As a cloud cost intelligence platform, we spend nearly every day talking to companies about their cloud cost — and there’s something we’ve noticed — people don’t generally have the warm and fuzzies for their cloud cost management tools. You know, the ones that have been around for a while, that you use to build reports about your cloud spend? We won’t refer to them by name. These popular tools have really given the whole category a bit of a bad rap.

Automation does not only eliminate the mundaneness of performing repetitive tasks, it can also help reduce errors and mistakes. Good cloud infrastructure encourages cloud automation. That should be one of the main motivators of implementing it. As ubiquitous as the idea of cloud computing has become in the last decade, there is still a lot of demystification to be done. People still don't fully understand its potential. Yes, it can save you money and time but how?

One of the biggest benefits of the cloud is that it gives engineering teams the freedom to deploy and iterate applications quickly. Unlike traditional IT environments where engineers require a series of approvals before embarking on projects, in the cloud, engineers can choose from several managed services and deploy them at the click of a button. This means your team can innovate faster and respond quickly to market demands.

Whether you've recently migrated to the cloud, or you've been operating there for awhile now, you've likely realized how challenging it can be to understand exactly where your cloud spend goes and what drives your costs. Using a cloud service provider can offer many benefits, like the ability to easily scale up and down based on need, but unless you're using the right services and tools, your costs can quickly spiral out of control.

Containers and serverless computing are two of the most popular methods for deploying applications. Both methods have their advantages and disadvantages. To choose the one that’s right for your business, you need to understand the pros and cons of managing your own containers versus using serverless services.

It’s been a great month for exciting insights about the role that engineering must play in driving profitable innovation.

Big data analytics help organizations use data to explore both new and improvement opportunities. Whichever cloud data platform you choose, there are two data storage technologies you will want to understand. Data warehouses and data lakes are the two dominant data solutions commonly used for defining how an organization stores, queries, analyzes, and reports on big data. This post will define what a data warehouse and data lake are, how they work, and their differences.

AWS tags are a bit like flossing your teeth every day — or getting eight hours of sleep a night. Everyone agrees they’re good habits that will make life easier down the road. But sometimes life gets in the way, and those habits fall a little short. Most teams set out with the intention to tag their infrastructure, but in our experience, it’s rare that companies have a perfect, thorough AWS tagging strategy.

Like a lot of SaaS companies, the CloudZero engineering team uses our own product — or as many would say, we “eat our own dog food” and “drink our own champagne”. So, when we built and released our cost per customer functionality last year, it was only natural that we were our own guinea pigs.

One of the most important things SaaS companies need to think about in order to be successful is financial modeling. To succeed in the increasingly competitive SaaS space, finance teams need to carefully consider a wide variety of KPIs and find ways to effectively manage both present and future cash flows. Cash flows and liquidity are two of the most common challenges faced in this industry — if effectively addressed, your company can position itself for long-term success.