Implementing FinOps: Should You Invest in a Vendor Platform?

As more and more enterprises accelerate their cloud adoption, managing and optimizing cloud costs has become a top priority. Over 65% of enterprise IT spending is now committed to cloud-based offerings, according to Gartner[^1]. And with worldwide end-user spending on public cloud services forecasted to grow 20.7% to reach $591.8 billion in 2023[^2], cloud costs are taking up an increasingly large portion of IT budgets.

However, studies show that 32% of cloud spend is wasted[^3] on idle or overprovisioned resources. Without proper cost controls and optimization, moving to the cloud can end up being more expensive than anticipated. This is where FinOps comes in.

What is FinOps?

FinOps, short for Cloud Financial Operations, is an evolving cloud operating model that enables organizations to get maximum business value by helping engineering, finance, technology and business teams to collaborate on data-driven cloud spending decisions.

The goal of FinOps is to bring financial accountability to the variable spend model of cloud. According to the FinOps Foundation, FinOps is a growing trend:

• 43% of FinOps practitioners saw their FinOps team size increase over 50% in the last 12 months[^4]
• Large companies with 10,000+ employees are 3X more likely to have a dedicated FinOps team[^4]
• By 2025, Gartner predicts 70% of organizations using the cloud will establish a FinOps function to automate policy-driven observability and optimization of cloud resources[^5]

As defined by the FinOps Foundation, this operating model has three iterative phases:

1. Inform – Visibility into cloud costs, allocation, benchmarking, budgeting, and forecasting
2. Optimize – Implementing cost optimization best practices and maximizing cloud efficiency
3. Operate – Continuous improvement and operations of cloud usage and costs

While FinOps is often thought of as a framework and set of best practices, technology plays an increasingly important role in enabling this operating model. Both cloud providers and third-party software vendors now offer tools to support critical FinOps functions like cost visibility, allocation, budgeting, anomaly detection, cost optimization, and more.

As organizations advance their FinOps practice, many find that native cloud provider tools have limitations compared to dedicated FinOps platforms. So should you invest in a vendor solution for FinOps? Let‘s explore the key differences and benefits.

Native Cloud Provider Tools vs. FinOps Platforms

All the major cloud providers – AWS, Azure, and Google Cloud – provide built-in tools for basic cost management and optimization. For example:

• AWS Cost Explorer for cost and usage reporting
• AWS Budgets for setting custom budgets and alerts
• Azure Cost Management for cost analysis, budgets, and exporting cost data
• Google Cloud Billing reports for monitoring spend by project and service

These native tools are a great starting point for FinOps and can help with fundamental cost visibility and allocation. They also provide some general cost optimization recommendations, like rightsizing underutilized instances or identifying idle resources.

However, as organizations look to scale and mature their FinOps practice, many find they need more advanced capabilities that native tools lack. This is where third-party FinOps platforms come in.

Dedicated FinOps software provides several key advantages over native cloud tooling:

1. AI-Powered Cost Optimization Insights

While cloud provider tools can identify basic cost saving opportunities, FinOps platforms use artificial intelligence (AI) and machine learning (ML) to generate more precise and actionable optimization recommendations.

By ingesting and analyzing large volumes of granular cloud utilization and cost data, these tools can identify patterns and anomalies that would be impossible to spot manually. Using AI/ML models, they can then more intelligently rightsize instances, often down to the application level.

Some platforms even use predictive analytics to forecast future costs and resource needs to proactively optimize usage. By leveraging ML algorithms trained on an organization‘s historical cloud consumption data, these tools can predict usage trends and make recommendations to avoid overprovisioning.

In addition to resource-level optimizations, FinOps platforms can uncover savings opportunities at the billing level, such as by recommending optimal use of reserved instances and savings plans across an organization‘s entire cloud footprint.

Here are a few examples of the AI/ML-driven cost optimization capabilities in leading platforms:

• Densify uses ML to analyze workload patterns and recommend optimal cloud resources and configurations. Their ‘Densification‘ algorithms can increase utilization by up to 80%.[^6]

• CloudZero ingests metrics from AWS, Datadog, Kubernetes and more to build ML cost models. These models can predict usage and costs for individual microservices, enabling engineering teams to proactively optimize.[^7]

• Spot by NetApp automates cloud infrastructure by continuously analyzing compute usage and making intelligent provisioning decisions via their AI-driven ‘elastiGroup‘ technology.[^8]

According to a recent study, organizations using AI-powered cost optimization tools were able to reduce their cloud spend by an average of 25%[^9], compared to those relying on cloud-native tooling alone.

2. Single Pane of Glass for Multi-Cloud

Most enterprises today use a multi-cloud strategy. 89% of enterprises have a multi-cloud strategy and 80% are using hybrid cloud, according to the Flexera 2022 State of the Cloud Report[^10].

With each cloud provider having their own native toolset, this makes it challenging to get a unified view of cloud costs. FinOps teams have to manually aggregate and normalize data across clouds.

Leading FinOps platforms provide a single pane of glass to manage costs across AWS, Azure, Google Cloud, and more. This gives a more holistic, apples-to-apples view to enable better optimization decisions.

For example, Apptio Cloudability‘s Unified Cost Model standardizes billing data across cloud providers and maps costs to an organization‘s own business hierarchy. This enables FinOps teams to create multi-cloud budgets, cost allocation rules, alerts, and more in one centralized platform.[^11]

3. Integrations with IT and Business Tools

FinOps platforms can integrate with an organization‘s existing IT tools to pull in additional cost and usage data for deeper insights. This could include:

• Connecting to a data warehouse like Snowflake or BigQuery to merge cloud billing data with other financial data
• Ingesting utilization metrics from monitoring tools like Datadog or Dynatrace to inform rightsizing recommendations
• Importing Kubernetes costs from Prometheus
• Mapping cloud costs to specific business metrics from Salesforce or other enterprise apps

These integrations provide FinOps teams a more complete picture of cloud unit economics to drive accountability and help ensure spend aligns with business priorities.

One example is CloudZero‘s Kubernetes integration which maps cluster costs to specific pods, nodes, namespaces, labels, and more. By pulling in this granular data, CloudZero can provide optimization insights for cloud native workloads tied to the business – like the cost per transaction in a payments system.[^12]

4. Automation and Governance at Scale

As cloud environments grow more complex, it becomes impossible to manually optimize costs. FinOps at scale requires automation.

FinOps platforms enable building automated cost optimization workflows, such as:

• Automatically shutting down idle resources
• Setting policies to rightsize instances or volumes
• Using infrastructure-as-code to deploy guardrails and prevent cost overruns
• Automating budget alerts and anomaly detection

With policy-driven automation, organizations can continuously optimize cloud spend without having to dedicate costly FinOps resources. Automation also embeds cost awareness into developer processes to operate more efficiently.

According to the State of FinOps Report 2022, automation is a key characteristic of mature FinOps practices. Organizations that have reached the "Run" phase of FinOps maturity are automating over 20% more tasks than those in the "Walk" phase.[^13]

5. Support for Cloud Native

Kubernetes and containers introduce new challenges for FinOps. Containerized workloads are highly dynamic, making it difficult to allocate costs and define budgets.

Leading FinOps platforms now support Kubernetes cost monitoring and optimization. Solutions like Kubecost and CloudZero can allocate shared cluster costs to specific teams, applications, or business units for chargeback. They also identify cost optimization opportunities like right-sizing pods and node pools.

For cloud native organizations, these container cost insights are essential to control spend on modern architectures. Cloud provider tools lack this specialized functionality.

The Future of FinOps: AI-Powered Automation

As FinOps platforms evolve, we‘re seeing a growing convergence with AIOps (artificial intelligence for IT operations). AIOps tools use big data analytics and ML to automatically monitor, detect and remediate application and infrastructure issues.

The next frontier is extending the core concepts of AIOps to cloud financial management. By applying AI to FinOps, organizations can continuously optimize costs in real-time while proactively managing performance and availability. Imagine a scenario where:

• ML models predict application usage and automatically scale cloud resources up or down in advance
• Anomaly detection spots a sudden spike in cloud costs and automatically throttles back resources or alerts FinOps teams
• Pattern matching identifies the optimal discount model for each workload based on past usage data
• NLP enables building cost policies and budgets using natural language (e.g. "Limit production Kubernetes spending to $X per day")

This vision of autonomous cloud cost optimization is quickly becoming a reality thanks to AI innovation. By 2025, Gartner predicts organizations using AI-powered FinOps automation tools will spend 30% less on cloud than those that don‘t[^14].

Expert Opinions

But don‘t just take my word for it. Here are insights from leading experts on the importance of FinOps tooling and AI in cloud cost management:

"AI and machine learning are a game changer for FinOps. The massive amount of cost and usage data available in cloud platforms today is impossible for humans to process at scale. AI can augment FinOps teams, mining this data to uncover cost anomalies, optimization opportunities and forecasting trends."
– J.R. Storment, Executive Director, FinOps Foundation

"When building a FinOps practice, establishing trust in your cost data and allocation is absolutely foundational. Cloud provider billing tools rarely provide the level of granularity needed. Investing in a FinOps platform with robust data management and analytics capabilities is critical to accurately mapping cloud costs to the business."
– Ashley Hromatko, FinOps Practice Lead, Pearson

"Automation is a key tenet of both FinOps and DevOps. Organizations need to leverage policy and infrastructure-as-code to continuously optimize cloud resources without manual intervention. AI and machine learning will play an increasingly important role in enabling this automation as environments grow more complex."
– Mike Fuller, Principal Systems Engineer, Atlassian

Conclusion

As cloud spending continues to rise, FinOps has become a must-have discipline for organizations looking to balance the speed, cost and quality of their cloud investments. While it‘s possible to get started with the basic cost tools baked into AWS, Azure and Google Cloud, scaling and maturing FinOps likely requires a dedicated platform.

FinOps vendor software provides significant advantages, from AI-powered cost optimization insights to multi-cloud support to automation at scale. Investing in the right tool is often essential to maximizing the value of the cloud.

Of course, tools are just one piece of the FinOps puzzle. People and processes are equally important. But arming your FinOps team with a robust platform can be the key to more efficient, optimized cloud operations.

If you‘re considering a FinOps management tool, start by getting clear on your goals and requirements, and carefully evaluate the leading vendors to find the best fit for your organization‘s needs. Make sure to assess their AI/ML capabilities to future-proof your investment. With the right FinOps platform in place, you‘ll be well on your way to successfully managing cloud costs at scale.

References

[^1]: Gartner Forecasts Worldwide Public Cloud End-User Spending to Grow 18% in 2021
[^2]: Gartner Forecasts Worldwide Public Cloud End-User Spending to Reach Nearly $500 Billion in 2022
[^3]: Cloud Computing Trends: 2021 State of the Cloud Report
[^4]: 2021 State of FinOps Report
[^5]: Market Trends: Cloud Financial Management and FinOps, Worldwide
[^6]: Densify: Machine Learning Cloud Optimization
[^7]: CloudZero: Kubernetes Cost Intelligence
[^8]: Spot by NetApp: Leveraging AI and Automation for Cloud Infrastructure
[^9]: GigaOm Radar Report for Cloud Cost Management
[^10]: Flexera 2022 State of the Cloud Report
[^11]: Apptio Cloudability: Unified Cost Model
[^12]: CloudZero Kubernetes Cost Allocation
[^13]: 2022 State of FinOps Report
[^14]: Innovation Insight for Autonomous Cloud Optimization