easily.

Tekton is very scalable. It's particularly effective for managing resources across multiple clusters and regions, integrating resources from different cloud providers.

Before Tekton, I used Jenkins and GitHub Actions for CI/CD pipelines. I also used Bitbucket pipelines for smaller web-related projects. These options required maintaining multiple pipeline files, unlike Tekton, which allows for streamlined categorization and management.

For infrastructure deployment, we categorize each Terraform file according to the specific components needed, such as VPCs and subnets. We maintain separate repositories for Tekton files and deploy specific components by providing the necessary paths and executing the appropriate files. Depending on whether it's a development or production environment, we maintain the appropriate pipelines.

I'd rate the solution eight out of ten.