Operations | Monitoring | ITSM | DevOps | Cloud

When your Microsoft 365 tenant reaches the SharePoint storage limit, the impact is immediate. File uploads start failing, Teams sites stop provisioning, indexing slows down, and storage overage charges begin applying automatically. For organisations storing large volumes of documents, drawings, media files, or project data, hitting the SharePoint capacity threshold can become a recurring and expensive problem—especially when underlying retention policies prevent deletion.

This blog is based on our research done as part of our white paper, The Cost of Cloud 2025 - India Edition. Research was conducted with over 600 Indian tech professionals to create a comprehensive analysis of the Indian cloud market.

When Azure Front Door began to fail on October 29, 2025, hundreds of downstream services, including Microsoft 365, Teams, SharePoint, and Azure SQL, went dark. While Microsoft didn’t publicly acknowledge the issue until 12:35 PM ET, StatusGator dashboards were already lighting up nearly 50 minutes earlier. StatusGator notified its subscribers of an Azure outage 42 minutes prior to the official status page at 11:53 AM ET.

Imagine you are building an e-commerce app. Every time a customer places an order, a lot happens behind the scenes. For example, you need to charge their card, update inventory, create a shipping label, and send a confirmation email. You could try to write one giant program that does everything in the correct order, but that quickly becomes a tangled mess — especially if something fails halfway through (say, payment succeeds but inventory update fails).

For years, enterprises have turned to Oracle Cloud Infrastructure (OCI) for what it does best: powering mission-critical applications with unmatched performance, security, and predictable economics. OCI has historically staked its reputation on being the go-to platform for organizations running complex, data-intensive workloads, from core databases and ERP systems to large-scale compute clusters, while putting extra focus on security and predictable pricing.

The Problem: AI workloads are dynamic, unpredictable, and expensive. Data prep can choke your pipeline, training jobs hog GPUs without awareness, and inference, the most latency-sensitive phase, is notoriously hard to scale efficiently. Worse, traditional infrastructure tools treat GPU as a static commodity, ignoring model intent, workload shape, and sharing capabilities.

Almost all companies have some kind of online presence, and if yours doesn’t, it definitely should. In order to make a splash online, your business needs content, webpages, photos, documents, PDFs, videos, you name it. How do you keep track of all the various media? Where do you store it and back it up? What’s the most convenient and safe way to collaborate and share it with employees and freelancers? Enter all-in-one cloud storage services for businesses like Dropbox and Box.

Getting started with monitoring for Microsoft Azure environments can be a lengthy and manual process. Many tools require users to create app registrations, assign permissions, and enable log forwarding or telemetry data collection across multiple portals and scripts. These fragmented steps slow down onboarding and introduce opportunities for misconfiguration, making it harder for teams to quickly achieve full visibility.