22nd November, 2023 14 Min read
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For organizations already well-established in the cloud, vendors such as AWS and Azure often bite large chunks out of annual revenue. Monthly cloud bills accumulate quickly – the 5 biggest cloud vendors rake in a combined total revenue of over $80 billion a year, with Microsoft and Amazon claiming the lion’s share of over $20 billion each. While undeniably exciting for the hyperscalers’ stakeholders, never-ending invoices can rapidly become a threat to any organization’s baseline expenses. So intense is cloud spend that it’s projected to outpace IT spending within the next few years.
This article will cover how cloud costs got out of control, and how FinOps tools (for a deep dive into this field, see our ‘What is FinOps’ guide) can help you stick to an ambitious budget, even while rapidly innovating.
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While this architecture grants new levels of flexibility and service delivery, the open-wallet policies of startups and tech giants have overwhelmingly become the reigning approach to cloud provisioning. In the last 3 years, swathes of data have begun to emerge surrounding chronic cloud overprovisioning. The very first of these warnings came in 2020, when reports such as Flexera’s and Virtana’s discovered that 88% of organizations were incurring unnecessary expenses – and that a third of all cloud spend is waste. Studies highlighted the fact that only half of CFOs are confident in their ability to measure each cloud service’s ROI.
Whether overprovisioned resources or an overreliance on costly services, organizations have quickly discovered that cloud cost efficiency is vastly more complex than just shutting off auto-scaling. In response, Financial DevOps (FinOps) is a rising movement that aims to parse investment from wastage. Despite the increased understanding of FinOps, many organizations are still struggling to actualize results: the sheer variety of cloud services continues to transform a budgetary headache into a FinOps migraine. With AWS having launched over 200 new services in 2020 alone, team managers are often blinded by thousands of rows of invoice data, with no clear picture of who, why, and where their cloud budget was spent.
This process becomes infinitely more complex when multi-cloud setups are brought into the equation. Every cloud provider places their own demands on the tools and skills required to consolidate meaningful data from their services. Every report can take significant time to generate and analyze – meaning these reports are outdated by the time they’re even finished.
FinOps tools equip decision-makers with clear cloud cost data that adapts to real-time changes. By digesting cloud data and transforming it into accessible and actionable insights, teams can begin realizing their own cloud responsibility.
There’s no way around it: compute power costs money. The goal of any cost management is to maximize usage while exercising more control over month-to-month costs. The traditional cloud cost approach was simply lifted and shifted from on-prem in the form of budget allocations. This model distributes funds from a general budget to different departments, so that each can individually act on strategic initiatives. These allocations give a maximum allowable expenditure for each department – including every conceivable cost across software, employees, and contracts.
Mapping the old-school budgeting process takes us a step upstream, as management positions determine an allocatable amount based on expected Return on Investment (ROI). As customers are acquired, metrics such as Customer Acquisition Cost (CAC) are used to determine the ongoing profitability of each budget. This process worked reasonably well for on-prem setups, thanks to the inherent accountability granted to in-house infrastructure. However, this traditional approach was built for setups that buy large swathes of compute power all at once (in the form of server stacks). Cloud turns this process on its head by allowing organizations to essentially rent compute power on an as-needed basis.
Just like on-prem, every customer places their own demands on your available compute power – but there’s no longer a substantial buffer of pre-purchased server stacks. Cloud vendors support essentially unlimited growth. This means developers – and the choices that are made in the resource provisioning process – now have direct influence over monthly cloud invoices. Add that to the wider context of the pressure for SaaS companies to rapidly release new features, optimize the existing customer experience, and increase revenue, users, and market share – it’s no wonder that cloud cost optimization has lagged behind.
Relying on traditional cost management has seen a vicious cycle arise. With a lack of accurate forecasting, it becomes almost impossible to avoid over-provisioning – as years upon years of cloud spend accumulate, it becomes increasingly difficult to extract useful metrics and foster responsible cloud allocation habits. The by-the-minute lifecycle of cloud resources also means that the longer you wait to implement FinOps, the greater the effort and cost it takes to remove yourself from a data center mentality. This is one reason for the popularity of FinOps as a Service – sometimes, it just takes a fresh pair of eyes to identify where you’re going wrong.
For those wanting a longer-term approach to cost optimization, FinOps tools allow slow-moving traditional cost management to begin making use of the relentless streams of cloud usage data. By aggregating and visualizing cost data, it becomes possible to see precisely what project, provider, and customer incurred which cost. While visibility is positioned as the first and foremost advantage, the downstream impact of FinOps on the DevOps culture can be significant: showing devs the true financial ramifications of their day-to-day decision-making starts to build a culture of cost accountability.
Ultimately, FinOps tools enhance cloud cost management by providing a comprehensive suite of features and capabilities that empower organizations to gain better control over their cloud expenses, optimize spending, and align cloud costs with business objectives. This, in turn, helps organizations make more informed decisions about their cloud resources and ensures that cloud spending is efficient and cost-effective.
FinOps becomes more complex the longer you stare at it. This is why it’s so crucial to segment your cloud landscape into key sub-categories. The FinOps Foundation recommends three primary domains:
With these three areas clearly defined, it becomes possible to identify exactly what each FinOps tool can achieve across every section. The capabilities of FinOps tools can finally be addressed.
Cost allocation is necessary thanks to the sheer quantity of data being produced with every invoice. Billing and usage data in the cloud is consolidated into a limited number of data repositories, frequently containing millions of data entries, and is delivered several times each day.
Basic cost allocation divides the total bill by account, project, or subscription. More advanced cost allocation tools allow your bills to reflect the hierarchy of every cloud-based project: across services, teams, and workload. The more granular the cost allocation, the higher the degree of visibility you’re granted into your cloud surface.
While cost data is necessary, data itself isn’t enough: it needs to be streamlined into actionable insight. This has traditionally been left to employees’ best efforts, but new FinOps capabilities now provide a wealth of analysis power. This can encompass multi-account analyses, which track the unit costs for key services over time. This focus on unit economics further allows product teams to prioritize a unit-cost mindset.
A key capability within FinOps data analytics is the ability to detect spending that deviates from the usual or anticipated levels. Detection tools and procedures empower the FinOps team to promptly respond, ensuring the preservation of spending levels aligned with an organization’s expectations. Employing automated, machine learning-based anomaly detection is crucial for swiftly identifying those outliers within your cloud data. Connecting this to ticketing systems – alongside contextual alert thresholds linked directly to service components – provides a strong backline of defense against cloud cost spikes.
All of this analytical power creates a data repository of normalized, queryable data from which reporting, analysis, and visualization of cloud cost and usage will occur.
A solid reporting system is one of the most important components of any FinOps feature. It allows finance and procurement teams to develop cost allocation models that accurately reflect operational budgets. This, in turn, ensures that cost models are allocating the right amount of money to each level of the organization.
With customizable cost dashboards, product managers are able to take an active role in budget consumption, allowing them to set achievable cost objectives for respective teams. Developers and engineers can finally gain a cross-departmental view of their day-to-day architectural choices.
While the previous capabilities are largely cross-functional, it’s important to consider the abilities that FinOps tools offer your cloud usage. Commitment-based discounts are one of the most accessible forms of this. Cloud Service Providers (CSPs) often offer discounts for compute power that is purchased in bulk, rather than on-demand. For organizations that enjoy steady rates of growth, instance commitments can unlock immediate savings.
The usage data being collected and streamlined by FinOps tools allow for machine learning models to accurately purchase commitment discounts in an optimal way. The frequency of purchase cycles vastly outstrips manual counterparts and unlocks even greater ROI for every workload.
Like any tool, third-party FinOps solutions offer a mix of advantages and disadvantages. Weighing these up demands an understanding of the individual tool, and your own architectural requirements.
For instance, the higher complexity of third-party tooling can place greater demands on a lean or at-maximum-capacity team. Even basic monitoring agents require installation and maintenance, and initial steps toward FinOps fluency can be burdened by these time demands. If lightning-quick results are your main priority, a tool with complex installation requirements can burden your FinOps journey with a rocky start. It’s not just how the tool supports monitoring – it’s equally important to keep an eye on what it can and can’t integrate with. While most cloud providers are supported, systems such as Kubernetes or on-premises integrations can be blind spots for the most budget-friendly multi-cloud tools. This becomes especially important when you consider the fact that third-party tools come with extra fees – on top of your pre-existing cloud costs.
With all potential pitfalls acknowledged, it’s worth exploring the ways in which third-party FinOps tools can exceed the capabilities of in-house solutions. One of the most apparent benefits to those getting used to the multi-departmental aspect of FinOps is the enhanced collaboration. Many third-party FinOps tools offer features that facilitate better collaboration among team members, such as shared dashboards, real-time updates, and integrated communication tools. This aspect is particularly beneficial for teams that are geographically dispersed or working remotely. This global scope also extends to compliance and regulatory demands, where third-party FinOps tools often shine by including features that help businesses stay compliant with both local and international financial regulations. This is supported by the multiple currencies and vast multi-cloud regions further embedded into their scope.
If your organization isn’t global, the ability for third-party tools to support rapid change further makes them an excellent choice for new FinOps efforts. This flexibility is crucial for companies that want to gain a new degree of control over fluctuating financial transactions or are in a phase of rapid expansion. Furthermore, making rapid and widespread changes no longer has to place significant stress on your teams: by automating routine tasks such as data entry, invoicing, and reconciliation, these tools can significantly increase efficiency. This automation not only saves time but also reduces the likelihood of human error, leading to more accurate financial reporting.
Finally, the constant updates and maintenance these tools receive from their providers ensure that they stay current with the latest financial regulations and technological advancements. This aspect is particularly valuable in a rapidly evolving landscape, where keeping up with changes can be a formidable challenge for any organization. fundamentally, these robust security measures help keep sensitive financial data away from cyber threats.
However, there are also notable disadvantages to using third-party FinOps tools. One major concern is the dependency on an external provider, which can pose risks in terms of reliability and control. If the provider experiences downtime or discontinues their service, it could disrupt the financial operations of the business. Additionally, integrating these tools into existing systems can be complex and time-consuming, potentially leading to operational delays and increased costs.
Another drawback is the potential for limited customization. While third-party tools are designed to be versatile, they may not always align perfectly with the specific needs or processes of a particular business. This mismatch can lead to inefficiencies or the need for additional workarounds. Privacy and data security, despite robust measures, remain a concern, as using an external tool involves entrusting sensitive financial information to a third party. This reliance can be a significant concern for businesses with stringent data privacy policies or those operating in highly regulated industries.
With such a wealth of capabilities on offer, the next step is establishing which FinOps tools are the best for you. Chief of this decision is whether to stick to the tools already offered by your CSP, or to venture out into the field of third-party options.
Many cost optimization projects are born within an individual cloud vendor’s tools. closely integrated into your current workflow, basic native tooling can kickstart everything. Azure’s resource manager enables the addition of tags – plain text keys – to resources in production. This forms the basis of cloud visibility, and is directly supported by Microsoft’s own Cost Management platform.
While this process is straightforward for simple single-departmental resources, it can become more complex when dealing with multi-departmental servers. Without clear policies, engineers may face challenges when web applications or environments draw resources from multiple subscriptions managed by different teams.
Alongside this, though PowerBI is a fantastic tool for fledgling cost analysis projects, the employee hours and expertise needed to create cutting-edge cost analyses are considerable. These reports also fall into the trap of being outdated by the time they reach project managers’ desks. To find out more, check out our guide about Azure FinOps best practices.
Just like Azure, native AWS tools are designed to streamline your cost management and analysis, without having to stray too far from the CSP itself. AWS Cost Explorer, the lightweight solution for visualizing and forecasting costs, offers easy access through the AWS Billing Console. It serves as an out-of-the-box solution, providing a simple way to analyze your AWS costs. Once enabled, you can effortlessly view your account’s overall cost and usage data, including linked accounts under your management account if part of an AWS Organization. The tool allows for basic filtering and grouping, providing a good low-level understanding of your cloud environment. For those interested in leveraging machine learning (ML) for forecasting, Amazon QuickSight, a cloud-native, serverless business intelligence service, offers a user-friendly approach without requiring ML expertise. On the other hand, Amazon Forecast stands out as the most customizable option, being a fully managed ML service that simplifies predictor training and forecast creation with a variety of built-in algorithm types.
For cloud-native tooling such as these, however, keep in mind that they rely heavily on resource tagging. However, many organizations grapple with sizable tagging backlogs. This makes the initial setup phase far longer than anticipated, as the manual updating of tags is incredibly time and cost-intensive. Moreover, tagging may not be universally applicable to every business use case, adding complexity to the FinOps journey. If you’re dependent on an AWS framework, check out our in-depth guide to AWS FinOps.
Container orchestration tools such as Kubernetes are a popular way for engineers to deploy containers while ensuring availability.
Despite this, Kubernetes remains notorious for its lack of FinOps optimization – its opaque pricing model has busted many budgets, thanks to the three prongs through which K8s scales. When setting up, engineers are expected to include request, limit, and replica data. Manually thinking of good numbers for different containers is hard, so most devs pick their best guess, and never change it. With no native FinOps tooling, it remains entirely dependent on third-party tools.
Multi-cloud setups are becoming increasingly popular thanks to the versatility and adaptability they offer to cutting-edge applications. Particularly well suited to DevOps frameworks, modern microservice architecture is almost wholly reliant on multiple cloud vendors.
If you’re reliant on native FinOps tools, however, this is where cost optimization begins to become a minefield of data streams and cross-cloud efficiency. In the same way, DevOps methodology once aimed to break down heavily siloed engineering teams, FinOps for multi-cloud understands that relying solely on individual vendors’ tools can allow cloud inefficiency to pool in the gaps between. Connecting the dots between every single line on a cloud invoice requires comprehensive, always-on FinOps tooling.
GlobalDots’ position at the precipice of FinOps innovation grants us unprecedented access to third-party tools. Automation is one particularly promising field that has already realized immense savings. Overarching FinOps management that is simultaneously real-time and fully accessible is far beyond manual capabilities – take the field of cloud usage, for example. While spot instances are the least expensive form of compute, these can be terminated at very short notice. Automated rightsizing sits adjacent to both cross-cloud data usage and cloud vendors’ own instance marketplaces. These streams of data are fed into a machine learning model to identify your cloud’s behavior profile, before being mapped onto reserved sale prices. When an upcoming change in capacity is predicted, lower-cost instances are automatically purchased to handle the extra work.
This predictive approach is echoed in the capabilities of storage capacity adjusters, which streamline usage across cloud and disk compute. Even once-optimized platforms such as Kubernetes are now able to take advantage of these FinOps solutions. Elastic pod rightsizing allows you to accurately see and align your K8s with real CPU and memory demands, even as load changes over time.
Another benefit of third-party tools is their ability to improve performance at the same time. Our FinOps for Kubernetes solutions also grant nodes great headroom, preventing your pods from waiting for new provisions and reducing latency.
Achieving the most optimal cloud usage is no longer a battle between finance and development teams. With a foundation of automated cloud rates, engineers are granted the space to take a step back and reassess how they’re approaching cloud ownership. This needs to be supported with a FinOps platform that can be customized to each stakeholder’s priorities. For engineers, this could be cost per environment or team. For other teams, however, they may want a higher degree of focus on cost per service or feature. Third-party tools are able to offer this flexibility and visibility – even in the absence of a perfect tagging strategy. Our comprehensive cloud cost optimization solutions are exclusively built upon impactful solutions from industry leaders and innovative startups. This risk-free commitment is designed to reduce your cloud costs significantly, incorporating relevant solutions into your tech stack. You only pay from the positive savings achieved, all while streamlining operations, cutting down on complexity, and conserving operational resources.
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