This conversation covers:

Why many businesses are shifting away from analyzing total cloud spend (CapEX vs. OpEX) and are now forecasting spend based around usage patterns.
The difference between cloud-native, cloud computing, and operating in the cloud.
The delta that often exists between engineering teams and business stakeholders regarding costs. Travis also offers tips for aligning both parties earlier in the project lifecycle.
Common misconceptions that exist around cost management, for both engineers and business stakeholders. For example, Travis talks about how engineers often assume that business teams manage purely to dollars and cents, when they are often very open to extending budgets when it’s necessary.
Tips for predicting cloud spend, and why teams usually fall short in their projections.
Why conducting cloud cost management too early in a project can be detrimental.
Comparing the cost of the cloud to a private data center.
The growing reliance on multi-cloud among large enterprises. Travis also explains why it’s important to have the right processes in place, to identify cross-cloud saving opportunities.
How IT has transitioned from a business enabler to a business driver in recent years, and is now arguably the most important component for the average company.

Links:

Twitter: https://twitter.com/TravisWRehl
LinkedIn: https://www.linkedin.com/in/travis-rehl-tech/
Main Company Site: https://cloudcheckr.com
CloudCheckr All Stars: https://cloudchecker.com/allstars

Transcript

Announcer: Welcome to The Business of cloud-native podcast, where we explore how end users talk and think about the transition to Kubernetes and cloud-native architectures.

Emily: Welcome to the Business of cloud-native. I'm your host, Emily Omier, and I'm here today with Travis Rehl, who is the director of product at CloudCheckr. Travis, I just wanted to start out, first of all, by saying thank you for joining me on the show. And second of all, if you could just start off by introducing yourself. What you do, and by that I mean, what does an actual day look like? And some of your background?

Travis: Yeah. Well, thanks for having me. So yeah, I'm Travis Rehl, director of product here at CloudCheckr. What that really means is, I have the fun job of figuring out what should the business do next in relation to our product offering here at the business. That means roadmap, looking at the market, what are customers doing differently now, or planning to do differently over the next year, two years or so, on cloud? What their cost strategies are, what their invoicing and chargeback strategies are, all that type of fun stuff, and how we can help accommodate those particular strategies using our product offering.

Sort of, day to day, though, I would say that a bunch of my time during the day is spent talking to customers, figuring out where they are in their cloud journey, if you will, what programs or projects they may have in flight that are interesting, or complicated, or they need help on. Especially making any sort of analysis help in particular, and then lastly, taking all that information and packaging it up neatly, so that the business can make a decision to add functionality to our product in some way that can assist them move forward.

Emily: The first question I wanted to ask is actually if you could talk just a little bit about the distinction between cloud-native, and cloud computing, and operating in the cloud. What do all of those things actually mean, and where's the delta between them?

Travis: Sure. Yeah so, it's actually kind of interesting, and you'll hear it a little bit differently from different people. In my background, in particular—I used to run an engineering department for a managed service provider. And so we used to do a lot of project planning of companies as to what's their strategy for their software deployment of some kind on cloud. And typically the two you see for, say, cloud-native versus operating in the cloud, operating on the cloud is very atypical.

You'd associate that to something like lift and shift—probably hear about a lot—the concept of taking your on-prem workload and simply cloning it, or taking it in some way and copying in some way, on to the cloud-native vendor in particular. So, literally just standing up servers of clones of hard drives and so forth, and emulating what you had on-prem, but on the cloud. That's a great technique for moving quickly to cloud. That's not a great technique if you want to be cloud-native. So, that's really the big segue for cloud-native, in particular, is you want to build a software solution that takes advantage of cloud-only technology, meaning serverless compute resources, meaning auto-scaling different types of services themselves, stuff you probably didn't have when you're on-prem originally, that you now have, you can take advantage of on the cloud. That's almost like a redesign, or reimplementation around those models that cloud itself provides to you. That, to me, is the big difference. And I see oftentimes that gap-wise, many companies who are starting on-prem, they will do the migration to cloud first, the lift and shift model, and then they will decide, “Hey, I want to redesign pieces of it to make it more cloud-native.” And then you'll see startups who don't have on-prem at all, they will just go into cloud-native from the get-go.

Emily: Of course, CloudCheckr specializes in helping with costs among some other things, but how do costs fit into this journey, and what sort of cost-related concerns do companies have as they're on this cloud journey?

Travis: Yeah, so there's a few. I would actually say that years ago—just to clarify, the discussion has changed over the last few years—but years ago, it started with CapEx versus OpEx costs, specifically for purchasing of your IT services. On-prem, you'd probably purchase up-front a bulk number of VMs or servers or otherwise, for a number of years, and so be a CapEx cost. When you moved over to cloud and more of this, usage-based, model kind of threw a lot of people for a loop when it came to more OpEx usage space models. AWS, Azure, GCP have helped in that regard with things like reserved instances for companies who are more CapEx oriented as well, but in terms of the initial years ago, a big hurdle was communicating that difference and how the business may pay for these services. And a lot of people were very interested in moving to OpEx back then, in particular.

When it came to how do you take into account all these cost-related changes the business may go through, one of the big ones that I see most recently is around the transference and storage of data. In the past, it would have been about how much money total am I going to spend on the cloud itself. Now, it's about what am I forecasting to spend based off of those usage patterns. It's a bit easier to forecast those things when you have servers that run for a period of time, but when you have usage patterns for data ingestion, for data transfer, for servers spinning up and spinning down and scaling out horizontally, ...

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