When it comes to cloud costs, engineers have a particular reputation. The executive suite and finance department often share perplexing frustration about why engineers appear not to care about budgets and costs at all. They provide hard budget limits, yet engineers frequently surpass them, citing performance and user experience as reasons for the excess spending.

Did you know that Google Search changes how it works about 12 times daily? For example, the internet search giant made about 4,500 changes to Search in 2020 alone. Those changes involved running more than 600,000 tests. Most of us can barely tell how often Google updates because the modifications are subtle. But we can tell that something keeps improving. Many other leading tech solutions do the same, from your tiniest mobile app to big ol’ Apple products.

Kubecost and Cast AI promise real-time Kubernetes cost monitoring. Both deliver this to a good extent. For example, Kubecost provides cost data from within and outside a K8s cluster. It also shares the data in a granular format, such as cost by Kubernetes concepts like namespaces, pods, and custom labels. Cast.ai offers several similar capabilities, including cost savings recommendations, and is currently available for AWS, Google Cloud, Azure, and on-prem Kubernetes.

In the SaaS world, it’s an accepted fact that engineering teams care most about performance, while finance teams primarily care about costs. The rift between the two teams is so large it caused the birth of a whole new SaaS company department — FinOps — to help them work together. This division of goals often leads executives to wonder which path the company should take.

Kubernetes (K8s) adoption has exploded over the past few years. But it hasn't been easy to monitor, manage, and optimize K8s costs. To provide greater cost visibility into Kubernetes clusters and environments, Kubecost launched in 2019 while OpenCost debuted in 2022. OpenCost has several founding contributors. But it is Kubecost that developed the cost allocation engine that the OpenCost implementation uses. Kubecost also announced the launch of OpenCost on June 2, 2022.

Cloud waste has been costing companies billions of dollars for years now. In 2022, organizations spent roughly $490 billion on the cloud. Common estimates suggest that about 30% of cloud spend is wasted, meaning a whopping $147 billion of waste — the most in any year, ever. Organizations are set to spend nearly $600 billion on the cloud in 2023. Could you be wasting money trying to save money? Fortunately, cloud waste is preventable in most cases.

New Relic provides full-stack observability and monitoring. It provides almost every type of system monitoring under one platform. This includes monitoring tools for infrastructure, application performance monitoring (APM), synthetics, user, log, mobile, network, and Kubernetes components. DevOps, security, and business professionals use these capabilities to detect anomalies, analyze root causes, and fix software performance issues.

The conflict between engineers and finance is notorious. Finance thinks engineers just want to build cool products regardless of the cost. Likewise, engineers often feel their every move gets monitored by hawkish finance employees who seem to only care about costs. How can you, as a leader, encourage both teams to work together harmoniously? And what can you do to diffuse the situation when engineers feel attacked for their infrastructure choices?

MongoDB is a popular non-relational database among developers of distributed apps. This Not Only SQL (NoSQL) database system delivers versatility, high scalability, and a flexible querying model. MongoDB is also remarkable for its fault-tolerance, self-healing, and real-time analytics capabilities. But many companies also decry that MongoDB pricing is expensive and complicated. We’ll share how the MongoDB pricing model works.

As a leader in observability and application performance monitoring (APM), New Relic empowers engineers with a data-driven approach to planning, building, deploying, and running great software. Last month, we announced support for New Relic on the CloudZero platform. With this new functionality, customers can gain visibility into their New Relic spend, combine it with any other IT or infrastructure spend, and achieve a complete view of business dimensions — such as products and customers.

FinOps, or the practice of managing a company’s cloud computing costs, and DevOps, the process of efficiently building applications in the cloud, should ideally go hand-in-hand. Engineers with a streamlined development process and a cost-conscious mindset can produce functional products with great margins and a high potential for scalability. However, incorporating FinOps into the DevOps lifecycle, and vice versa, isn’t always easy.

Amazon SageMaker makes it easy to prepare data for machine learning (ML) and then train, deploy, and modify ML models. SageMaker is a fully managed service that automates much of the ML lifecycle. So, if you want a single partner to help you through all stages of your Artificial Intelligence (AI) lifecycle, SageMaker might be the answer. Perhaps more important for this post is the promise that Amazon SageMaker can reduce your machine learning model costs. But does SageMaker pricing reflect this?

There are almost as many reasons for learning FinOps practices as there are resources available to learn from. Maybe you operate a SaaS company, for example, or you're looking for a job in FinOps. The FinOps field grows in importance every time cloud services and their associated costs become more complex — which can feel like every single day. Regardless of your situation, you’ll need high-quality FinOps resources that offer up-to-date materials and in-depth information.