In today’s fast-paced cloud environment, understanding the pricing model of some of the best cloud monitoring tools like Datadog is essential in optimizing costs. With several features and adaptable pricing, clarifying Datadog costs and aligning them with your organization’s operational needs is vital for fully harnessing this robust monitoring tool. This guide simplifies Datadog pricing, offering a clear overview of its cost structure.

Serverless architectures have revolutionized how businesses deploy applications, offering scalability and cost efficiency by abstracting infrastructure management. This model, charging only for resources consumed, poses unique budgeting and cost optimization challenges due to its variable pricing structure. Navigating cloud costs in a serverless environment requires a nuanced understanding of leveraging this technology’s benefit while effectively managing expenses.

A recent Kubernetes Adoption Report showed that 68% of surveyed IT professionals adopted containers more during the pandemic. Among their goals were accelerating deployment cycles, increasing automation, reducing IT costs, and developing and testing artificial intelligence (AI) applications. But, what role does container technology play in this? This guide shares what containers are, how container orchestration works, and more.

Cloud cost management- Anomaly detection CloudSpend’s Zia framework, powered by AI, is designed to detect any unexpected spikes or irregularities in your cloud expenses. The Zia Anomaly Report aids in optimizing your cloud bills and protecting your cloud infrastructure from unforeseen issues. You have the option to share anomalies with your team through CSV, PDF, or email formats.

Apache Spark is an open-source, distributed application framework designed to run big data workloads at a much faster rate than Hadoop and with fewer resources. Spark leverages in-memory and local disk caching, along with Apache Spark is an open-source, distributed application framework designed to run big data workloads at a much faster rate than Hadoop and with fewer resources.

AWS’ Relational Database Service (RDS) is a pivotal component for businesses leveraging cloud technology for their database management needs. RDS simplifies a relational database’s setup, operation, and scaling, offering cost-efficiency while automating time-consuming administration tasks like hardware provisioning, database setup, patching, and backups. However, despite its benefits, navigating the cost structure of RDS to ensure efficient spending can be a complex task.

After watching part one and part two of our FinOps Automation Series, hopefully you’re chomping at the bit to adopt your first automation solution. In part three below, our experts — Erik Peterson (CTO and Founder of CloudZero), Alan Hand (Founder and CEO of Xosphere), Steven O’Dwyer (Senior FinOps Specialist at ProsperOps), and Mike Fuller (CTO of the FinOps Foundation) — are going to walk you through what you need to know before you take any concrete steps.

Access to critical business data, such as cloud cost data, is both incredibly important for finance, engineering, and DevOps teams, but also a privilege that should be managed carefully. This month, our engineering teams put a major focus on developing industry-leading capabilities related to access control, while also making it easier for people who require certain kinds of data to get it where it needs to be.

Many often wonder, “Is there a difference between observability and monitoring?” The thing is as IT environments have become more complex, monitoring alone has become increasingly less effective. That’s because while monitoring is crucial, it isn’t particularly suited to tracking unforeseen or unexpected turns of events. That’s what observability is meant for. This guide will clarify what observability and monitoring are – and how they differ.