Operations | Monitoring | ITSM | DevOps | Cloud

Microsoft Azure was a relative latecomer to the public cloud market: AWS preempted everyone in 2006, and Google GCP came next, in 2008. By the time Azure launched in 2010, the public cloud market had already grown to more than $24 billion. Despite being third to the party, Azure had a key advantage: The Microsoft Office Suite was (and is) a go-to choice for corporate collaboration.

MongoDB has evolved into the database solution of choice for developers looking to build efficient, scalable applications in the cloud. But, as when using any cloud-based infrastructure, using MongoDB introduces an additional layer of complexity to customers’ IT spend. This week, we announced support for MongoDB on the CloudZero Platform.

In the early days of SaaS companies, most engineering and cloud operations teams weren’t tasked with monitoring or optimizing cloud costs. In fact, it would have been unlikely for these teams to care about cloud cost optimization at all, let alone take measures to fix issues and look for opportunities. Today, SaaS companies that want to secure a competitive foothold and ensure long-term success have to care about cloud costs.

Amazon’s Elastic Compute Cloud (EC2) service provides a variety of virtual machines (instances) for compute workloads. Among those, Amazon EC2 M5 instances are among the most versatile. In addition, there are different types of M5 instances available. Each of these has its own best use case. In this guide, we share an in-depth look at M5 instance types, sizes, and when to use them.

“Perfect tagging” is a little bit like perfectly tuning a piano. It takes an enormous amount of manual effort to get right, it’s never truly perfect, and the slightest change in the surrounding environment can throw it out of harmony. The closer an organization wants to get to perfect tagging, the more engineering resources they have to invest in it — frequently at the expense of more impactful, innovation- and product-oriented projects.

Yet, the same flexibility that makes EC2 so appealing can also make it complex, confusing, and unnecessarily costly. A good way to understand the compute service is to familiarize yourself with EC2 instance types, and what the best use cases are for each. This guide will cover that and more.

Conventional wisdom states that SaaS engineers don’t care about costs. They care about building an optimal product, regardless of the dollar signs associated with it. As a result, the finance team feels they must corral the engineers’ efforts and constrain them to work within an agreed budget. In fact, the FinOps Foundation’s annual survey consistently ranks “getting engineers to take action on cost optimization” as the number one challenge experienced by FinOps specialists.

As a cloud product manager, much of our jobs center around creating KPIs (or OKRs, if you prefer) such as sales revenue, freemium conversions, or customer stickiness, and encouraging development teams to hit product performance goals. But what happens after those goals have been met? Do we create new KPIs and push forward, or do we take a step back and evaluate whether each indicator actually translates to higher profit for the business?

A good chunk of your Amazon Web Services (AWS) public cloud spending goes to the Amazon Elastic Compute Cloud (Amazon EC2) service. Because it is the default compute service on AWS, Amazon EC2 is key to building, running, and scaling your AWS-based applications. That also means Amazon EC2 pricing has a tremendous impact on your AWS budget. Understanding how the EC2 billing model works will help you control and optimize your AWS spending.

Amid the first bear market in over a decade, the world’s largest companies are facing intense pressure to cut back. Layoffs have made headlines, but cutting workforce is not a silver-bullet solution to surviving in a down market. True, companies tend to spend the most on personnel, but using layoffs as a first line of recession defense has myriad negative consequences for survivors, including reduced job satisfaction, reduced organizational commitment, and declining job performance.