Operations | Monitoring | ITSM | DevOps | Cloud

Nearly every industry today uses AWS for different services. Developers, cloud architects, DevOps engineers, and IT teams all use it to provision servers, databases, and storage. However, doing this service by service and then wiring them together can get messy. That’s where AWS CloudFormation comes in to save time, enforce consistency, and lower the risk of misconfigurations. But beyond simplifying infrastructure management, one big question remains: at what cost?

Today, we’re making CloudZero even better with the launch of Dimension Studio, a major evolution in how CloudZero customers create and manage Dimensions — customizable “lenses” that allocate cloud and AI spend to relevant categories like products, features, teams, or customers, without relying on resource tags. At CloudZero, our mission has always been to help organizations make sense of their cloud and AI spend.

When you build your network with APIs at its core, you give your business a competitive edge. Here’s how to do it. Application Programming Interfaces (APIs) have become ubiquitous with modern networks for good reason. As companies use more service providers, endpoints, and software platforms, APIs help them get the most possible utility from their data with the least possible effort.

Over the years, Chaos Engineering has proven its effectiveness time and time again, uncovering risks and saving companies millions they would have lost in painful, brand-impacting outages. But as Chaos Engineering adoption increased, we found organizations running into the same stumbling blocks when they tried to scale. Individual teams would get great results with Chaos Engineering, then stall as they tried to get more teams involved.

Redis earns its place in modern stacks because it’s an in-memory data store with microsecond latency and rich data structures, making it perfect for things like caching, sessions, and rate limiting. Since it often sits on the request path, small issues (connection churn, blocked commands, memory pressure) can quickly ripple into user-visible incidents.

Extending Large Language Models (LLMs) with custom tools has become increasingly valuable in today’s AI landscape. Model Context Protocol (MCP) servers provide a standardized way to connect external tools and resources to LLMs. This can enhance their capabilities beyond basic text generation. While thousands of pre-built MCP servers exist, creating your own allows you to address specific workflows. You can implement use cases that off-the-shelf solutions cannot handle.

For a long time, public cloud has been the default answer to scaling infrastructure, but it's not the only path forward. As more teams weigh the risks of vendor lock-in, data residency, and dependence on US-based providers, the conversation around private cloud has taken on new urgency. However, building on private infrastructure doesn't have to mean sacrificing flexibility.