Operations | Monitoring | ITSM | DevOps | Cloud

When evaluating CI/CD platforms, it can be easy to view them as commodities — interchangeable tools that accomplish the same basic tasks. But as development teams scale, small differences in platform performance can be compounded, significantly impacting development velocity and resource utilization. To better understand these differences, we conducted a head-to-head comparison between CircleCI and GitHub Actions, focusing specifically on performance at enterprise scale.

Jenkins is the backbone of many CI/CD pipelines, automating builds, tests, and deployments at scale. However, when handling high-traffic workloads, such as during peak development hours, large-scale deployments, or parallel builds and pipelines, Jenkins can quickly become a resource hog, leading to slow builds, queue backlogs, and even system crashes. Optimizing resource usage is essential to ensure smooth, efficient, and scalable performance.

CI/CD for generative AI applications presents unique challenges in model deployment, testing, and monitoring. Unlike traditional software applications, generative AI systems involve large model artifacts, complex dependencies, and specialized hardware requirements, making a sophisticated CI/CD pipeline essential for reliable delivery. As organizations embrace generative AI technologies, the need for specialized CI/CD solutions becomes critical.

For platform teams, success means empowering developers with efficient, reliable tools and workflows that accelerate software delivery. As organizations scale, platform engineering teams must balance developer autonomy with standardization while providing infrastructure that supports rapid, reliable software delivery. In today’s complex development environments, platform teams are crucial for maintaining productivity at scale.

JFrog has always prioritized universality, ensuring software development teams have true freedom of choice. Core to the JFrog Platform, JFrog Artifactory is the world’s most versatile artifact manager, natively supporting nearly 40 package types. After taking in valuable feedback from the developer community, we’re thrilled to discuss how we’re further expanding our universe with the support of Hex packages.

In today’s hybrid infrastructure landscape, migrating applications to the cloud unlocks significant financial and technological benefits. Whether internal or external, these applications require robust, efficient infrastructure. Cloud providers like Amazon Web Services (AWS), Google Cloud, and Microsoft Azure offer frameworks to help organizations build better systems.

Agile production has become the norm for software development cycles. The backbone for such a fast-paced landscape is the continuous integration and continuous delivery (CI/CD) pipeline. But merely depending on the CI/CD pipeline isn’t enough, even though the automated workflows give you a competitive edge. The pipeline needs to be optimized to function at its best. This is where monitoring your applications within the pipeline can be a game-changer.

We here at JFrog have long advocated for promoting – never rebuilding – release candidates as they advance across the stages of your SDLC. For many JFrog customers, that meant using JFrog’s “Build Promotion” capabilities. Now you can level up your CI/CD game with promotions using Release Lifecycle Management (RLM)! In this article we’ll show you why promotions with RLM are more simple, secure, and scalable than our legacy build promotion API.

So much of our modern software runs on containers. Containerized applications offer great flexibility for developers, but they also come with security risks. Container security is a holistic approach to managing risk in containerized environments. Securing containers requires a broad approach incorporating different tools and policies that enforce security in the software supply chain, infrastructure, container runtime environments, and other potential container attack surfaces.

Behavior-Driven Development (BDD) is a software development methodology in which applications are built to match the behaviors a user would expect from the software. An evolution of Test-Driven Development (TDD), BDD gathers user stories about how users expect applications to behave, then creates software tests to validate that their applications match this behavior. The BDD methodology utilizes specific language and naming conventions.

Unit testing is a software testing methodology that tests the behavior of individual functional units of code. Through unit testing, developers can verify that their code performs as intended. Providing an opportunity to catch bugs, validate the implementation of logic, and assess the quality of the code, unit testing enhances the quality of applications and preemptively identifies problems before they become major issues.

Enterprises should prioritize securing their software artifacts to protect intellectual property (IP), maintain compliance, and mitigate supply chain risks. A strong security posture requires a deep understanding of access management, distribution controls, compliance enforcement, and software lifecycle governance.

We are excited to introduce a new capability in Bitbucket Pipelines – Step Failure Strategies. This is the first of a set of new features allowing developers to implement more comprehensive logic and control-flow inside their CI/CD pipelines. Failure Strategies are designed to give you explicit control over how your pipeline behaves in the event that an individual step within the pipeline fails.

We are excited to announce the release of ARM builds in the Pipelines cloud runtime. Our release of Linux based ARM runners in cloud allows you to build and deploy software for ARM-based systems with all the benefits of our fully managed CI/CD platform. To use the new cloud ARM runners in your pipeline, make the following modifications to your bitbucket-pipelines.yml file.