Operations | Monitoring | ITSM | DevOps | Cloud

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.

AI costs haven’t been a major factor in cloud computing — until now. For example, AI demands massive data processing and storage, such as for training Large Language Models (LLMs) and generative AI. Additionally, AI workloads require parallel processing, which traditional instances struggle to handle — forcing companies to invest in specialized (and expensive) GPUs to get the job done.

Cloud provisioning involves defining, setting up, and allocating cloud resources — such as compute power, networking, and storage — so they’re ready for use in your organization. Provisioning used to be slow and error-prone. Not anymore; it’s a streamlined, hands-off process now. But it doesn’t happen on its own. You need the right cloud provisioning tools to automate and optimize the process.

Amazon Web Services (AWS) offers a suite of cloud storage services. Among the most widely adopted are Amazon S3 and Amazon EBS, known for their robust scalability, performance, and flexibility for a wide range of workloads. However, AWS also offers Amazon Elastic File System (EFS). This is a serverless file storage service for workloads that need shared, scalable storage for use with AWS services and on-premises resources.

We’ve covered the fundamentals of FinOps in several guides on this blog, including FinOps 101, the FinOps maturity journey (as outlined by the FinOps Foundation), and more. But if you need a quick refresher, no worries. We’ve linked key guides on what FinOps is, why it matters, and how to make it work for you in “Related reads” in the next section.

Last week, the FinOps Foundation released its latest installment of the State of FinOps report. This year’s theme: Cloud+. Fortunately for all of us, Cloud+ isn’t yet another streaming service to add to an already infinite list. In the FinOps Foundation’s parlance, Cloud+ refers to all the non-public-cloud spending — e.g., SaaS, AI — that you’d want to group under the broad umbrella of “cloud spending.”