Operations | Monitoring | ITSM | DevOps | Cloud

Like most SaaS companies, you’re under pressure to ship AI-powered features faster, smarter, and at scale. For many teams, that pressure leads to relying on external AI platforms, managed models, and third-party APIs instead of building everything from scratch in-house. At first, it feels like a win. Your team ships an AI-powered feature in weeks instead of months. No GPU clusters to manage. No models to train. No infrastructure to babysit.

Most executives assume that moving to Microsoft 365 simplifies cost control. Storage is “in the cloud”, usage is elastic, and governance is handled through policy. In reality, many organisations face a very different experience. They invest heavily in retention policies to meet legal and regulatory requirements, yet their SharePoint storage costs continue to rise year after year, even after large cleanup programs.

Master the 2026 multi-cloud operating model. Bridge the complexity gap to enable autonomous AIOps, edge convergence, and shift-left security.

This report analyzes cloud provider status data for January 2026, covering 12 major cloud platforms: AWS, Azure DevOps, DigitalOcean, Fly.io, Heroku, Linode, Microsoft Azure, Netlify, Railway, Render, and Vercel. The data includes official incident reports from each provider's status page and early detection capabilities from IsDown's monitoring system.

Enterprises operating at cloud scale today face a growing reality: managing infrastructure performance and cost in silos no longer works. Kubernetes, multi cloud environments, and GPU accelerated workloads deliver immense agility and capability, but they also introduce complexity that outpaces traditional monitoring and cost governance approaches.

For most of the last decade, the direction of cloud strategy was clear: standardize, consolidate, and reduce sprawl. Engineering teams worked to pick a primary cloud, reduce vendor dependencies, and simplify their stacks. FinOps teams unwound years of fragmentation. Platform teams built guardrails to make sure it didn’t happen again. Then AI arrived, and it’s a fundamentally different class of workload. AI demands specialized hardware and, increasingly, diverging providers.

S3 object storage is a popular storage for businesses and enterprise who need rapid access to data, and large amounts of storage not available with traditional file storage. If you’re interested in learning more about S3, we cover how the S3 protocol works, services offering object storage, and how they can meet your use case.

When I joined Kubex last year, the company was already well aware of the growing power of Large Language Models. As a company focused on intelligent resource optimization for Kubernetes, GPUs, and cloud infrastructure, generative AI didn’t feel like a threat so much as a natural extension of where the industry was heading. Kubex had already invested heavily in machine learning, but it was becoming clear that foundation models could unlock an entirely new class of capabilities for our customers.