Operations | Monitoring | ITSM | DevOps | Cloud

Cloud object storage powers a wide range of workloads, from AI training datasets to customer-facing media libraries. As your data grows into the petabyte scale, managing storage costs and ensuring reliability requires fine-grained visibility. You need answers to questions like: Which specific teams, services, workloads, or datasets are driving spend? Which data is cold and should be archived? What fixes will have the biggest impact on cost and performance?

Kubernetes is now the backbone of modern software infrastructure, helping teams deploy, scale, and manage applications efficiently across clouds. But when it comes to understanding costs, Kubernetes remains opaque. Teams often can’t answer basic questions like: How do you solve the gap between engineering usage and financial visibility? CloudZero’s new Kubernetes capabilities are built to address this challenge.

Data has exploded — and so have the challenges that come with it. Every click, transaction, and sensor ping generates mountains of data that traditional databases can’t handle. That’s why more than 94% of organizations now rely on cloud platforms, according to CloudZero’s 2025 cloud report. The goal isn’t just to store data, but rather, to make sense of it fast. And this is exactly where tools such as Google BigQuery step in.

As companies race to ship AI features into production, FinOps teams everywhere are scrambling to keep up with the variability of their engineers’ new favorite toys. The rapid pace of AI experimentation is creating a new wave of unpredictable usage patterns and costs.

Last month was certainly eventful. (If you use AWS or Azure, you probably already know what we’re talking about). But in case you missed it, here are the must-see headlines from October 2025.

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.

On Oct. 20, the Internet woke up and seemingly chose violence. For more than 12 hours, Amazon Web Services (AWS) went down. From banking platforms to hospital communications to mobile ordering apps, digital services came to a screeching halt. The cause? Two programs are trying to write a DNS entry simultaneously, failing, and leaving the entry blank. Thus began the incredibly costly failure cascade.

Before 2022, Alex Hanna worked on Google’s Ethical AI team. Today, she’s the director of research at the Distributed AI Research Institute, a transition sparked by Google’s handling of a paper exposing AI’s growing environmental footprint. So, how bad is it, really? That depends on who you ask. Take Jesse Dodge, a senior research analyst at the Allen Institute for AI. Jesse told NPR that a single ChatGPT query can use as much electricity as keeping a light bulb on for 20 minutes.