Different Approaches to Cloud FinOps Modeling Tools

Introduction

Companies forecasting cloud spend typically have variances in the 10% to 20% range, which can add up to hundreds of thousands of dollars a month. Accurate, automated, and timely forecasting of cloud spend is one of the most challenging areas for company Cloud Ops and FinOps teams to master. For companies with multiple cloud providers just gathering, organizing, and formatting the millions of rows of usage and billing data is a monumental task (1). Companies need to carefully consider the tooling and platforms that they utilize for forecasting purposes. This decision becomes especially important for companies that have multiple cloud providers with thousands of cloud resources. There are four basic approaches that companies can take regarding FinOps modeling tools:

• Native Tools
• Build In-House
• Third Party Solutions
• Hybrid

With any of these tools automation is the key to successful Fin-Ops modeling & forecast processes. (Please see my other artice on Cloud FinOps Spend Forecasting) However, automation needs to be done in a graduated manner that allows for full transparency at all stages of the process.

Native Tools

The big three cloud providers each offer native tools such as AWS Cost Explorer, Azure Cost Management and GCP Cloud Billing Cost Management which are a good starting point for companies that are just starting their FinOps practices. Smaller cloud providers usually have only limited native tooling that is not even comparable to what the big three offer. These native tools have limitations, which is intentional by the cloud provider so that their customers do not gain too much of an advantage when it comes to pricing negotiations. These tools allow firms to evaluate cloud costs only in one dimension. If a company wants to evaluate its cloud cost structure in a multi-dimensional manner, then it needs to either develop its own tools internally or use one of several external commercially available packages. Whichever method that is adopted, native tools do serve a useful purpose in helping to check the validity of data and results coming out of other platforms.

Build In-House

As companies outgrow cloud provider native tooling they decide to develop in-house Fin-Ops tools for the following reasons (2):

• Organizational preferences to continuously improve internal capabilities.
• Fee structures do not always scale with actual value.
• Ability to customize capabilities based on specific company requirements.
• Flexibility in and diversity of reporting capabilities.
• Ability to quickly modify and enhance.
• Control over company’s own data
• Technical ability to perform complex software development and data analysis in-house.

Corporate in-house models and tools are built on on top of existing business intelligence (BI) software such as Microsoft Power BI, Tableau or Google’s Looker or using cloud-specific solutions such as AWS Cost Intelligence Dashboards or Google's BigQuery Export. However, if a firm decides to use these BI tools, then it is recommended to use those that are visually based such as Microsoft Power BI or Tableau. Avoid using non-visual based BI software like Google’ Looker as they are not easily auditable unless you are a programmer which makes them difficult to maintain over time. A few companies also use open-source tools but these need to be highly customized to be effective which takes significant cost and resources to initially build out and maintain.

Any Fin-Ops models tools built in-house need to be maintained by a dedicated team of at least 2-3 people. The in-house Fin-Ops model team needs firmwide support including active participation and further dedicated resources from Cloud Ops, IT and Engineering teams. Too often companies treat the development of these in-house cloud FinOps tools as small-scale side projects and do not dedicate adequate technical resources or personnel for the task. Any in-house developed tools must also be fully documented, and this documentation needs to be maintained over time. High employee turnover rates are common in the technology and software sector, and in-house expertise on how these Fin-Ops tools work can quickly disappear if even a few people leave the firm.

Third-Pary Solutions

The decision to buy external third-party SaaS Fin-Ops solutions is one that companies choose either because they have a need for more functionality than native tools or have failed at building in-house tools. Companies that have multiple cloud providers can benefit the most from buying externally.

Companies choose to buy external SaaS Fin-Ops solutions for the following reasons (2):

External third-party software packages such as CloudZero, IBM Turbonomic, CloudHealth and Apptio Cloudability can fully automate the data collection, organization, and analysis of millions of rows of billing and usage data across multiple cloud platforms. These highly automated tools can generate driver and trend-based forecasts more frequently allowing for faster decision-making from an operational perspective. The more popular third-party tools

• Cloudability: Focuses on cost optimization and offers tools for forecasting based on historical data and planned business changes.
• CloudHealth by VMware: Provides detailed cloud cost management, budgeting, and forecasting capabilities.
• CloudZero: This platform?provides granular cloud cost visibility across flexible, business-centric dimensions. It helps organize cloud cost into unit cost metrics like cost per customer, cost per feature, without requiring tagging.
• IBM Turbonomic: Is a software platform that helps firms optimize the performance and cost of their IT infrastructure, including public, private and hybrid cloud environments.

Third party solutions remove the need to run and manage software altogether and give a company the chance to get up and running faster. Firms will often pay a monthly, quarterly, or annual fee for these third-party solutions but when properly utilized these fees will be more than offset by the long-term cost savings that they enable.

Hybrid Approach

Finally, companies find that there are no perfect solutions for Cloud FinOps modeling and tools. These firms use a combination of native tools in conjunction with in-house or external third-party solutions. The key drivers being the complexity of a company cloud profile, the availability of internal resources and the capabilities and functionality of each of the tools available.

Conclusion

Companies operating in the cloud have multiple options when it comes to Fin-Ops modeling tools. They can initially rely on native tools provided by cloud hosting providers. As companies grow their cloud profiles, they can build in-house tools or purchase third party solutions. Some companies will try only one approach, and others will try a hybrid approach. No matter the FinOps solution that a firm chooses, it needs to ensure that they achieve operational optimization and cost savings for both the short-term and long-term.