Operations | Monitoring | ITSM | DevOps | Cloud

As your organization scales its applications across many different cloud and SaaS providers, it becomes more challenging to understand your costs. You likely receive your bill at the end of the month, meaning you don’t have real-time visibility into who’s spending what and which services or applications your teams are spending the most on. Changing service costs also makes it difficult to break down your costs and identify what is driving spend, leaving you unable to take action.

SRE, DevOps and Platform Engineering are important concepts in today's world of software development. There are dedicated teams to manage these areas, each with a unique primary focus, set of responsibilities, tools and metrics used to gauge their performance requirements. This article explains SRE, DevOps, and Platform Engineering, including similarities and differences, and, most importantly, how these teams help streamline modern software development, delivery, and maintenance processes.

This blog post is part of our data centre networking series.

Privileged Access Management (PAM) solutions have become increasingly popular due to their ability to address data security concerns with flexible access control. One essential area for employing PAM solutions is using them alongside database clients (from the simplest ones to full-fledged IDEs) to ensure safe access to databases. StrongDM is a leading PAM solution that supports modern software development infrastructures both on-premises and in the cloud.

GoMock is a powerful tool for generating mock objects in Go, making it an essential asset for developers aiming to write advanced unit tests. By simulating the behavior of real objects, GoMock allows you to test your code in isolation, ensuring that each component functions correctly on its own. This capability is particularly useful in a language like Go, where interfaces play a crucial role in defining the behavior of different components.

Machine learning (ML) models are almost always developed in an offline setting, but they must be deployed into a production environment in order to learn from live data and deliver value. A common complaint among ML teams, however, is that deploying ML models in production is a complicated process. It is such a widespread issue that some experts estimate that as many as 90 percent of ML models never make it into production in the first place.