Operations | Monitoring | ITSM | DevOps | Cloud

At its core, FinOps is a cultural practice that brings together finance, technology, and business teams to manage and optimize cloud spending. For SaaS companies, where cloud infrastructure is integral to operations, FinOps practices directly impact the bottom line. But as with any emerging discipline, the big question is: Is a FinOps certification truly worth the time and financial investment?

AWS is one of the most popular public cloud platforms, offering over 240 cloud services. Despite the cloud provider’s efforts to make its tools easy to use, managing the vast array of AWS resources and services can be challenging. For example, AWS environments require continuous monitoring to determine what changes need to be made to reduce costs, improve performance, and secure your systems. This is where AWS monitoring tools, services, and best practices can help.

At CloudZero, we drink deeply of our own champagne. We consider ourselves customer zero, and use our product to its maximum potential, to reduce wasteful spending and improve our unit economics. We even use our software to drive meaningful board discussions (with help from a slide template you can download here).

For data professionals interested in data modeling, artificial intelligence, and machine learning, Databricks offers a robust cloud data platform. The Databricks Lakehouse combines data lake and data warehouse capabilities in one architecture. This makes Databricks particularly suitable for building sophisticated data analytics, artificial intelligence (AI), and Machine Learning (ML) models. Plus, Databricks claims its lakehouse is up to 12X cheaper than traditional alternatives.

You can assess your company’s financial health using a number of SaaS metrics, depending on the type of business you are in. Among the most useful is the SaaS Magic Number. So, why is it called the SaaS Magic Number, and how do you calculate it? And why is it so important to track your SaaS Magic Number regularly?

Google Cloud Platform (GCP) launched in April 2008 with Google App Engine. This developer-centric Platform as a Service (PaaS) offering allowed developers to build and host web applications on Google’s cloud infrastructure. Initially, App Engine only supported Python, but in 2009, Google added Java support, offering more programming flexibility. In 2010, GCP expanded further with Cloud Storage, its second major cloud product.

AI costs can spiral out of control before you know it. One day you’re building an AI feature that promises to bring in a solid chunk of revenue for the company. The next day you’re obsessing over an astronomically high cloud bill that will significantly eat into your profits — or consume them entirely. To help you solve this problem, we brought in Jeremy Daly, Director of Research (and AI cost management guru) at CloudZero.

The Amazon Redshift data cloud provides a fast, secure, and widely accessible data warehouse solution. It is an ideal platform for performing complex analytics and processing large data sets. In addition to supporting multi-parallel processing (MPP), Redshift is also a type of Online Analytical Processing (OLAP) database. Yet, one of Redshift’s main selling points is its cost-effectiveness over alternatives like Snowflake and BigQuery. Is this the case? What is the actual cost of Amazon Redshift?

Not long ago, our co-founder and CTO, Erik Peterson, shared some insights on AI spending. He shared how AI costs currently fall under the write-off-friendly world of R&D. He also acknowledged why DevOps teams might feel it’s too early to start optimizing AI costs. As the saying goes, “Premature optimization is the root of all evil.” But after more than a decade of software development, Erik knows that eventually, research, experimentation, and big ideas need to deliver real returns.

Amazon Web Services (AWS) offers a myriad of virtual servers to cater to almost any need you can think of. And the pricing reflects these options. Let’s dive right into it.