Share your experience using JFrog Bintray [EOL]

My main use case for JFrog Artifactory is to store Docker images and other build artifacts from our Java applications.

In our workflow, JFrog Artifactory acts as a centralized repository where we push all our artifacts, and being a SaaS company, we have a lot of releases happening very frequently. We also have a Kubernetes cluster where we run our worker nodes. As we release something, we ensure that it is stored and sent to the proper repository, in this case JFrog Artifactory, from where Kubernetes will pull and deploy those images.

The best features JFrog Artifactory offers include JQL, which is very helpful for finding particular artifacts that are older or identifying items that need to be cleaned up. We also appreciate the caching mechanism that allows us to cache images in different locations in our AWS infrastructure, ensuring that we do not always have to go to a central location but can fetch those artifacts from a nearby cached location.

JFrog Artifactory caching has improved our deployment speed and cost. In AWS, pulling an image from a cross-location incurs charges, and we run many jobs on an event basis, necessitating frequent image pulls from JFrog Artifactory. Since we operate in the same location, we end up paying less to our cloud provider by reducing the total network cross AZ network usage.

For usability, JQL is valuable, as it allows us to fetch particular artifacts and do cleanup efficiently.

JFrog Artifactory has positively impacted my organization, especially in terms of cost savings. We used to have a centralized repository, but with JFrog Artifactory, we can cache artifacts in different locations close to our production and development servers, significantly reducing costs.

Although JQL is a great tool, I have noticed that JQL queries can be hard to learn. A more user-friendly query language or improved UI would be beneficial, as the current UI requires deep technical knowledge for effective use.

I have been using JFrog Artifactory for three years.

JFrog Artifactory has been stable in my experience.

JFrog Artifactory is definitely highly scalable. When we deploy in serverless environments, especially ECS, JFrog Artifactory scales effectively to handle a lot of requests for serving images.

I have not interacted with their support team, so I cannot comment on customer support.

We initially used Amazon ECR, but it did not work well for us when scaling, which led us to switch to JFrog Artifactory, especially for multi-cloud stability.

I purchased JFrog Artifactory through the AWS Marketplace.

Although JFrog Artifactory is slightly high-priced, we are willing to pay for it because it is a critical part of our technology operations and DevOps processes. While there are free tools available, we find it worth the investment.

I evaluated ECR from Amazon before choosing JFrog Artifactory.

JFrog Artifactory still has room for improvement, particularly with the user interface and documentation. However, JFrog Artifactory has helped us scale faster and is a highly available, robust artifact repository that boosts our confidence in production.

My advice for others considering JFrog Artifactory is to start with free tools available in the market. If you encounter bottlenecks while scaling, that is when you may need a dedicated tool like JFrog Artifactory. Larger companies will need a robust artifactory to ensure their production does not struggle.

My company does not have any business relationship with JFrog Artifactory other than being a customer. I rate JFrog Artifactory a nine out of ten.

JFrog Artifactory serves as our main tool for managing and distributing software artifacts across our deployment pipelines. We use it primarily for storing, versioning, and securely sharing binaries, Docker images, and dependencies across multiple client environments, ensuring consistent builds and faster release cycles.

In a recent project, we used JFrog Artifactory to manage Docker images and dependencies for a multi-service application deployment across several client environments. By pulling versioned artifacts directly from JFrog Artifactory, we ensure consistent builds, faster deployments, and reduced errors, which helped the team deliver updates reliably across both cloud and on-premises systems.

We also use JFrog Artifactory for dependency management and release promotion, moving artifacts from deployment to QA to production in a controlled, versioned way. This streamlines CI pipelines, ensures traceability, and reduces deployment errors across multiple clients.

The best features JFrog Artifactory offers are the universal artifact repository, versioning and immutability, secure access control and permissions, Docker and Helm registry support, artifact promotion workflows, high availability, and scalability.

The universal artifact repository in JFrog Artifactory lets us store and manage all types of artifacts, Docker images, binaries, and dependencies in one place, which simplifies CI/CD pipelines across multiple client environments. Secure access control ensures only authorized users and CI/CD jobs can access or deploy artifacts, reducing the risk of accidental overwrites or unauthorized deployments. For example, during a recent multi-client release, this setup ensured consistent builds and prevented deployment errors, even across different cloud and on-premises environments.

JFrog Artifactory has streamlined our build and deployment process across multiple clients and environments. We have seen faster, more reliable releases, reduced build errors, and improved JFrog Artifactory traceability, which helps with auditing and compliance. Overall, it has saved time in CI/CD pipelines and ensured consistency across cloud and on-premises deployments.

JFrog Artifactory could be improved with a more intuitive user interface and faster search capabilities, especially when managing large multi-client repositories. Setup and configuration for multi-tenant environments can be complex, and better out-of-the-box monitoring and alerting features would help reduce operational work overhead.

Faster replication for global deployments and more native integration with security scanning tools would be helpful.

I have been using JFrog Artifactory for three years.

JFrog Artifactory is stable.

JFrog Artifactory is scalable from small and medium enterprises to larger enterprises and very large enterprises.

My overall experience with JFrog Artifactory's customer support has been good for standard issues with reasonable quick responses and helpful guidance. However, when there are complex or multi-cloud deployment issues, resolution sometimes takes a longer time.

Positive

Previously, we used a combination of local artifact repository and manual versioning, which was prone to errors and inconsistent builds. We switched to JFrog Artifactory because it provides centralized artifact management, version control, CI/CD integration, and better reliability across multi-client environments.

The initial setup was good, but it took a lot of time to complete.

We have clearly seen a return on investment with JFrog Artifactory. Due to its centralized artifact management, which has reduced build and deployment times by twenty percent, prevented multiple rollback incidents, and freed our team from manually managing dependencies, saving several hours per week. For example, during a recent multi-service deployment across several client environments, consistent artifact versioning eliminated five potential errors, ensuring a smooth and reliable release.

JFrog Artifactory pricing and licensing are enterprise-level, which means it is relatively costly, especially for high availability and replication across regions.

We evaluated other options such as GitHub Packages and Nexus Repository Manager. We chose JFrog Artifactory because it offered the broadest universal artifact support and better scalability for multiple client hybrid cloud environments.

I would rate JFrog Artifactory nine out of ten.

I chose nine out of ten because JFrog Artifactory greatly improved build consistency, CI/CD efficiency, and artifact traceability, which are critical across multiple client environments. I did not give it a perfect score of ten due to the user interface complexity, slower search in large repositories, and some setup challenges for multi-tenant deployments, which could be improved.

JFrog Artifactory should provide better documentation for advanced multi-client and multi-cloud setups, which would also reduce time and troubleshooting effort.

I would recommend that you plan your repository structure, take advantage of versioning and artifact promotion, and access control to ensure consistent builds and secure releases. Also, prepare for initial setup effort in multi-client or multi-cloud environments to maximize efficiency and reliability.