How finance and engineering can collaborate to control cloud costs

We sat down with Erik Norman, founder and CRO of Caligo s.r.l., to discuss the thorny issue of collaboration between finance and engineering, and what advice tech leaders have for working together to manage costs.

What are the biggest ‘missed opportunities’ you see when it comes to optimizing cloud spend?

Erik Norman (EN): The most obvious missed opportunity is not having a strategy in place whatsoever. The problem is that cost reduction projects can be passed around like a hot potato between teams, and they usually end up with engineering. 

But engineers are already overloaded with projects and KPIs - and they are rarely aligned with cost reduction. They tend to focus on areas like product uplift and minimized downtime. 

So projects like cloud cost optimization just get left. If there are no service level agreements (SLAs) attached to cost efficiency projects, then there is no incentive to take them on alongside other work.

But it goes deeper than this. We use phrases like ‘cost reduction’ or ‘cost optimization’ but that’s a narrow focus, and creates the impression that all costs are bad. Without costs, you wouldn’t have anything running. It’s the value that’s provided from costs that should be analyzed.

For example, if revenue increases by 25% but costs only go up by 10%, then that’s a positive outcome even though costs have increased - your ROI is improving. However if revenue increases by 10% and costs increase by 25%, you need to analyze your spend to find where you can redress the balance.

Why do companies miss opportunities to optimize their cloud value?

EN: Usually, teams are built in horizontal, siloed structures which makes it really difficult to not only collaborate on projects, but to implement specific value optimization strategies. 

Every team has their own function-specific KPIs (e.g. product uplift, qualified leads, gross/net margin) to focus on already. Because they are related directly to their own work, there is no measurable incentive to collaborate with other teams in their day-to-day activities. 

You could introduce KPIs for each team that revolve around value optimization. However this requires a multi-disciplinary approach, and only concentrating on ‘your’ KPIs would restrict how effective the outcomes are. 

For example, if a procurement team for a retail company has a KPI to reduce spend on cloud applications by X amount, they could choose to not renew some products, therefore bringing down spend. But, by not including engineering in the conversation (because they aren’t included in the KPIs), they might accidentally delete components that help maintain capacity and computing power.

This then risks the cloud overloading and breaking down, creating an outage and ultimately losing income that the cloud generates.

Long-term strategic change needs alignment across teams. Having a shared metric, with associated budget, would help share responsibility. This is actually where using cost unit economics can help. 

What advice do you have for finance leaders who want to collaborate with engineering teams on cloud value optimization?

EN: Build out a shared working model for finance and engineering teams. This will help spread the responsibility between teams and encourage engineers to get involved, as they will see that a) it’s not all on them, and b) it’s being taken seriously given the targets attached to it. 

Whilst this is easier to do in an agile, smaller company, it can be really difficult in a larger, enterprise company with embedded legacy working practices. So you really need to get consistent buy-in from key engineering and finance leaders.

What’s your advice for connecting cloud value to company growth goals?

EN: If you have performance metrics that map out what is provided in terms of services and usage, you can see associated costs rise or fall. You can then map this to help predict input vs output against company goals - which should show the value. 

But sometimes you might not immediately have that performance data to relate to cost trends. Creating testing environments that create proof-of-concepts for their platform help model this data. Then, you can run it with increasing numbers of fictional users to help simulate a real-time scenario that shows what a cost-curve would do. 

You’ll also be able to spot where some of your architecture that usually works at lower usage goes wrong or causes cost surges when usage is ramped up. So this testing environment not only helps you map cloud spending to company growth goals, but also figure out whether your cloud growth will be sustainable with your current setup.

Reporting like this is such an asset to showcasing the value of the cloud company-wide too. Testing environments that predict cost curves can highlight the perceived value of the cloud to managers, getting them on board with thinking about value optimization and collaboration. And it can predict the future ROI of the cloud - which can be used to prevent headcount being lost from cloud engineering teams. 

What advice do you have for getting people in organizations to think more carefully about cloud costs?

EN: It’s all about empowering people to get involved, rather than placing it upon them. For example, give engineering teams SLAs and a budget for cloud spending that they control. 

This autonomy comes with criteria that if there is overspend or poor application of products (architecture breaks, no relation to company growth goals etc), then SLAs won’t be met and budget autonomy will be removed.

Handing over fiscal responsibility encourages intuition and care over spend, rather than a loss of financial control. It means that any decisions they make will include considerations about cost and overall value because they are directly related to them.

How do you think AI might impact future cloud costs?

EN: There are plenty of projects that are based around AI at the moment, and a lot of potential for more. However very few are likely to make it into production. 

The added value of AI at this moment in time is low compared to production costs and purchasing prices. So we know that there will be significant costs that will impact your cloud budget, but with limited return at the moment. Plus, AI products haven’t had the time in the market yet to showcase their true, long-term value so we are still experimenting with it at the moment. 

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For more insights into cloud spend optimization, check out our extensive guide, where we cover:

• How to enhance your spending visibility to pinpoint cost optimization opportunities 
• The most effective steps you can take to make your cloud spend more commercially sustainable
• How to embrace new technologies to maximize ROI without breaking the bank.

All with valuable insights from AWS, FinOps practitioners, and expert cloud engineers.

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About Erik Norman.

Erik is a seasoned cloud solutions expert, certified FinOps consultant, and tech mentor with over 15 years of experience in the software industry. As the founder and CEO of Caligo s.r.l., Erik has been at the forefront of driving digital transformation for clients. 

Specializing in FinOps, they have successfully helped numerous customers optimize their financial operations, ensuring cost efficiency and automation in cloud migration and management. Their expertise in fostering cross-departmental collaboration and aligning financial strategies with business goals has made them a trusted advisor and leader in the field.