Operations | Monitoring | ITSM | DevOps | Cloud

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The cloud landscape is no longer just about "getting to the cloud"—it is about mastering the complexity once you are there. For organizations using VMware Cloud Foundation (VCF), the stakes have never been higher. As infrastructure converges, the margin for error shrinks, and the need for precision grows. To understand how the industry is navigating these changes, we dive into the VMUG Cloud Operations and VCF User Experience Report 2026.

December has arrived. The change freeze is looming, and the holiday requests are likely piling up in your inbox right now. It is the natural time for you to look back at the last twelve months, not just to measure your team's performance, but to consider how much the game itself has changed. If you look at the trajectory of your industry this year, a clear pattern emerges. You didn't just face new technical challenges; you faced a genuine shift in what it means to manage a network. The old metrics broke.

Cloud migration is a multi-layer transformation involving infrastructure, CI/CD, governance, security, and cost management—not just application movement. Enterprises face unique migration challenges due to complex systems, parallel cloud operations, compliance requirements, and tool sprawl. Automation and standardization are critical to reducing risk, manual effort, and operational inconsistency during cloud-to-cloud migrations.

Knowledge graphs and RAG (Retrieval-Augmented Generation) are complementary techniques for enhancing large language models with external knowledge, and each brings unique strengths for DevOps use cases. While they are often mentioned together, they are fundamentally different systems, and combining them delivers far better outcomes than relying on either approach alone.

Every year is a big year for Bitbucket, but in 2025, we delivered transformative changes that cap off years of work to make Bitbucket Cloud the secure, scalable, cloud-first standard for large engineering teams around the world. Today, 15M developers build on Bitbucket, including all of Atlassian’s 10,000-strong engineering organization, and Bitbucket Pipelines runs more than 1 billion build minutes per month.

These days, engineering leaders find themselves caught between a rock and a hard place. On paper, AI adoption looks like an unqualified success. Developers are shipping more code faster than ever, pull request volumes are up, and teams report feeling more productive. Their leaders rush to LinkedIn to share their plans to scale adoption because their teams are just so much more efficient. But then, the incidents and bug reports start piling up.