As technology advances and business environments become increasingly competitive, your DevOps team has to continuously improve your product. The challenge is to free up their time so that they can release new product features and improve existing ones. Automating repetitive tasks is one way to accomplish this. Manual approaches also tend to generate or miss errors, slow time to market, and fail to test and monitor system health quickly enough.

Ever since its release in September 2014, Kubernetes has been equally powerful and meme-able in the engineering world. For all the magic of its container orchestration and compute resource management, it’s also mysterious and, to many, confounding — especially when it comes time to pay for it. As we’ve written before, migrating to Kubernetes often means losing cost visibility.

You’re a savvy SaaS business leader, so you already know the importance of keeping costs low to maximize your margins. What you might not have considered, however, is how tracking unit cost in your cloud spend data can help you achieve optimizations far beyond what you’d get with traditional cost-cutting methods. Keep reading to learn how cloud spend unit costs can drive savings you never knew were possible.

When operating in the cloud, one of the key decisions to make is about which type of architecture to adopt for your business and customer data. This is because choosing cost-effective architecture is key to building profitable SaaS software. Single-tenant and multi-tenant cloud environments are the options to consider. Both types of architecture have security and privacy implications. There’s also the issue of cost, which differs significantly depending on the architecting model you adopt.

As many companies shift from traditional IT infrastructure to cloud computing, they are also rethinking how they handle cloud costs — from accounting to tax reporting. Computing costs are predictable and relatively fixed in traditional IT environments. An organization purchases computing capacity upfront and uses it over time. The total cost of ownership is fairly easier to calculate with this setup. By contrast, cloud computing operates on a pay-as-you-go basis, with no upfront payments.

Amazon’s Relational Database Service (Amazon RDS) offers a variety of database instances, which can be confusing at first. In this guide, we'll clarify what each RDS instance class, family, type, and size means in under 15 minutes. Let's start at the beginning.

Cloud computing is among the roles occupied by 9.1 million people in the tech industry. With “tech jobs” appearing in 255,000 Google monthly searches and tech job listings increasing by 25% in the U.S., which are the best cities to pursue a career in tech?

Amazon Web Services (AWS) offers over 200 fully-featured services. AWS Elastic Compute Cloud (EC2), Elastic Container Service (ECS), Amazon Lambda, and the AWS Simple Storage Service (Amazon S3) are some of the most critical services you should become familiar with. We’ve covered Amazon ECS vs. EKS vs. Fargate for managing and deploying containers before. In this guide, we'll explain how Amazon EC2, Lambda, ECS, and S3 compare and when you’ll want to use each.

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.

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.