Operations | Monitoring | ITSM | DevOps | Cloud

Code in production will break. Maybe a request takes too long, maybe it fails quietly, or maybe it works fine one minute and falls over the next. Logs can help, sure—but they don’t always show the full picture, especially when performance issues are involved. Elastic APM gives you a clearer view. It traces what your application is doing from incoming requests to database queries and everything in between.

Running workloads on Google Kubernetes Engine (GKE) delivers impressive scalability and flexibility. Yet, it can also introduce a tricky challenge: tracking GKE costs accurately. Remember, GKE costs rarely scale linearly. Overprovisioned nodes, idle autoscalers, and orphaned workloads can quietly balloon your bill in the background. And while GKE’s native tools offer some visibility, they often miss the full picture.

Sovereign AI ensures control over AI/ML data, models, and infrastructure, which is now essential for enterprises, regulated industries, and national interests. JFrog and NVIDIA have collaborated to deliver a secure, scalable solution for sovereign AI. NVIDIA provides the accelerated computing and AI software while JFrog ensures trusted DevSecOps and MLOps practices across the entire AI lifecycle, from model development and security scanning to deployment at the edge and in air-gapped environments.

In modern software development, different environments often have different compliance requirements. Your development environment might allow more flexibility, while production demands strict controls around security scans, testing, and code review. Environment Policy helps you codify these requirements and enforce them consistently.

Across the Asia Pacific region significant investment is going into new subsea cable infrastructure that will help sustain the long-term demand for AI. We’ve written a lot on this blog about the impact of AI on networks and how AI workloads require low latency, high-capacity data transfer. This in turn puts more pressure on existing network infrastructure and in particular subsea cable systems - which provide the global backbone for cloud platforms and data centres.

Infrastructure management isn't about taking sides. Too often, teams get pulled into “X is better than Y” debates that miss the bigger picture: your compute stack should serve your needs, not industry hype. A common decision point in the past has been the choice between bare metal or cloud hyperscalar virtualization. Nowadays, the answer isn't 1 or 0.

Megaport and AWS explore how Network as a Service (NaaS) transforms WAN design with cloud-native agility, on-demand provisioning, and GenAI-ready flexibility. Co-authored by: Rishi Katdare, Leader – AWS Core Networking & GTM, AWS Mokshith Kumar, Sr. GTM Specialist Solutions Architect – AWS Core Networking, AWS As enterprise architectures grow more distributed and cloud-native, traditional methods of building and managing Wide Area Networks (WANs) are reaching their limits.

To ease the path of enterprise AI adoption and accelerate the conversion of AI insights into business value, NVIDIA recently published the NVIDIA Enterprise AI Factory validated design, an ecosystem of solutions that integrates seamlessly with enterprise systems, data sources, and security infrastructure. The NVIDIA templates for hardware and software design are tailored for modern AI projects, including Physical AI & HPC with a focus on agentic AI workloads.

The combined solutions simplify infrastructure operations and accelerate time-to-value for AI, telecom, and enterprise computing workloads.