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My primary use case for AWS Amplify is to build and host full-stack web applications where it handles front-end deployment, authentication via Cognito, and basic back-end integration.

AWS Amplify offers a solid set of features for rapidly building and deploying front-end focused applications. One of the most valuable features is AWS Amplify hosting with built-in CI/CD, which allows me to deploy my application seamlessly directly from the Git repository.

Previously, we used to create the CI/CD pipelines manually and that was time-consuming. The built-in CI/CD in AWS Amplify is significantly simpler and faster to set up compared to traditional solutions like Jenkins pipeline or GitHub Actions. Features like branch-based deployment and preview URLs were especially useful for me, as every pull request could be tested in an isolated environment without additional configuration.

Earlier, it was very challenging for us to manage all the CI/CD pipelines because for every new project, we had to create the pipelines manually. After using AWS Amplify, this solved our problem completely. Now, by using AWS Amplify, we are easily able to deploy our projects on AWS.

Currently, I do not believe that AWS Amplify is lacking in features, but one of the main limitations I feel is a lack of flexibility in CI/CD pipelines. The built-in pipeline works well for simple front-end deployment but becomes restrictive when I need custom workflows, multi-stage approvals, or integration with other AWS services. This is one area that AWS could improve.

I have been using AWS Amplify for the last one and a half years.

AWS Amplify is stable and we do not need to change pipeline configuration day-to-day.

In my experience, AWS Amplify scales very well for front-end applications and moderately for back-end workloads, primarily because it leverages underlying AWS services like S3, CloudFront, and Lambda. From a back-end perspective, AWS Amplify works well for simple to moderately complex use cases, especially when using managed services like Cognito, AppSync, or Lambda. It allows applications to scale without requiring manual infrastructure management. However, as the system grows in complexity, such as requiring custom workflows, fine-grained scaling, controlling, or multi-services orchestration, AWS Amplify can become limiting. In those cases, we had to extend or move parts of the back-end to native AWS services for better scalability and control.

Most of the time, I have received customer support in real time. My experience is good with the AWS Amplify customer support team.

I have used Jenkins CI/CD pipeline previously. That was good for custom workflows, but it was really time-consuming and setup also required a great level of DevOps skills. Because of those things, we switched to AWS Amplify and that simplified our complete CI/CD workflows.

AWS Amplify saved time to market in a huge amount. Earlier, time to market was one week after development, as developers used to take one week to create the CI/CD pipelines and configuration. Now it has been reduced to one day.

Earlier for the CI/CD pipelines, three employees were required, and they spent one complete week to build the CI/CD pipelines. After using AWS Amplify, only one employee was required and he completed that setup within one day.

AWS Amplify simplified our complete requirement and the integration of user authentication with my application.

My experience with pricing for AWS Amplify has been generally positive, especially for small to mid-sized applications. Most of our clients required small to mid-sized applications. The pricing model is pay-as-you-go, which makes it cost-effective during the early stages when usage is low. I only have to pay for what I am using. Additionally, as there are no upfront setup costs, this also saves my money.

Before using AWS Amplify, we evaluated a few other options based on our CI/CD requirements. We looked at Vercel and Netlify because they also have integrated CI/CD. We also considered continuing with native AWS services like Amazon S3, Amazon CloudFront along with AWS CodePipeline, which would give us full control but required more setup and maintenance effort. Ultimately, we chose AWS Amplify because it offered a good balance between ease of use and AWS ecosystem integration. Since our back-end was already on AWS, AWS Amplify made it simpler to manage authentication, storage, and deployments without introducing additional platforms.

I suggest you do not go blindly with AWS Amplify. Check your use case first. If it is not a very complex use case, then you can surely go with AWS Amplify. But if your pipeline requires some custom stages or some custom approvals workflows, then think carefully before using it. I would rate this review as nine out of ten.

My main use case for GitLab is as a version control system that we are using. Currently, I am working on an end-to-end AI pipeline, and I have deployed my whole code using GitLab so that all things are utilized for version control for my back-end AI and front-end team. We merge all the Git codes into GitLab, and my CI/CD, issue tracking, security, and monitoring is maintained inside GitLab.

We try to collaborate with all the teams together on different features. Within GitLab, we are not utilizing Jenkins or any product management tools because it perfectly renders all the information. It maintains the version control system, which is very helpful to containerize and deploy all services, allowing us to have everything together in production.

CI/CD is the most important feature that I am utilizing with version control and security as well, and all things are very useful inside GitLab.

CI/CD helps my workflow by allowing me to integrate any new changes or any new version that I want to deploy in the whole ML lifecycle, which I implement through new integration phases and identify updates in the deployment scripts. We generate YAML manifest files, add dependencies, and deploy them utilizing GitLab's versioning system, identifying any security patches that need to be added or incidents that need to be managed, triggering the workflow. We try to manage the perfect scenarios.

GitLab has impacted my organization positively in terms of version control systems, providing many smart features and reducing the sharing of dependencies compared to what we used to do previously. It has helped the organization merge and collaborate within the team on the level of code accesses and identify how actionable insights can be inputted within the whole pipelining mechanisms, allowing us to easily perform actions on CI/CD. My organization has adapted this and resulted in more productive work.

There are many improvements that GitLab can implement, such as addressing the issue of caching. Currently, when I have multiple tasks to merge or attempt multiple merges, the CI/CD and overall GitLab processes get slower. Implementing caching to allow parallel jobs to execute together would optimize the solution and enhance efficiency.

The UI is not user-friendly compared to how GitHub Actions operates. If we could customize the UI interface or have options for plugin-based mechanisms, that would be more suitable and increase DevOps in enterprises.

I have been using GitLab for around three years.

GitLab is stable.

GitLab's scalability is good, allowing multiple employees to work together, change code collectively, and perform all kinds of CI/CD regardless of impacts from bug fixes.

I have not reached out to customer support currently, but email support is very good, although I have not interacted with any call-based mechanisms or voice call systems.

I was using GitHub and GitHub Actions prior to this. While GitHub Actions provided good CI/CD operations, GitLab has added smart features, such as a graphical view of branches in version control mechanisms and tracking changes made by authors at specified times. Everything is maintained perfectly in my codebase, and I can easily track buggy code, identifying issues with excellent visibility. This visibility compared to GitHub and GitHub Actions is what led me to switch to GitLab.

The setup cost for GitLab is minimal since the team has its own minimal resource balancing. The costing falls into an intermediate stage and is impactful across all results within the team. It allows for CI/CD stages and addition of security patches smoothly, with only a slight charge that is not significant. Everything related to scripting, processing, management, and deployment works fine.

I have seen a return on investment with increased collaboration within the team resulting in more productive work and a reduction in time based on prior experiences, which emphasizes GitLab's usefulness.

The setup cost for GitLab is minimal since the team has its own minimal resource balancing. The costing falls into an intermediate stage and is impactful across all results within the team. It allows for CI/CD stages and addition of security patches smoothly, with only a slight charge that is not significant. Everything related to scripting, processing, management, and deployment works fine.

Before choosing GitLab, I evaluated options like GitLens and Bitbucket. I have tried those tools prior to this project, but ultimately, I chose GitLab as the foremost solution for version control mechanisms.

Since using GitLab, we have experienced fewer errors in identification, and any incidents coming out at the production level can be maintained to facilitate complete results, ensuring that collaboration works fine and deployment scripts remain easily executable, maintaining all services perfectly. Any impactful scripts work fine, whether making minor or major version updates.

I recommend GitLab if you are looking for a good graph-based solution or any impeccable solution for version mechanisms. I would rate this product a 9 out of 10.