EngineeringOps: A New Take on FinOps

By Kiersten Putnam | Trace3 Senior Innovation Researcher

Over the past few years, our Innovation team has noticed an uptick in conversations around cloud costs, cloud utilization, and the best ways to optimize them. We’re noticing many enterprises expanding their FinOps practice and teams, tasking them with tracking all cloud costs and ensuring cloud cost optimization. 

However, as their scope grows, it’s important they have the support required to take advantage of optimization opportunities. 

Establishing a robust FinOps foundation amid scope growth can be tough because, while FinOps teams find the opportunities, cloud engineering teams execute the optimizations. These engineering teams have their own priorities, such as developing fast and creating new IT capabilities, without the demand for formal processes or financial management. So, it's all about finding the right balance between free innovation and resource optimization, as guided by core FinOps principles.

While cultural alignment of these FinOps practitioner teams is essential for this balance, it is also important to challenge the current processes that have paved the way for cloud optimization. In our research, we have come across emerging solutions that are challenging these processes by making cost optimization a natural part of engineering functions. 

We believe this has the potential to shift FinOps into Engineering Optimization, also referred to as managed FinOps, and thus, we are calling this out as one of our major themes for 2024. If you want to learn more about other themes we are tracking for 2024, continue following Trace3’s Top Predictions for 2024 blog series.

A New Breed of Cost Optimization

So, what’s this new breed of cost optimization all about? Short answer: giving engineering teams and their organization more power to optimize cloud resources.

Over the years, traditional FinOps tools and associated financial operations created platforms that provide general information about the cloud environment, the associated costs, and recommendations for improvements based on FinOps best practices. While these tools can start bridging the gap between teams, the manual optimization required for analysis and remediation may place limits on their consistent adoption cross-functionally, especially when considering complex cloud environments.

In comparison, this new wave of solutions is designed to be implemented closer to the engineering teams. While they are focused more on the engineering side with resource optimization, they also optimize costs along the way and provide those metrics for the FinOps team to use. This shift from traditional FinOps tools to engineering-focused cloud FinOps solutions is fueling this new era of engineering optimization.

Early Signals of this Engineering Optimization Evolution

We’re seeing some early signals of this evolution with solutions taking advantage of this in different ways. On this journey to engineering automated optimization, some solutions provide observability into cloud resources and optimization recommendations for associated cloud usage resources. 

Examples include BlueSky, Revefi, and Infracost, amongst others. While these provide value closer to engineering teams, solutions are going farther to provide actionable insights and find automatic optimization opportunities to reduce manual efforts, supporting the FinOps team with data analytics insights.

Due to the knowledge required to provide this engineering optimization lens, emerging solutions are typically narrow in their expertise and their offerings are dependent on the resources' ability to be optimized. Each cloud service provider and infrastructure component has a different offering structure, with some being easier to identify optimization opportunities than others. 

A prime example of this is AWS Cloud Computing—due to AWS’ flexible purchase options, many vendors have created offerings around optimizing AWS Savings Plans, Reserved Instances, and cloud spend. Azure and GCP have fewer of these offerings, as they naturally have a different structure for cloud utilization.

Although cloud usage was the example above, solutions are spanning across many areas, and our team expects new automated optimization approaches to continue evolving as this engineering optimization trend takes off. In the meantime, below is a list of signals we are currently tracking in the automated optimization space:

• Cloud Computing Optimization (AWS)

Hybrid and multi-cloud environments can make it challenging to ensure visibility over cloud resources, as each cloud service provider differs in their cloud computing offerings. They also differ in their ease with optimization and therefore, while AWS is represented in the automated optimization market, Azure and GCP are more commonly seen in visibility reporting solutions. 

This is mainly due to AWS’ flexible purchase options, allowing solutions in this space to automatically right-size Reserved Instances and other instance plans and provide the highest level of discounts for cloud cost optimization.

AWS Computing Optimization: ProsperOps, Zesty, Exostellar

• Infrastructure Optimization

At the infrastructure layer, there are a variety of different opportunities for ensuring cloud cost optimization and performance optimization. Kubernetes optimization solutions have become popular for analyzing cluster configurations and automatically scaling resources to optimize performance and cost.

Kubernetes Optimization: ScaleOps, Sedai, Cast AI

• Data Optimization

Enterprises continuously leverage more and more data analytics, resulting in increased data costs for management, transport, governance, and storage. To solve this, solutions are providing cloud optimization opportunities for cloud engineering resources. Our team is tracking approaches across data infrastructure, including compression and rehydration for data management and GPU utilization optimization for AI/ML infrastructure. With the boom of GenAI, we are seeing signs in the AI/ML infrastructure market of solutions tailoring their offerings to help with the extensive resources required for deploying and managing generative AI. This contributes to the wider FinOps maturity lifecycle by integrating AI-driven resource management.

AI/ML Infrastructure: Run.ai , Deci, OctoAI

Data Management: Granica, Ultihash

*Note: Solutions listed above are sample solutions in this space and are not meant to be representative of the full market.

As seen above, the signals arising in the market are grouped by optimization function. Since these solutions are narrow in their focus areas, we recommend determining your use cases to begin taking advantage of optimization solutions that fit your specific cloud FinOps framework.

The Future of Cost Optimization

The future is always tricky to make bets on, but signals in this market are leading us to certain outlooks. While we focused on the investment signals of best-of-breed engineering optimization solutions, our Innovation team predicts this approach to cost management will extend beyond these solution sets. In fact, we are already starting to see traditional FinOps visibility reporting platforms extending capabilities to automated optimization solutions, either through partnerships or proprietary development. 

Additionally, there are signs to show that solutions are expanding their optimization scope into other areas where the use cases make sense, including FinOps services. This further validates that there is something special here and that with a shift towards an engineering optimization approach, FinOps teams and developers can work together to keep cloud costs in order.

If you’re interested in learning more, please don’t hesitate to contact us at innovation@trace3.com, or visit our website.