As an engineer, engineering supervisor, or CTO, you are responsible for making architectural decisions that help your team create innovative products and optimize technology costs. The type of architecture you select affects how much control you have over data, infrastructure, and customization options. The Software-as-a-Service (SaaS) model is one of the major architectures you can use to deliver services to customers — anytime and anywhere.

More enterprises continue to adopt managed data integration services like AWS Glue. According to the Data Pipelines Market Study Report, 65% of organizations now prefer cloud-based or hybrid cloud data integration solutions. And how exactly do they stand to benefit? This article explains the craze by covering AWS Glue in detail, which happens to be one of the most popular cloud data integration services today. We’ll cover.

When it comes to September’s cloud cost news, cloud-based organizations will have to take a little bit of sour news along with the sweet: Cloud costs continue to rise with inflation, and IBM cloud service users will soon see price hikes, but AWS and Azure have both recently released updates that may help you keep your costs under control. Read on to see how these changes may affect your company in the coming months.

Measuring and monitoring unit economics can help your SaaS brand make informed business and engineering decisions. But how do you get that data and what exactly are SaaS unit economics? We’ll cover exactly what SaaS unit economics are, metrics you should monitor, how to calculate your unit economics, and the tools you can use to be successful.

Getting to cloud efficiency is both an art and a science. The art has long been CloudZero’s specialty: giving engineers complete visibility into the costs associated with their cloud infrastructure, putting it all in a business context, and helping them make proactive decisions about how to build and scale efficiently. In other words, the art is on the human side. Giving human engineers the visibility and context to understand and control cloud costs.

It can be challenging to translate complex engineering concepts to business leaders. If you frame things well, though, you can have more productive conversations that lead to greater alignment between engineering and the rest of the business. With that in mind, I’d like to share some of my recommendations for discussing cost with your leadership team — and hope it might help you have stronger cost conversations.

Implementing any cloud development project can be tricky, and frustrating. Especially when you are pressured with time, reactive approaches, or cost-saving scenarios. However, there are some things you can do to implement solutions in your cloud architecture for long-term scalability and risk mitigation. Rather than short-term fixes until it arises again, consider designing your cloud with failure in mind, or speculating worst-case scenarios. It might sound counterintuitive or obvious.

Pricing matters. Charge too little and you won't earn enough revenue to stay afloat. Charge too much and you could lose thousands in potential business. You don’t want to price out some customers. So what can you do? This is where tiered pricing comes into play. The SaaS pricing approach can be helpful for SaaS companies to meet the needs and budgets of each of their different customer personas.

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.

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.