Operations | Monitoring | ITSM | DevOps | Cloud

When we first introduced the JFrog AI Catalog, it was our mission to provide the industry with a single system of record for governing the complex landscape of internal, open-source, and external commercial AI models. This foundational step was critical for enterprises to move from uncontrolled innovation to delivering AI with trust and confidence. However, the AI landscape is ever-evolving. The challenge for today’s enterprise is already evolving beyond simply managing a library of known models.

When you run Kubernetes at scale, one of the first challenges is understanding what the cluster is actually doing. Workloads shift around, pods restart for normal reasons, and traffic doesn't always follow the patterns you expect. Having clear signals makes day-to-day operations much easier. That's where kube-prometheus-stack helps. It brings Prometheus, Grafana, Alertmanager, and supporting components together as a single package.

In October, technologists from across the financial industry gathered in New York for OSFF 2025 where the general theme was clear: open collaboration has moved from promises to proof. Projects like Fluxnova and OpenGris showed how institutions can build shared, production-grade infrastructure. The Common Cloud Controls and AI Governance Framework demonstrated that regulatory assurance can be achieved collaboratively, not competitively.

An artifact repository like JFrog Artifactory is a cornerstone of modern DevOps. It stores binaries, versions, and release bundles — your complete “what.” But when audits or incidents happen, the question quickly shifts from what to how: “How did this artifact get here — and can we trust it?” If all you have is a warehouse of files, you’re left scrambling to reconstruct the story. You check pipeline logs. You pull test results. You cross-reference approvals.

Are your deployments getting stuck waiting for approvals? Your code is ready. Your tests are green. But your ServiceNow change ticket is still holding up the release. In most organizations, this isn’t a people problem or a process problem. It’s an evidence problem. Every release has to prove that it met the required checks — tests, scans, reviews, and approvals. But when that proof isn’t instantly available, everything slows down.

At a previous company, we had over 100 microservices. I’d make what seemed like a simple change to one service and deploy it, only to discover it broke something completely unrelated. A change to the user service would break checkout. An update to notifications would break reporting. We spent more time fixing unexpected bugs than shipping features. The problem was our test scenarios were too simple.

We are thrilled to announce a major update across our line of ODBC Drivers, introducing a graphical configuration interface for macOS and Linux, extended authentication methods, and compatibility with the latest database versions including PostgreSQL 18 and MariaDB 12.