Kubernetes, or K8s, is an open-source, portable, and scalable container orchestration platform. With K8s, you can reliably manage distributed systems for your applications, enabling declarative configuration and automatic deployment. Yet, K8s can be resource-intensive and costly, with a rather steep learning curve. But in 2019, a lighter, faster, and potentially more cost-effective alternative appeared: K3s. Still, K3s is not a magic wand that works for all Kubernetes deployments.

In the last couple years, the SaaS world has undergone a paradigm shift with regard to cloud spending. As macroeconomic conditions shift from “peachy” to “moldy,” as SaaS grows increasingly crowded, as company valuations face new pressure, and as venture funding cools down, companies are looking to reduce — or in industry jargon, “optimize” — their cloud spending as much as possible, as soon as possible.

The latest cloud cost news is primarily good news: Each of the major cloud providers released changes that could improve your bottom line: Dive into the details below.

Amazon Web Services (AWS) provides more than 200 services. Among those, Amazon Elastic Compute Service (ECS), Elastic Kubernetes Service (EKS), and AWS Fargate help deploy and manage containers. Choosing between these services can be challenging. They seem similar on the surface (and are all popular). But each offers unique benefits and limitations. In this guide, we compare the three services, discussing the best use cases for each, and helping you choose the best fit for your business.

According to Statista, the mass volume of data created, stored, copied, and consumed in 2020 was over 64 zettabytes (ZB), or about 64 trillion gigabytes (GB). This is expected to rise to 181 ZB by the year 2025. A large portion of this data is likely to be significant to your business. It can provide you with new insights that help you improve your product, communicate with consumers, and perform risk analysis. However, you’ll need the right tools to extract, sort, process, and analyze it.

The most successful brands are data-driven. Whether it's Google, Amazon, or TikTok, they all use data to inform their next moves. But here's the thing. It's easy to collect a lot of data. Making sense of all that data is often the most challenging part. You can do that in three ways. Scripting is one option. Here, your developers code custom data integration tools in Python and Java alongside technologies like Hadoop and Spark.

If you’ve done any shopping around for a cloud cost intelligence platform, you may have heard about the “single pane of glass” approach to presenting cloud cost data. It’s a descriptive phrase uttered by almost every cloud cost company in one form or another. We may not all say it in a similar way, but it’s the same concept across the board.

The best pricing strategy for your SaaS business will depend on your specific business model, target market, and competition. You’ll also want to test different pricing strategies to see which one works best for you. That said, feature-based pricing can be a very profitable way to price SaaS products. Here’s how it works. We’ll also include how to determine your cloud costs per feature so you can set profitable prices.

Amazon Athena is heralded for its simplicity, pay-per-query pricing, and speed. Others say Athena has its fair share of limitations, which could affect your overall analytics experience and quality of analyses. So, what is what? In this post, we’ll share what Amazon Athena does, compare it to Amazon Redshift, and share how to understand and control your Athena costs in AWS (with just one tool).

Does it seem like your cloud costs are always shifting with the winds? It could be because the cloud itself is in a state of rapid change and advancement. The best way to anticipate these changes is to stay on top of current cloud cost news trends.