FinOps leaders are becoming the ones who bring the right culture and proven practices to improve cloud ROI. The spread of cloud computing created a massive knowledge gap between technical and business teams. Finance teams find the cloud cost dynamics challenging, and engineers are only starting to feel accountable for the infra expenses they generate. FinOps emerged as the answer and we provide tools that set organizations up for success.
How do you implement FinOps without spending months on planning and team training? And even with team training, you still are in a position where people have to routinely perform tasks they’re often not excited to do.
Here’s a two-step practical approach to implementing FinOps at your company, no matter the industry or size.
Is FinOps the ultimate remedy to cloud cost troubles?
Companies tend to go over their cloud budgets by 13% on average and waste 32% of their cloud spend.1
FinOps is here to address the most painful aspects of running applications in the cloud: cloud waste, overprovisioning, idle resources, and lack of control over which teams use which resources.
Despite the relative newness of “FinOps,” monitoring and reporting on cloud expenses have likely been around since public cloud services became widely available. Organizations that migrated to public cloud soon found that while it may offer flexibility and speed, the cloud also comes with new financial challenges. There’s a lot of talk in the industry today about the cloud ROI and how unexpectedly low it can become.
To control cloud costs, many companies continue to use solutions that rely on manual tasks such as meticulous resource tagging or setting precise usage thresholds to avoid cost spikes.
There is no reason why FinOps should continue this way; automation tools are already solving so many problems in our industry, so why not use them here too? After all, the ultimate goal of FinOps is reducing cloud expenses. Cost optimization solutions can bring teams to this stage within minutes.
Cost visibility is the core of FinOps, and it brings serious cost savings when combined with automation. Here’s how to implement it fast.
FinOps step 1: Cloud cost visibility
A cost monitoring solution helps you build the following FinOps capabilities:
Keeping up with the cloud budget
Teams can always check the rate at which they’re spending the cloud budget and ensure they stay within its limits, to the delight of the finance department. They can do this by checking daily or weekly spend and extrapolating it to get a realistic estimate of monthly costs.
Here’s an example of a daily cloud spend report from CAST AI, which helps to understand your burn rate:
Faster discovery of cost anomalies
How to discover unusual cost spikes as soon as they happen? Cloud providers usually update cost data once or twice a day, so don’t count on them.
Instead, a monitoring solution with real-time capabilities will show you everything you need to know on a dashboard or daily spend report. This is where you can catch these problems as soon as they emerge – and long before they snowball into serious issues.
More accurate cloud planning
Another perk of cloud cost monitoring is that teams can understand their real costs when planning budgets for cloud services. People often overprovision applications because they want to sleep well at night knowing their workloads are running smoothly. When they fail to request the provisioned capacity, the final cost of cloud resources is higher.
Compare the requested vs. provisioned CPUs to discover this gap and calculate how much you’re actually spending per a single requested CPU to make your planning more realistic.
Here’s an example that shows the impact of cloud waste on any cloud budget:
Your cost per provisioned CPU is $2. But you end up using less than that, and your cost per requested CPU rises to $10. This means that you’re running your cluster for a price 5x higher than expected.
Faster issue investigation thanks to historical cost data
In a recent survey, 55% of engineers reported spending time each week on issues related to cloud costs.2 These ranged from unexpected spikes in costs to discrepancies between forecasted and actual expenses. For 11% of respondents, disruption caused by cost issues lasted one sprint or more!
By accessing historical data, you can quickly investigate any cost discrepancies and avoid losing time when all you want to do is work on strategic tasks. Read this to get a step-by-step guide to investigating a cost anomaly.
This is what the historical cost report looks like in CAST AI. You can see the average monthly cost, the average daily cost during that month, the average cost per CPU, and more.
Bonus tip: Share cost metrics with engineers to build a FinOps culture
The State of FinOps survey revealed that getting engineers to act on cost optimization recommendations is the biggest challenge for almost 40% of respondents.3
By providing cost-control metrics in a format that engineers prefer and use, you can spread awareness of infrastructure costs and allow them to make informed decisions about infrastructural investments.
Engineers are used to observability tools that offer real-time monitoring of the application’s performance. If you pick a solution that integrates with their existing operational tooling, adding costs into the mix is easy. This creates a common ground between technical and business teams, building a stronger FinOps culture where everyone feels responsible for managing costs.
Take a look at this article from our Engineering Manager, who shared a few best practices for sharing cost metrics in a standard observability tool Grafana: Control Cloud Costs and Build a FinOps Culture with Grafana
FinsOps step 2: Cloud cost optimization
As demand and utilization change rapidly in cloud-based applications, manual cost optimization becomes time-consuming and labor-intensive.
Automation is the way out. It lets teams achieve their financial goals around cloud costs without the added effort from the engineering side of the company.
Here’s what automated cloud cost optimization can do for you:
- It selects the most cost-efficient instance types and sizes that fit the requirements of your applications,
- It automatically scales your cloud resources to handle spikes in demand,
- It eliminates all the idle resources such as virtual machines left running by accident,
- It rearranges workloads to fit into a smaller number of VMs for cost reduction,
- It automates spot instances to drive costs down and handles any interruptions gracefully.
Most importantly, all of these activities happen in real time. Implementing an automation solution helps your teams master the point-in-time nature of cloud optimization without the added workload of babysitting their infrastructure.
Case study: How Branch saved millions of dollars on the cloud by automating spot instances
Spot instances offer the highest discount on the price per compute hour, but the risk of downtime and the time investment to manage them made it impractical for the marketing automation leader Branch to continue using spot capacity.
The company started looking for a solution that would automate spot instance usage, offer real-time cost visibility, and provide the ability to provision the most cost-effective cloud resources.
By automating cloud cost management with CAST AI, Branch:
- gained the ability to securely deploy spot instances,
- reduced upfront reservation payments,
- improved real-time cost visibility,
- slashed cloud costs further thanks to automated provisioning of the most cost-effective EC2 compute resources.
The CAST AI Kubernetes Cost Optimization solution has been a big success for Branch, saving us several millions of dollars per year in AWS Cloud compute costs for our Kubernetes clusters while maintaining our reliability SLAs. The modest amount of effort by our team makes this one of the highest ROI cloud cost savings initiatives we’ve done at Branch.Mark Weiler, Senior VP of Engineering, Branch
Support your FinOps strategy with the right tooling
What options can you choose from to implement FinOps?
- Native cloud cost management tools – tools like AWS Cost Explorer are usually the entry point into the world of cloud costs for most teams. But once your cloud footprint grows beyond a single cloud provider and service, native cost management tools fail to provide accurate data.
- Custom tools – some companies decide to build a homegrown cost monitoring solution, but there are a few risks that come with this choice.
- Third-party cost and optimization monitoring tools – you can choose from a range of modern solutions that handle cloud-native cost dynamics, bringing teams all the cost monitoring and optimization features that act on cloud resources in real time.
The approaches outlined above come with unique advantages and limitations, leading many teams to combine multiple legacy solutions for cost visibility and potential optimization.
CAST AI brings together cost monitoring and automated cost optimization to remove the hassle of managing multiple solutions in a team’s constantly growing toolkit.
If you run your applications on Kubernetes, get started with your FinOps journey and start monitoring your cloud costs with this free tool, without any limits on connected clusters.
CAST AI clients save an average of 63% on their Kubernetes bills
Connect your cluster and see your costs in 5 min, no credit card required.
-  – Flexera 2022 State of the Cloud Report
-  – The State Of Cloud Cost Intelligence
-  – The State of FinOps 2022
Leave a reply