JFrog Artifactory Reviews

My main use case for JFrog Artifactory is hosting artifacts for products, ranging from Docker images to Helm charts, and regular binaries.

I can give you a specific example of how I use JFrog Artifactory in my workflow: we have automated steps in our pipelines where once we build our artifact, such as Docker images, we push them to JFrog Artifactory, and then in the deploy pipeline, we have automated steps that pull the artifact from JFrog Artifactory and deploy them to different environments.

The best features that JFrog Artifactory offers are that you can have all kinds of artifacts, supporting many different ones such as Docker images, Helm charts, and different binaries from different software languages. It has versioning that you can use, proxying as well that you can use as a mirror proxy, and there is also nice access control around it, so you can limit who has access to which parts of your JFrog Artifactory tree.

Regarding access control and proxying, those features help my team by making it easy to provide read access to many different teams for different artifact repositories, which works really nicely when you have many teams requiring the same package but then have one team that is actually the developer of that package or artifact. In terms of the proxy, you can use it as a mirror proxy so that all your pulls for an image can go through JFrog Artifactory before they reach Docker or something, adding an extra layer of security in the company.

JFrog Artifactory has impacted my organization positively by helping us have a standard artifact management solution, so everybody is aligned, allowing us to use the same best practices and same documentation, making it easy to implement in many different pipelines.

Having a standard solution such as JFrog Artifactory has affected productivity and collaboration positively. Documentation is available for everybody that is using it, so my team, as clients to JFrog Artifactory, finds it easy to pull the documentation and implement it in our pipelines or for whatever needs we have related to JFrog Artifactory, which eases up collaboration by providing a single way of doing package management.

I cannot say much about how JFrog Artifactory can be improved.

I appreciate the opportunity to add more about the needed improvements, even small ones. Maybe around user experience, I think one improvement could be the UX, which shows its age, as it has not changed for a bit, but other than that, it is fine.

I do not think JFrog Artifactory needs any other improvements. I cannot think of anything else apart from the UX that I mentioned already.

I have been using JFrog Artifactory for about seven years.

JFrog Artifactory is stable.

JFrog Artifactory scales very well for large organizations and is a nice enterprise solution.

I think JFrog Artifactory's customer support is good.

I have not used a different solution before JFrog Artifactory.

I do not have much experience with pricing, setup cost, and licensing, as it was set up by a different team.

My company does not have a business relationship with this vendor other than being a customer.

I have seen a return on investment with JFrog Artifactory. It saves time by being a standard go-to product that every team uses, which avoids different paths to explore and spend time figuring out different solutions. When many teams collaborate on one solution for artifact management, it results in significant time cost savings.

I would estimate that my teams have saved around 70% of the time compared to figuring out their own solutions and using custom-built team solutions.

I do not have much experience with pricing, setup cost, and licensing, as it was set up by a different team.

I have not evaluated other options before choosing JFrog Artifactory.

My advice for others looking into using JFrog Artifactory is to give it a try. Once you set it up, it is very easy to use and teach other teams how to use it. I would rate this product an 8 overall.

Private Cloud

Microsoft Azure

My main use case for JFrog Artifactory is that it serves as our main repository in my enterprise for storing dependencies and artifacts of any kind.

A specific example of how I use JFrog Artifactory for storing dependencies or artifacts in my day-to-day work is that JFrog Artifactory is the centralized repository. Any dependencies, whether third-party or internally developed, including JAR files, TAR files, and container images, are stored in JFrog Artifactory.

We have the entirety of the JFrog solution that includes X-ray as well, the scanner. Whatever we have stored in JFrog Artifactory is also being scanned by X-ray. JFrog offers other modules as well, including Curation, which acts as a firewall and prevents unwanted packages from entering into our environment.

The best features JFrog Artifactory offers include it being the main centralized repository, and additionally, the indexing by X-ray helps scan the artifacts as they are pushed into JFrog Artifactory. Other valuable features include role-based access control and continuous scanning of the artifacts. Overall, the cloud storage capabilities are robust and helpful.

When I say the cloud storage capabilities are robust, I can tell you that our enterprise is pretty large. We have several artifacts in the centralized repository, and these artifacts can amass to several millions. The tool withstanding that amount of load is impressive.

JFrog Artifactory has positively impacted my organization by enhancing software supply chain security, and that is the biggest takeaway. Mature organizations with a more mature security posture are expected to have a centralized repository. JFrog Artifactory does exactly that. It helps us store the artifacts there and prevents developers from using unwanted packages, which might end up being malicious, vulnerable, or otherwise problematic.

I believe JFrog Artifactory can be improved; it is not specifically with JFrog Artifactory, but overall, the entirety of the JFrog solution. Our enterprise is pretty large and it is a demanding ask. However, because of the sheer size and magnitude of artifacts that end up getting stored on JFrog Artifactory, the tool tends to be bogged down and most of the functions also get bottlenecked because of that. The scanning sometimes gets pretty slow because there are so many artifacts that are being scanned simultaneously. The APIs get slow to respond. That is precisely the level of issues we have with respect to throttling. Any tool for that matter would struggle under this amount of pressure or load being put on it. JFrog, however, claims to be one of the best enterprise solutions for software supply chain security and if it cannot tackle an organization this size, then that tends to be problematic.

I have been using JFrog Artifactory for almost two years.

JFrog Artifactory is stable in my experience.

The scalability of JFrog Artifactory is good and I have no complaints on that, but sometimes the load tends to slow down.

Customer support for JFrog Artifactory is good.

Previously, we used Sonatype Nexus as a different solution, but I am not aware of the reason we switched.

My advice to others looking into using JFrog Artifactory is to go ahead. I have no complaints. I would rate my overall experience with JFrog Artifactory a 9 out of 10.

Currently, we are using JFrog Artifactory to store artifacts and we are using JFrog to scan those artifacts by using Xray scan.

JFrog Artifactory allows you to create many repositories. Currently, I'm using Docker, so it is very easy for me to create repositories in Docker and we can push Docker artifacts to that repository and pull and use them. It's very straightforward.

Currently, we have many different repositories created in JFrog Artifactory. We use them for versions. We can create multiple repositories and store artifacts in different packages.

I haven't experienced any downtime for JFrog Artifactory because we are currently operating at a small scale with small units stored in JFrog Artifactory, not a large volume. This is why we haven't faced any issues yet.

Currently, I'm using Docker and Kubernetes for JFrog Artifactory. My Docker creates images and pushes them to JFrog Artifactory, which stores my artifacts and images. When it comes to pulling, my Helm chart retrieves those artifacts and deploys my newer version if I change anything. It's a full CI/CD process that I'm using currently.

JFrog Artifactory gives us metadata and policies. We can use those policies in our scripts to pull images and artifacts from JFrog Artifactory repositories for use in our deployment process.

Whenever we push artifacts to JFrog Artifactory repository, they are automatically scanned. We just need to enable that feature and it will provide vulnerability scan reports.

JFrog Artifactory provides metadata. Whenever we access the UI and open those artifacts, it gives you information about which version is present. You can also tag those versions in your script file and we can automate that dynamically to create those tags in the script.

JFrog Artifactory also provides authentication features. If we are using JFrog with an enterprise grid, we can invite users who can access the same repository. We can add those users so they can access and view the repository. We can also share repositories with others and generate authentication tokens there. It provides encrypted tokens.

Even when creating any application, there are parts we need to focus on. Currently, JFrog Artifactory doesn't have AI features, which is an area they could update in the future.

I have been working with JFrog Artifactory for almost two years.

I haven't used any technical support yet. However, when my organization purchased the enterprise grid, they handled some vendor-related activities.

When we started using JFrog Artifactory, it provided a 14-day trial feature. We could try JFrog Artifactory and check whether it would be suitable for our organization and test those features. It also provided a server for the 14-day trial. When it came to the enterprise grade, we were previously using Nexus and we migrated from Nexus to JFrog Artifactory because JFrog Artifactory offers more features than Nexus.

The offerings are almost the same in both JFrog Artifactory and Nexus in some aspects. I cannot define those differences precisely right now, but there are some distinctions. Vulnerability scanning is one feature where they differ, and there are some other differences as well.

I think the setup process in JFrog Artifactory is pretty simple and straightforward. If we know what we want to implement, we can configure those things there. Even if we are using JFrog CLI, we can use some commands and it will create things for you. That is pretty simple.

Nexus is an alternative solution to JFrog Artifactory.

I think JFrog Artifactory is good whether we use it for small scale or large scale operations. It provides features that allow you to store artifacts and have vulnerability scanning for your artifacts, which is a good feature. I would rate JFrog Artifactory as an eight because I have invested significant time in it and have accomplished many things with it.

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.

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.

Public Cloud

Amazon Web Services (AWS)

I primarily used JFrog Artifactory for package management because it provides a remote proxy and caching that helped accelerate our release lifecycle and made our releases much easier and faster. We were uploading all of our artifacts to this repository, and it provides build tools like Maven. Since we were using the JBoss application, uploading our JAR and WAR files directly to JFrog Artifactory really helped us deploy applications in a fast manner.

Earlier, we used Git and GitHub for our version control system and uploaded all our JAR and WAR files directly to GitHub. However, when we discovered that JFrog Artifactory is a tool that helps in directly uploading all those artifacts, the versioning feature stood out to us. It also provides the build tool within itself, so we do not need to build the source code individually or on another system. As soon as we discovered these features in JFrog Artifactory, we switched to using it, and it has helped us with many releases. For example, if we deployed the application today on version one and the artifacts for version one are already stored in JFrog Artifactory, after a couple of days if we need to add other features, it will be version two and we need not commit again and again or undo the commit to get the release. We simply deploy our changes and version two is created in JFrog Artifactory, and if we want to roll back those changes, we can simply click from version two to version one and the changes are rolled back.

We have used JFrog Artifactory in a limited capacity, as they have many other features but that depends on their cost and models. We used the free version earlier and then moved to the Pro version.

JFrog Artifactory offers many features, but the system's stability stands out the most to me since whenever we want to use those artifacts, they are almost always available globally. I work from India and my colleagues work from Europe or the USA and they can also access those artifacts without issues. It is a great tool for managing binaries across different environments such as Development, QA, Prod, Non-prod, and SIT, handling all those environments without issues, and it provides very good security and access control if we are using the Pro version, allowing us to monitor who made changes and when. We started using it with Docker, which also helps in managing and distributing our Docker images, so that is a very great feature.

In our day-to-day operations, we were using Docker since we had the chance to containerize our application, so we were using a repo server to manage all those images. However, JFrog Artifactory has the feature to keep those images within itself, making it very easy for us to keep all those images in one place. This was the feature we started using once we discovered it. Regarding security and access control, we can easily trace changes. For example, if I work on an application today and make changes, a version one is created for this application and tomorrow a colleague makes some changes creating version two. My manager can easily identify the changes I made versus my colleague's changes with the help of traceability and access control on the system.

Since we started using JFrog Artifactory, we no longer manage our own server just to keep source code and binary code. All our binary repositories are in JFrog Artifactory, so I can say it is a very good repository manager. This is where we can store all our artifacts without needing to find another server or directory, and it is very easily accessible so we do not have to wait for JFrog Artifactory to be down or for our application to be unavailable. We have not experienced that. Another very good feature is that it provides a proxy for remote applications, enabling us to use build tools not available within our intranet, allowing us to compile our source code into binary code easily, which has transformed our release process to be faster.

I think JFrog Artifactory already has good features and has evolved since its inception, incorporating AI/ML and supporting Kubernetes and Docker images. JFrog Artifactory is progressing well, and I do not see many other features according to my requirements that are needed. I would only suggest that the cost could be reduced for older customers or made consumption-based so that more people can opt for it.

The user interface can be better. It has evolved significantly since I started using JFrog Artifactory, so it can improve further.

I choose a nine out of ten because, as mentioned in my previous answer, it sometimes faces performance issues due to slow repository response and long download times for artifacts, which affects overall performance. That is why I feel it needs improvement to achieve a perfect ten.

I have used JFrog Artifactory for over one year in my previous company.

In my experience, JFrog Artifactory is stable and available for usage most of the time globally. It does have performance issues at times for certain locations, but overall, it has been a very good experience.

We have effectively used JFrog Artifactory within a good team of ten to twelve people. However, there are performance issues when two or three people are using it simultaneously for our code, which results in performance lags.

We have utilized support from JFrog Artifactory, which has been very good. Opening a ticket on the customer portal yields a response, especially if using the Pro or Enterprise version. For the free trial, support is unavailable, but the assistance we received was fast and helpful, with easy tracking of ticket status and history on their portal.

We previously used Git and GitHub, which just stored repositories and artifacts, but without the additional features JFrog Artifactory offers. Hence, we switched from GitHub to JFrog Artifactory.

The setup was very easy, and the setup cost was reasonable. We started with a free trial before moving to the Pro version, which costs around one hundred dollars per month for us. We had a ticket with the support team, who provided very good discounts, although I cannot share the specifics, but they were very helpful.

We have definitely seen a return on investment in terms of savings. Money has been saved through intelligent utilization. It has positively impacted both money and time saved, facilitating the downloading of artifacts and uploading of source code into a central repository easily. Thus, there is no need for more than two or three people to manage it, making it a cost-saving solution.

We have definitely seen a return on investment in terms of savings. Money has been saved through intelligent utilization. It has positively impacted both money and time saved, facilitating the downloading of artifacts and uploading of source code into a central repository easily. Thus, there is no need for more than two or three people to manage it, making it a cost-saving solution.

The setup was very easy, and the setup cost was reasonable. We started with a free trial before moving to the Pro version, which costs around one hundred dollars per month for us.

We have not tried any other options besides Git and GitHub. Once we started working with JFrog Artifactory, we never looked back.

If you want to minimize efforts in compiling source code without frequently downloading and maintaining a server, you should definitely consider JFrog Artifactory. They offer vast tools for different coding environments such as Maven and NuGet, and they are continuously evolving, adding support for Docker images and Kubernetes. Companies utilizing Docker or Kubernetes will benefit significantly from a reliable tool to compile their source code into binary code. I recommend trying JFrog Artifactory once because you will probably start loving it. I have rated this product as a nine out of ten.

Public Cloud

Microsoft Azure

My main use case for JFrog Artifactory is to store packages, usually Docker images, but I have also worked with teams who have used it to store their Maven packages and NPM packages. After I use a Dockerfile to build a Docker image, I use JFrog Artifactory to store the Docker image by pushing it from my local machine to Artifactory. When I create new versions of the Docker image, I also push those up. JFrog Artifactory is integrated with my CI/CD pipelines within Jenkins, where the image is pulled from JFrog Artifactory and then deployed with the application being built.

The best features JFrog Artifactory offers include its universal capability to handle any package format commonly used, allowing the front-end team, back-end team, and DevOps team to use it. I also appreciate the ability to attach custom properties to artifacts, which I have used to tag artifacts as approved for production release or whether they have been scanned by certain software as true or false.

Universal package support and custom properties help my team day-to-day by making us more efficient, as we have one tool with which all engineering teams can interact regardless of their team. This makes it very easy for people to find the artifacts, understand the naming conventions, and know where they are stored, saving us time and reducing complexity in managing different packages and potentially different artifact storage systems.

JFrog Artifactory has positively impacted our organization by making us more efficient through using one system for storing all binaries and artifacts. It has also enhanced security because of the tagging feature, ensuring that in our CI/CD pipelines, artifacts will only get deployed if they are marked with the correct tags, providing an additional check to prevent deployment, which is beneficial.

In terms of improvement, self-hosting JFrog Artifactory can be complicated since you have to manage the database and storage back end as well as upgrades, which requires a bit of a learning curve. Although I find the documentation quite useful from JFrog, it is still complicated, and the licensing complexity is definitely a problem as it is somewhat expensive compared to other artifact storages like Amazon ECR or Google Artifact Registry.

While the integrations are very good, which I consider a benefit of using JFrog Artifactory, the maintenance toil can be a little complex, especially if you are using a self-hosted tool. Your team would have to be responsible and understand that it will require significantly more effort than using a cloud-managed storage solution.

I have been working in my current field for five to ten years.

JFrog Artifactory is stable and is a well-built piece of software. If you are self-hosting it, you also have to ensure that you maintain its stability with frequent updates and by correctly managing the machines, so it is a mutual effort required to maintain stability both from JFrog and the entity hosting it.

In terms of scalability, JFrog Artifactory is very good, as you can host it on a gigantic machine if you need to use it in a very powerful way or you could deploy it on a very small machine if you want to use it only for one engineering team.

I have never interacted with customer support before since I have never had to. However, their documentation, which I suppose falls under that umbrella, is very good.

Neutral

This was the original solution I used in this category of artifact storage systems. Although I also used Google Artifact Registry at the same time, I find it a little easier and cheaper. The limitation of using that solution is that it works very well with Google services but can be more complex when linking to other clouds. JFrog Artifactory is more useful as a multi-cloud system. If an organization were to use only one cloud, they would probably prefer the cloud provider's own artifact storage system.

We have seen a return on investment. However, I would not say that it saves time, as it is more of a necessary feature for modern engineering teams to have a certain place to store their artifacts. In terms of money saved, I would say this is not really applicable since it does cost money to use and does not save money. It is more of a necessary tool than one that generates a financial or speed-related return on investment.

The price is expensive. JFrog Artifactory is a best-in-class solution for artifact storage, so it is going to be more expensive. However, I do believe it is quite expensive for what it is, especially considering the many alternatives available in the marketplace right now.

The complexity of managing the tool and the high cost prevent it from being a ten, especially considering there are cloud-native alternatives that are cheaper and easier to use. Those alternatives may be specific to a particular cloud provider, making it more challenging to link with other clouds, which is something that JFrog Artifactory does well. However, it still remains more expensive and complex to use.

I would recommend that if you are using multiple clouds, JFrog Artifactory could be a good tool to use. If you are using just one cloud, you probably would not want to use it since it is more complex to set up and can potentially be more expensive as well.

From a security perspective, organizations can quite easily see a ten to twenty percent improvement in overall security because of deploying only secure artifacts that have been tagged correctly. Organizations might even see better security improvement. I would rate this review a nine.

Public Cloud

Amazon Web Services (AWS)

My main use case for JFrog Artifactory involves integrations such as Amazon RDS, Amazon EBS, and CloudWatch, which generally help me improve our processes. It is a standard artifactory that helps me scale enterprise workloads, especially when building high-volume pipelines or managing multiple projects to ensure reliability. Additionally, DevSecOps automation is where JFrog Artifactory comes into play.

A specific example of how I use JFrog Artifactory in one of my projects involved a project where we had several artifacts on our AWS S3 buckets and a hard dependency on AWS's security competency. JFrog Artifactory helped us achieve that because it has DevSecOps automation capabilities that assist in preventing modern-day supply chain hacking and cybersecurity attacks, making those integrations with AWS particularly valuable.

I do not have anything else to add about my main use case or that project without breaching confidentiality.

JFrog Artifactory has positively impacted my organization by initially being part of two vertical divisions and later extending to four or five verticals as positive stories were shared. Thus, it has had a good positive impact.

As for specific outcomes about the positive impact from expanding to four or five verticals, we have achieved faster deployment speeds, faster time to market, and lower pipeline failure rates. These were the key metrics monitored mostly by the DevOps and NOC teams, while I had limited involvement with those metrics.

The best features JFrog Artifactory offers include automating dependency management, which reduces the risks of failing builds and broken builds, making the CD pipelines much smoother when I use JFrog Artifactory with AWS CodeBuild or EKS-based CI runners. I really appreciate the security competency aspects because it helps me ensure that JFrog Artifactory meets the requirements for the supply chain, and I find both of these features very useful.

Regarding improvements for JFrog Artifactory, I remember that the documentation was more focused on the on-premises JFrog version. I was mostly redirected toward that, so I found a lack of specific or clear documentation on using JFrog Artifactory with AWS. I felt this gap two years ago, and there were capabilities such as X-ray or integrations with other AWS features that I found lacking at the time.

I do not have much more to say about the needed improvements in integration or documentation, but I want to mention that, coming from a quality background, I think built-in quality gates for intelligent automation, vulnerability checks, or improved visibility and communication during slow responses or service downtime would be useful for visibility in distributed environments.

Looking back, I think the learning curve for JFrog Artifactory could be eased, and the installation process could feel less overwhelming. While it is not that difficult, I have seen new joiners struggle with the initial setup.

I think JFrog Artifactory could improve with some UX revamps since many tools these days provide very intuitive user experiences, and I believe that could be something to look into for the future.

I have used JFrog Artifactory across multiple jobs, so my total experience would be more than three years.

In my experience, JFrog Artifactory is quite stable.

The scalability of JFrog Artifactory was decent for our organization of 2,500 people, as we used it across three or four verticals, but my exposure to scalability was limited due to my QA background.

I do not recall using a different solution before JFrog Artifactory. It was JFrog on-premises, and later, JFrog Artifactory on AWS, which is all my organization has used.

Before choosing JFrog Artifactory, we evaluated some options such as GitLab packages, which were somewhat relevant, and I would not say they were direct alternatives. We also used a package repository called Cloudsmith and Docker Hub at different stages. While these might not be direct competitors, they were tools I have used.

My advice for others looking into using JFrog Artifactory would be to work with someone who has previously set up and onboarded themselves on JFrog Artifactory because it might be challenging for first-timers. I would not recommend the official documentation but suggest using blogs or guides created by the community instead. For places such as banks or financial institutions, I would recommend ensuring there are more security features as security is a high focus. I would rate this product highly based on my overall experience with JFrog Artifactory.

Public Cloud

Amazon Web Services (AWS)

JFrog Artifactory is designed for software management. We used it for storing all assets and packages that were downloaded from external package systems, making them available for our development teams to use in their builds.

It primarily supported a fail-fast approach, where vulnerabilities were identified ahead of time, enabling us to alert our development team to use the latest packages without those vulnerabilities.

JFrog Artifactory proved very helpful in supporting a variety of package types for different projects.

The best features of JFrog Artifactory include the core functionality of package management and software management, along with scanning capabilities to prevent vulnerabilities from being introduced.

The metadata management feature was particularly useful for managing packages within JFrog Artifactory.

We utilized Xray integration with JFrog Artifactory, which was instrumental in managing vulnerabilities overall.

JFrog Artifactory has robust functionality in terms of access control, which helped us ensure minimal access to various artifacts.

I would rate it eight out of ten because it is a great product that is widely used in the industry. It has excellent features from an artifact management perspective and maintains good integrations.

JFrog could improve this product with tighter integration capabilities.

I used JFrog Artifactory in the last twelve months.

I would rate their support for JFrog Artifactory as seven out of ten.

Positive

I am no longer using JFrog Artifactory in my current role as I moved away from the team. The metadata management features were very useful, particularly for managing packages inside JFrog Artifactory. We were customers of JFrog. Based on my experience, I would rate JFrog Artifactory eight out of ten.

On-premises

Amazon Web Services (AWS)

Our main use case for JFrog Artifactory is for Maven builds and Docker images as an artifactory support, so anytime someone builds an image or creates a Maven build, we upload it to JFrog Artifactory. We use it for Maven builds, Docker builds, and for our CI/CD.

JFrog Artifactory has positively impacted our organization by ensuring that unified package management is happening at a central place, which has been helpful for our distributed service mesh because otherwise, everyone would be rebuilding their own versions of the central package, eventually leading to problems. Unified management is welcoming. Better consistency across services is perhaps the bigger point we have seen as an outcome. Once a new service goes in or a new build for any existing service goes in, we are at least certain that it is picking up the latest code that we had pushed to the central package, ensuring that if an observability fix or security fix went in, we just need to ensure a rebuild happened on all services after that build and it is now reflecting.

The best features JFrog Artifactory offers are a unified store for our builds, Docker, and everything, and its integration into CI/CD, which is quite smooth for us as we are using it across several different repositories and two different package types including Go and Java Spring Boot. The integration of JFrog Artifactory into our CI/CD has been smooth because the documentation was pretty clear on how we had to do it. We did not have any issues since, and I was well aware of when it was happening, so the part that stood out was the clear documentation that I think was available online.

JFrog Artifactory could be improved for simpler workflows without dedicated infra teams or dedicated DevOps, as it could be difficult to configure. For us, it was slightly easier because we have a lot of senior engineers and people with CI/CD experience, but it could be slightly problematic in those cases.

I have been using JFrog Artifactory for about three months since we configured it.

JFrog Artifactory has been stable for us.

JFrog Artifactory's scalability has been good as we have 14 to 15 services today, and it will increase to maybe 30 over the next few months. We have not had any issues so far.

I have not interacted with customer support yet.

We did not previously use a different solution because we started with JFrog Artifactory itself.

JFrog Artifactory is deployed in our organization on our VPN right now, so it is on the private cloud.

I was not involved in the licensing and pricing part, but the setup cost was I think one or two days, so whatever engineers were required, it was about the specific pay for those two days.

The return on investment has been more on productivity, although we have not quantified it, so there are no metrics to share.

I was not involved in the licensing and pricing part, but the setup cost was I think one or two days, so whatever engineers were required, it was about the specific pay for those two days.

Before choosing JFrog Artifactory, we evaluated Nexus and JFrog, but we realized JFrog Artifactory is slightly better because Nexus does not provide that smoother experience for CI/CD and for Java Maven builds, so that is why we went ahead with JFrog Artifactory.

If you have some Maven builds or Gradle builds and Docker repositories that you want to store at a central place, JFrog Artifactory can be a solution one can look into. It should be one of those options that you evaluate. I give this review an overall rating of 9.

Private Cloud

Amazon Web Services (AWS)

I have been using JFrog Artifactory for the past three years. We primarily use JFrog Artifactory for storing our Docker images, packages, artifacts, and other files that we use in our applications on a day-to-day basis.

In a CI/CD pipeline, there are several workflows and steps where you bundle your code as an image or as an artifact so that you can use it for running the image in containers using Kubernetes. In those stages of the CI/CD pipeline, we generally build the image using Docker. After building the image, we need to store it for future usage. For storing the images that we build using those CI/CD pipelines, we use JFrog Artifactory in our current environment.

One of the greatest features JFrog Artifactory can offer is RTFS, which stands for Real-time Federation Service. If we have two Artifactorys in two different regions, RTFS is the feature of JFrog Artifactory that can help us in federating those two repositories in real time. It is definitely helpful and it reduces the latency, and it is a brilliant feature that I love.

We have teams working across the globe. For that reason, if we have JFrog Artifactory in a single region, the people who are working far away from that region will definitely face latency. For that reason, we have deployed JFrog Artifactory in two different regions so that our users can access it without any latency. If we have two Artifactorys in two different regions, there will be a gap in data or a data mismatch between two JFrog Artifactorys. What the RTFS feature does in JFrog Artifactory is that it federates and syncs all the data from one repository to the other repository and vice versa. It always maintains the same data in both repositories and in both JFrog Artifactorys. This feature helps us in syncing these two repositories all the time without any manual effort, and it happens in real time within seconds.

There are many features such as user management, administration, and service management and different types of features, but this one is the most standout feature.

The most significant thing that I can express is that upgrading JFrog Artifactory to the new version is a bit cumbersome and it is a tedious process. If that could be addressed, then everything would be very smooth and very useful. There are no other challenges except this.

I have been working in this same field for the past three years.

JFrog Artifactory is pretty stable.

It is very scalable. I would rate it a ten out of ten.

Customer support is brilliant.

Positive

I can share that JFrog Artifactory definitely has a very good return on investment and it has saved a lot of time, money, and manpower, though I cannot share the specific numbers.

The pricing is very competitive and fits well within our budget.

JFrog Artifactory has definitely impacted our organization in a positive manner. Approximately 20,000 people or 20,000 pipelines use JFrog Artifactory on a day-to-day basis. If we had any other tool instead of JFrog Artifactory, it would definitely not work as effectively as JFrog Artifactory.

Most of the time JFrog Artifactory saves time because it is very fast. The tools, the services, and the features it has are really brilliant and no other tool can match it. If you are a large organization with multiple JFrog Artifactorys in multiple regions, JFrog Artifactory with RTFS is a brilliant tool. I would rate this review a ten out of ten.

On-premises