Cloud spend is a significant line item in every company’s IT budget, and controlling it is especially important in today’s challenging economic climate. A steep decline in share prices, valuations, and a slowdown in venture capital funding have led CEOs to cut costs within their large line items, reduce their workforce, and reevaluate their unit economics — especially their margin per customer. The question is, how many organizations know their margin per customer? Over the last year, I’ve interviewed over a hundred SaaS businesses and not a single one could answer that question satisfactorily. The fact that so many resources are being invested in cloud transformation and FinOps without this basic understanding is astounding. In this blog post, I’ll help you tackle this question. But before we get started, we’ll need to understand what components are required to do the analysis and why it’s so hard to get an accurate answer. Unit economics explained    A business model's unit economics describes its revenues and costs in relation to one unit — such as a customer served or unit sold. Without a clear understanding of the relationship between the cost and the revenue, it's impossible to understand the effectiveness of each customer. Calculating margin per customer will be straightforward once you find the relationship. To understand costs, you must first identify the resources required per customer. What do I mean by resource? A resource is any cloud service that has a direct or shared cost associated with it. If you have dozens of customers on shared resources like databases, storage, and microservices, you would need to model the resources into smaller pieces and understand the time (CPU) and/or memory a specific customer is consuming. Once you have the micro-units information then you will be able to measure more logical functions such as logins, transactions, and requests. Breaking down all your cloud resources into these micro-units and units is an extremely difficult and tedious process. How to calculate margins per customer in five steps  1. Understand your unit(s) economics Every business can have a different unit or units of economics. The economic unit of a b2b fintech may be transactions, while that of a streaming app might be hours of video. In other cases, the unit of economics will be the same one you use to define pricing and ARR. A good unit of economics is one that you can determine the cost accurately for and use it to calculate your maximum margins and validate your pricing strategy. These units are not necessarily well measured with the low-level costs of CPU and memory or disk. However, it is extremely important to define them in a way that they are correlated to your customer consumption. If you have a customer that costs you more, it has to be reflected in the amount of consumption of these units. 2. Calculate your unit cost Unit costs will never be accurate. The cost of these services can, however, be estimated intelligently using several methods of varying accuracy. With more complicated methods requiring some R&D effort. The simple but naive way — count on big numbers: Measure total cloud spend for a particular application against the total units (i.e., customers) for that application or service. Unit Cost = Total Cost / Total Units The proximal and linear way — combine multiple micro-units to estimate your unit costs:  Start by breaking down your unit economics into more unit economics. Transactions, for example, can consist of compute resources, database calls, API calls, as well as storage consumption. It is not easy to map micro-units for a customer, and the trick is to simplify things and estimate them in a linear way. The next step is to weight the contribution of each micro-unit to overall costs using a percentile. This would be the closest estimate to actual costs. If a transaction is made up of a DB query and an intensive CPU working process, and a 1000 queries represent 20% of the total costs and the working process represents the other 80%, it would make sense to split the total costs between these two micro units using this ratio. The most accurate way — instrument your software with customer tags: The last method requires a bit more effort but yields the most accurate unit costs. When you instrument your software and log granular activities, each micro-unit can now be mapped and related to a specific customer, using a customer id or name tag. With this type of mapping, you can accurately allocate costs including shared resources such as Kubernetes. 3. Get revenue per customer data Customer revenue data is usually stored in a CRM or an ERP application like Salesforce or Netsuite. This data will need to be fetched into a BI system or FinOps tool, and mapped and tagged per customer. 4. Create a margins dashboard Create a dashboard with the following information: Revenue per Customer Cost per Unit Economic (i.e., Cost per Transaction) Number of Units per Customer Margin per Customer  = (Revenue per Customer - Number of Units per Customer * Cost per Unit Economic) / Revenue per Customer * 100 5. Monitor changes in the margins per customer Since each component of the Margin Formula is dynamic and may change, anomaly detection is crucial in identifying parameter changes. As an example, if your FinOps team finds ways to reduce cloud costs, then it will increase the total margins for all your customers. Alternatively, your customer success team might have to offer a significant discount to customers, resulting in lower margins.  Anomaly detection systems are the best tools for monitoring these changes on a continuous basis, if you wish to know their impact proactively. What does unit economics mean for Anodot?    Our business monitoring product analyzes metrics and identifies anomalies. Therefore, metrics are our main KPI and unit of measure. Therefore, the simplest way for us to measure the costs of our customers is to count the total metrics for all of our customers, then divide the cost of all of our cloud costs and divide it by the total unit metrics. To improve our accuracy on cost, we need to break the metric structure down into micro-units. In our case, we look at the number of data points and requests per second (RPS) which can vary widely between metric types and customers. A high-resolution real-time metric can have x100 more data points than a daily resolution metric, for example. Next, we weigh the data points and RPS per customer to get a more accurate picture of the cost per customer. Finally, we collect the revenue data from Salesforce (Annual Recurring Revenue per Customer) and create a simple ratio between the cost per customer and the ARR to calculate an efficiency score — the lower the number, the higher the efficiency. Manage your costs and protect your margins   The market will reward and demand effective business models in the next few years. Growth remains a key indicator for both public and private firms, but the first half of 2022 has demonstrated that the market is looking for businesses that burn less cash. Because of this change in mindset, more companies are starting to think about how they can optimize their cost and margins, and no doubt the SaaS market will find a solution to its lack of visibility into margins. It is just a matter of time before new technologies will come up to offer a more accurate model than cloud cost.  Cloud services are the number one source of unexpected overspending for companies today, with engineering generally being free to consume them. It is important to remember that cost increases are not always bad. The point at which things need to be carefully considered is when costs increase but revenues don't. It has become increasingly challenging for companies to protect themselves from margin reductions and cost overuse. The allocation of multi-cloud costs is essential for understanding your actual cloud usage, establishing cloud cost ownership, and creating accurate budgets and forecasts.  With Anodot’s Business Mapping feature, you can accurately map multi cloud and Kubernetes spending data, assign shared costs equitably, and report cloud spend to drive FinOps collaboration for your organization. Anodot helps you understand your cloud unit economics by aligning your cloud costs to key business dimensions. Allowing you to track and report on unit costs and get a clear picture of how your infrastructure and economies are changing.

We are excited to share that Anodot’s Cloud Cost Management solution has been named a Challenger and Fast Mover in the 2022 GigaOm Radar for Evaluating Financial Operations (FinOps) Tools report. About the report The report from industry research firm GigaOm outlines issues, trends, and purchase considerations for organizations seeking solutions to help them reign in unanticipated and unplanned cloud costs.  Controlling cloud spend is especially important in today's challenging economic climate. After a period of hyper growth and business expansion that aligned closely with the rise of the cloud, businesses are looking to cut costs within their large line items.  At the same time, it's becoming more challenging to monitor and control cloud spend. Gaining granular visibility into complex, multicloud environments is difficult. Additionally, most companies are billed by cloud provides each month without clear reasons as to why there are cost fluctuations or how to accurately forecast future spend.  The GigaOm report aims to alleviate some of these challenges by presenting findings from the firm’s analysis of the relative value, progression, strategy, and execution of various product vendors, recognizing those who excel in their offerings. It’s a forward looking assessment that plots the current and projected position of each vendor.  The categories & criteria Among the vendor categories evaluated are small-to-medium businesses (SMBs), large enterprises, multinationals, and managed service providers (MSPs).  In terms of deployment models, software as a service (SaaS), hybrid, and self-managed solutions were all evaluated. The key criteria for recognition include: Normalized billing across multiple cloud vendors Cloud vendor cost comparisons Cloud rate optimization IT finance integration and chargeback Identification of cost optimization opportunities Real-time decision making   According to GigaOm, those set closer to the center are judged to be of higher overall value. And we are proud to be placed among those who bring the most value to organizations aiming at heightened efficacy in monitoring and controlling cloud costs. Proud to exceed in the market Anodot was recognized for exceeding the market on several key criteria among 13 other vendors.  These include normalizing billing across providers, where the analyst firm highlighted the solution’s: Delivery of granularity  Speed-to-awareness of cloud spend Ability to correlate spending by application across cloud vendors Forecasting accuracy was also noted as one of our solution’s strengths, further to its ability generate a one-year forecast with 95% accuracy, as based on only two months of historical data. Moreover, the solution’s ability to identify cost optimization opportunities was recognized for exceeding the market. Namely, other offerings extend only a trendline, but Anodot for Cloud Costs is AI-powered and can predict future costs, powering robust negotiation of long-term discounts with cloud providers. Another capability that sets Anodot apart in the report, is that we offer the unique ability to work with serverless and container spend, delivering the same level of forecasting accuracy for these kinds of workloads. According to GiagOm, this is an emerging area in FinOps that few vendors have yet to address. The GigaOm report also gives Anodot a high score for its flexibility in dealing with new types of workloads and IT projects, as well as for its scalability in meeting global enterprise needs.   Providing next generation capabilities  The analyst’s perspective in the report includes a prediction that in the coming years, the focus of FinOps tools will be on enforcing compliance with or exposing deviations to spend that has been approved. It is then stated that the best tools will be those that provide intelligent forecasting and optimization recommendations for future spend. This is what Anodot Cloud Cost is doing today, providing hyper-accurate forecasting of cloud costs with easy-to-implement recommendations that enable companies to cut unnecessary cloud costs and eliminate waste at savings of up to 40% on annual cloud spend. The need has never been greater With cloud costs being one of the most expensive resources for data driven businesses today and an average of 30% of those costs often going to waste, the need for continuous monitoring, deep visibility, and automatic alerts to usage anomalies, has never been more compelling. This is what Anodot for Cloud Costs delivers, enabling organizations to:  Improve cloud cost visibility at a granular level Reduce cloud waste and optimize cloud spend Allocate and control the multi-cloud budget  Accurately forecast cloud spend and usage  Track Kubernetes spending and usage across clusters  Easily track your spending and usage across your clusters with detailed reports and dashboards. Anodot for Cloud Costs’ powerful algorithms and multi-dimensional filters enable you to deep dive into your performance and identify under-utilization at the node level.  

VMware CloudHealth, a first-generation cloud management platform, has a strong legacy of delivering value for customers. But, since being acquired by VMware in 2018, innovation within the CloudHealth platform has not kept pace with the evolution of cloud cost management and FinOps practices, and with the Broadcom acquisition of VMware looming, the outlook for CloudHealth is increasingly uncertain. What Is VMware Tanzu CloudHealth?   VMware Tanzu CloudHealth is a cloud cost management software for over 20,000 worldwide organizations. Designed to optimize and help manage your multicloud setup, VMware Tanzu CloudHealth provides toolsets and dashboards designed to optimize and simplify, though, as mentioned above, their ability to keep pace with the modern needs of the cloud has declined since their 2018 acquisition.  Key Features of VMware Tanzu CloudHealth   VMware Tanzu CloudHealth includes a wide array of capabilities to help improve your multicloud experience, including:  AI-powered forecasting and budget management  Multicloud reporting and dashboards Anomaly detection Kubernetes optimization Migration planning recommendations GreenOps Cost chargeback and allocation Getting Started with VMware Tanzu CloudHealth   How you start working with VM Tanzu CloudHealth depends on your current cloud account setup. For example, if you’re working with Kubernetes, your setup will look like you are either using a helm chart to automatically deploy the Tanzu CloudHealth collector or deploying the Tanzu CloudHealth Collector to each individual cluster, though this varies depending on your deployment file.  Using the helm chart will call the Tanzu CloudHealth collector to gather your environment metadata. You’ll need the following prerequisites if you want to use this approach:  Helm 3.0+ Kubernetes version 1.12 or later Administrator privileges for deploying Tanzu CloudHealth collector in your cluster If you use the other option of deploying Tanzu CloudHealth into each cluster using a deployment file, you’ll need to manually configure a collector for each cluster. This is the only option for you if you’re using an older Kubernetes version.  On the other hand, your VMware Tanzu CloudHealth setup can look completely different if you’re looking to set up your AWS account or your GCP account. Make sure to carefully review the rules for each to ensure you’re establishing CloudHealth correctly.  Limitations of VMware Tanzu CloudHealth   Beyond the obvious limitations of VMware Tanzu CloudHealth of complicated and differing setup depending on your toolset, there are many other drawbacks:  Only a few basic savings recommendations Limited K8 visibility  Laggy features Unintuitive toolset  Forecasts and budgeting can be inaccurate Alerts are not customizable Unpredictable pricing structure  Designed for larger companies, not great for small to mid-sized organizations Few help documents Steep learning curve Compatibility issues with pre-existing infrastructure 50% of Anodot cloud cost management customers chose us to replace CloudHealth Companies like yours are switching to Anodot because they’ve exhausted the return on investment they are able to receive from CloudHealth, and are in need of a next-generation approach to cloud cost management that delivers exponential value atop their cloud investments. Deepest visibility and insights Visualize and allocate 100% of your multicloud costs (with K8s insight down to the pod level) and deliver relevant, customized reporting for each persona in your FinOps organization. Easy-to-action savings recommendations Reduce waste and maximize utilization with more than twice as many savings recommendations as CloudHealth, highly-personalized to your business and infrastructure with CLI and console instructions for easy implementation. Continuous cost monitoring and control Adaptive, AI-powered forecasting, budgeting, and anomaly detection empower you to manage cloud spend with the highest degree of accuracy and relevance, so the right people are automatically alerted to take action when needed to keep your cloud investments on track. Immediate value Day one, you’ll know how much you can immediately save, will begin relying on pre-configured, customized reports and forecasts, and can start eliminating waste due to our comprehensive, pre-purchase proof of concept process. Comparing CloudHealth CloudHealth Anodot Supported infrastructures VMware, AWS, Azure, Google Cloud, K8s AWS, Azure, Google Cloud, K8s Virtual tagging and cost categorization ✅ ✅ Cost allocation Perspectives provide powerful cost categorization, but feature is laggy at scale and targeting is limited Business mappings enable simple assignment of costs by any rule to any business object Preconfigured, customizable reporting for each persona ✅ ✅ Showback and chargeback ✅ ✅ Kubernetes Very limited K8s visibility and management capabilities; no savings recommendations Deepest K8s visibility; savings recommendations are still in development Savings recommendations Very few, basic recommendations across primary services; automatable; savings projections are inaccurate and inflated; no way to mute irrelevant insights 40+ easy-to-action recommendations across many services; configurable preferences; mute irrelevant recommendations; implementation instructions; accurate savings projections Rightsizing ✅ ✅ Forecasting and budgeting Inaccurate forecasts frustrate many customers Adaptive, AI-driven forecasting provides highest degree of certainty at multiple levels of granularity Anomaly detection and management Configure email alerts based on basic detection of anomalous activity that deviates from historical trend; Does not differentiate between noise and impactful activity Fully-automated AI detects anomalies in near real-time and alerts the appropriate teams only when risk is meaningful, enabling quick response and resolution Extensibility Multiple, fractured APIs leave much data inaccessible; supports Datadog and more as data sources Single, robust, easy-to-use API; Data source integrations in development Scalability User interface lags at scale and some features have upper scale limitations Unlimited scale to meet the enterprise demands Ease of use Frustrating, laggy interface, but visually-pleasing Intuitive, responsive, and visually-pleasing interface Pricing Unpredictable, dynamic pricing taxes customers based on 3% of all cloud spend; provided at low cost by MSPs Predictable, flat pricing based on large, capped tiers of cloud spend; provided at low cost by MSPs Outlook CloudHealth was acquired by VMware in 2018, precipitating a slowdown in product innovation. Broadcom acquisition of VMware puts the future of the CloudHealth product in doubt Anodot has recently doubled the size of the team supporting their FinOps product and publishes a public-facing roadmap that promises rapid innovation CloudHealth’s new name In September 2022, VMware announced that it was enclosing CloudHealth within its Aria suite, separate from the main VMware Aria Automation and VMware Aria Operations products as the standalone cloud cost tool. CloudHealth FinOps would also receive a new name, VMware Aria Cost powered by CloudHealth (CloudHealth). CloudHealth’s popular cloud security capabilities are now part of VMware Aria Operations for Secure Clouds, while the FinOps capabilities remain separate. This actually the second or third time VMware has attempted a renaming of CloudHealth since acquiring the tool in 2018. CloudHealth's future CloudHealth customers are heading into uncertainty with the constant changes happening within the platform. World Wide Technology CEO Jim Kavanaugh expressed: “We would love to build a strategic partnership with VMware. Unfortunately, I’m not sure that’s what they have planned.” Exploring CloudHealth alternatives with Anodot We boast seven key strengths that set us apart from the start: Accurate Forecasting and Budgeting: Our data feedback helps fine-tune your model for top accuracy. The autonomous forecast is up 24/7, crunching real-time data streams to give ongoing forecasts for smarter budgeting and cost savings. Cost Visibility and Control: We offer visibility for efficiently understanding multi-cloud and Kubernetes spending, helping you manage costs across all cloud accounts. Savings Recommendations: Over 80  real-time recommendations to monitor and optimize cloud costs and resource usage across AWS, GCP, and Azure. Dive deep into your data to see how your infrastructure is and get immediate savings. Real-time Anomaly Detection and Alerts: Our advanced algorithms identify irregular patterns and potential cost anomalies in real-time, alerting you to deviations from the norm. Automatic Savings Tracker: With automated report saving and tracking capabilities, you can effortlessly track and evaluate the performance of your recommendations. Multi-Tenant, Multi-Billing for MSPs and Enterprises: Consolidate and simplify billing operations for your customers on a unified platform. CostGBT for AI-Powered Cloud Cost Insights: Enhances user experience with contextual insights, cost projections, and answers to complex cloud cost queries with a simple search.

More enterprises are adopting cloud computing to ensure that they can accelerate innovation, stay competitive, and enjoy cost savings. This trend has only increased in the last two years with the rise of remote work necessitated by the COVID-19 pandemic. With the rise of cloud adoption, multi-cloud and hybrid cloud deployments are increasing in popularity as well. According to a Gartner survey, 81% of survey respondents are using two or more cloud providers. Another survey by Microsoft revealed 86% of respondents were planning to increase their investment in either multicloud or hybrid cloud environments. Benefits of multi-cloud Multi-cloud refers to a configuration where an organization is using two or more cloud vendors, and possibly their own private cloud, as part of their computing operations. The different fee structures and operating models of these disparate cloud resources make it extremely challenging to quantify costs and implement proper cloud cost management measures. The following benefits are driving many organizations to move from single public or private to multi-cloud environments:  Combining the strengths of each provider By selecting multiple cloud providers, a business can take advantage of the strengths of each provider's offerings. No matter the quality of each cloud vendor, some may not be able to provide all of the features and capabilities your organization needs.  Organizations often mix and match cloud services to suit the requirements of their business, workloads, and applications. Reducing outage risk  A cloud service outage can have a significant impact on organizations that fully rely on cloud operations. For example, a recent AWS outage affected Netflix, Ring, Disney, Slack, and McDonalds, among others. Leveraging multiple cloud vendors lowers the exposure that a system can be taken out by a single public cloud outage.  Meeting compliance requirements A multi-cloud approach allows businesses to use a mix of cloud providers to comply with statutory regulations such as the GDPR and the CCPA, which require companies to store customer data in specific geographic locations. Achieving greater cost and performance optimization Using a multi-cloud approach allows businesses to select the cloud provider that offers the best cost or performance benefits in a particular geography. Complexities of multi-cloud environments While using a multi-cloud environment offers definite benefits over a single cloud environment, there are certain complexities you should be aware of if your business is looking at a multi-cloud approach. These include: Security Businesses may find it challenging to secure and monitor all the different systems in a multi-cloud environment as there is no single control point to monitor security issues. Integration With applications spread across more than a single cloud, there should be a way to ensure that the multi-cloud architecture allows the transformation and delivery of enterprise data across silos.  Challenges in Optimizing Costs A multi-cloud environment is inherently complex. As a result, being able to monitor costs, identify waste, and put an appropriate optimization strategy in place can be challenging. This is due to low visibility into operations, especially considering the complexity of tracking multi-cloud costs across cloud service providers. Thankfully, there are automated management solutions that can simplify multi-cloud visibility and cloud cost monitoring complexities. Managing the costs of multi-cloud environments Businesses looking to operate in a multi-cloud environment need to practice effective multi-cloud cost management to take into account the costs of several cloud providers. A business can effectively enforce accountability with a better understanding of usage and costs. These capabilities can improve your multi-cloud cost management: Visibility  With different cloud providers having disparate reporting interfaces, at times, it may be challenging to get a holistic view of the costs you are incurring in a multi-cloud environment. You should choose a tool that lets you get full visibility of your cloud spend, across all cloud environments.  Unified view  Each cloud provider has its own billing rules and tools, most of which are complex. Many organizations find it challenging to proactively understand and control cloud costs across multiple vendors. Having a single dashboard and a unified view of all cloud activities will help your business manage cloud costs in an efficient manner. Focus on cost efficiencies Many cloud cost monitoring services let businesses get visibility into where and how they spend their cloud resources. As a result, businesses can forecast and plan alternate scenarios that may result in greater cost efficiencies. A key technology to consider is Kubernetes which can help drive multi-cloud cost management as it lets organizations achieve full redundancy by running containers in multiple clouds. Use agnostic AI and machine learning driven monitoring Payment companies that use agnostic AI and ML-driven business monitoring can detect outages well before they actually occur. As a result, IT teams can take appropriate actions in real-time to mitigate damages or even migrate to a different cloud without any downtime. Furthermore, since the analytics and monitoring are agnostic, the IT teams don’t need to change the monitoring platform while moving between clouds. Assess your multi-cloud visibility A clear understanding of your cloud and Kubernetes usage and costs is critical to getting the most value out of your multi-cloud investment. To understand if you have complete visibility, start with these questions: Can you see all of your multi-cloud and Kubernetes data in one screen? Is your organization successfully executing your tagging strategy and can you tag untagged resources? Can you accurately tie spending data to relevant business dimensions? Does each stakeholder in your organization have the views and dashboards they need? Can you detect anomalies across cloud providers and teams? What to look for in a multi-cloud cost management solution? AI-powered An AI-powered multi-cloud management solution is a flexible and scalable solution that helps mitigate many of the challenges businesses face in a multi-cloud environment. Specifically, advanced AI monitoring solutions will help you get valuable insights into the metrics of the entire operation. Such a solution will give you an actual picture of all cloud costs by analyzing relevant data. You can also accurately correlate metrics with costs with the help of specialized algorithms. Anomaly detection  Real-time anomaly detection is another essential feature to look out for in a multi-cloud management solution. System administrators will get real-time alerts when there are unusual cost spikes or patterns. AI-powered anomaly detection autonomously works across cloud infrastructures. This allows organizations to resolve negative cost issues before a shocking bill arrives.  Complete visibility into end-to-end cloud operations A multi-cloud management solution should provide administrators with complete visibility of all cloud operation data. With the help of this information, administrators can decide on how to best optimize cloud resources by balancing budgetary constraints against business requirements. Multi-cloud cost management with Anodot Anodot seamlessly combines all of your business's cloud spend into a single platform. With Anodot's cloud cost management solution, you can monitor and optimize your cloud costs and resource utilization across Azure, GCP, and AWS.  Anodot includes a single view of cost and usage metrics across multiple clouds. Users have the ability to filter costs in multiple ways including payer accounts and linked accounts to gain an itemized view by developer or line of business.  With Anodot, you can easily visualize and report costs with unlimited views and ML-powered savings recommendations, budgeting, forecasting, and anomaly detection to help you continuously control costs.

Most companies today have a “cloud first” computing strategy. According to Foundry’s April 2022 report outlining their 2022 Cloud Computing research, 92% of businesses globally have moved to the cloud. What’s more, the percentage of companies with most or all of their IT infrastructure in the cloud is expected to leap from 41% today to 63% in the next 18 months. As companies move more workloads onto various cloud platforms, cloud budgets continue to increase. Foundry reveals that, on average, organizations will spend $78 million on cloud computing over the next 12 months, up from $73 million in 2020.  With burgeoning growth of cloud computing, it should be no surprise that IT decision makers say one of the biggest obstacles to implementing their cloud strategy is controlling cloud costs. Long gone are the days of highly predictable and stable costs and change management processes that were the hallmark of legacy computing architectures.  The Challenge of Controlling Cloud Costs The very nature of cloud computing – and indeed, a reason that companies flock to it – is that compute capabilities can change rapidly to accommodate current business demands. Capacity can grow or shrink automatically by turning (billable) resources up or down. Each time the overall IT environment expands with new VMs here and additional storage there, increases in complexity drive the total cost of cloud usage higher. It’s easy to spin up cloud instances without oversight from IT or Finance. Developers do it every day as they create, modify and test applications. There is no formal change management process where a committee oversees the turnup of a dozen new VMs; this would take too long in a time-sensitive work culture. As a result, invoices for cloud resources can be a shock at the end of the month. Unfortunately, many companies don’t have total visibility of their cloud assets—some of which are created and forgotten as time goes on. Developers can login to the cloud platform at any time and add, delete, or modify operations. Individual teams or departments may have different methods for managing cloud resources and costs. All of this takes place under the demand for speed in operations to get to market first. Another challenge is the complexity of cloud providers’ billing processes. The pay-as-you-go services tend to offer many confusing options that are billed as separate components, making it difficult to understand what components tie back to which applications. The Rise of FinOps Cloud billing complexity has spawned the creation of an entirely new financial management role known as FinOps, defined by the FinOps Foundation as “an evolving cloud financial management discipline and cultural practice that enables organizations to get maximum business value by helping engineering, finance, technology, and business teams to collaborate on data-driven spending decisions.”  Other names for the practice include cloud financial management, cloud financial engineering, cloud cost management, cloud optimization, and cloud financial optimization. Regardless of the moniker, companies are finding it necessary to have specially trained people who can cross the barriers between the usage of cloud infrastructure and cloud cost management. Check out these tips for maximizing cloud ROI Predicting Cloud Costs is Difficult  Most cost forecasting tools base their numbers on what has been used and spent in the previous month. However, the very nature of the cloud is that it can automatically expand and contract according to work demands. Thus, cloud spend is variable and inherently difficult to predict. Furthermore, there can be seasonality in those work demands. For example, an online store is likely to see increased activity in the pre-holiday months of November and December. If November’s spend forecast is based on October’s activity, that forecast could be greatly underestimated and very inaccurate. Forecasts should be done frequently to know when the company is deviating from the budget. Even small deviations can result in big cost overruns. If a forecast is only done monthly, by the time a month passes, it can be too late to make adjustments that can help control costs. Many companies are multicloud, meaning they have two or more cloud platform providers. The tools necessary to make cost forecasts may be platform-specific and only work on one cloud, increasing the complexity of generating an overall forecast. Cloud technology is evolving quickly—from VMs, to containers, to serverless and whatever’s next. Some forecasting tools can’t delve into all the technologies, leaving a gap in forecasts where there is no visibility. Benefits of Forecasting Cloud Costs Despite the challenges of getting a truly accurate forecast of cloud expenditures, the benefits of doing so are valuable. In a recent 451 Research study, respondents indicated they saved 56% on cloud costs as the result of applying Cloud Financial Management (CFM) practices in their organization.  Controlling spend and knowing when a budget is about to be busted gives the organization an opportunity to make a fix to prevent excessive cost overruns. Real-time forecasts are most helpful in detecting when spending is going off the rails. Getting an Accurate Forecast into Cloud Costs The first step in getting an accurate cost forecast is to gain complete visibility into cloud costs, meaning, understanding what is being spent on cloud services in real time and having the ability to correlate cloud spend with business KPIs. The three major cloud platforms – Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure – all have native tools to help estimate costs. The tools are, respectively, AWS Cost Explorer, Google Cloud Billing, and Microsoft Azure’s online pricing calculator.   These native tools only work for their own cloud platforms, so in this approach, a multi-cloud organization would have to use multiple solutions. What’s more, the tools may not get to the level of visibility, detail, and frequency that an organization needs. They may not deliver information in real time, which is necessary to effictively control  spending.  Cost forecasting is a good use case for artificial intelligence (AI) analytics. In this approach, real-time continuous data feeds let the organization analyze cost changes as they are happening, not long after the fact. The underlying machine learning (ML) models can account for seasonality and other factors that could have a legitimate (i.e., expected) impact on spend. Moreover, the data feed can come from various sources, including multiple cloud platforms. When unexpected changes in costs take place, AI analytics can alert on a deviation as it is happening. This gives the organization an opportunity to investigate the root cause and make adjustments if necessary to prevent excessive cost overruns. Cloud Cost Forecasting with Anodot Anodot’s Cloud Cost Management solution helps organizations get a handle on their true cloud costs by focusing on FinOps to drive better revenue and profitability. From a single platform, Anodot provides complete, end-to-end visibility into an organization’s entire cloud infrastructure and related billing costs. By monitoring cloud metrics together with revenue and business metrics, Anodot enables cloud teams to understand the true cost of their cloud resources, with benefits such as:  AI-based analysis for identifying inefficiencies and anomalies – With the help of machine learning and artificial intelligence, Anodot’s cloud cost solution analyzes data to find gaps and inefficiencies in the system. It can also catch anomalies in various parameters such as usage, cost, performance, etc., thus solving the inefficiency challenge.  Real-time cost monitoring – Monitoring cloud spend is quite different from other organizational costs in that it can be difficult to detect anomalies in real time. Cloud activity that isn’t tracked in real-time opens the door to potentially preventable runaway costs. Anodot enables companies to detect cost incidents in real time and get engineers to take immediate action.  Cost and usage forecasting – Anodot’s AI-driven solution analyzes historical data in order to accurately forecast cloud spend and usage by unit of choice, anticipate changing conditions, and get a better read on related costs. This helps organizations to make more informed budgeting decisions and find the right balance between CapEx and OpEx.  Savings recommendations – Anodot helps organization to continuously eliminate waste and optimize their cloud infrastructure with personalized recommendations for unknown saving opportunities that can be implemented in a few steps. The dashboard below illustrates how Anodot reports on cloud infrastructure costs. As cloud adoption and cloud spending grow, so does complexity and waste. Forecasting cloud spend is becoming more important for Finance and FinOps teams. Learn how Anodot can help. Request a demonstration today.

As a member of the FinOps organization, Anodot is excited to sponsor the upcoming FinOps X event in Austin, TX.  Anodot's mission has always been to help organizations solve one of the most recognized challenges associated with public cloud adoption — cost control and optimization. Every feature of our Anodot cloud cost management platform has been built by taking a core FinOps market concern and working backward to deliver a capability that fills that need. Our product team works closely with customers from different business segments and companies of all sizes, including Nice and Trax, to identify and address their cloud adoption challenges. We develop solutions that directly address our customers’ needs and provide significant value. Examples include the development of features such as K8s container costs, unit economics, anomaly detection, budgeting, forecasting, and more. These enhancements are  the result of listening and working with our customers to solve their most pressing issues. The FinOps Foundation We’re proud to announce that Anodot is sponsoring the FinOps Foundation’s premier event, FinOps X. The FinOps Foundation is a program of The Linux Foundation ,dedicated to advancing people who practice the discipline of cloud financial management through best practices, education, and standards. The foundation has developed the FinOps framework, an evolving cloud financial management discipline and cultural practice designed to bring accountability to cloud spend and enable organizations to get maximum value by helping engineering, finance and business teams to collaborate on data-driven spending decisions.  The framework also outlines six guiding principles needed for a successful FinOps journey: Establish a culture of collaboration across IT, product, operations, and finance teams.  Accountability for cloud costs at the feature and product team level A centralized team responsible for purchasing commitments and negotiating vendor agreements.  All teams using cloud infrastructure should have access to timely reports.  Make decisions based on business KPIs.  Take advantage of the cloud's variable cost model. The FinOps journey consists of three iterative phases — Inform, Optimize, and Operate. The Inform phase provides visibility into cloud costs, allocation, budgeting, forecasting, and helps develop shared accountability by showing teams what they spend and why. In the Optimize phase, teams are empowered to take the right optimization actions based on their goals. During  the Operate phase, objectives shared by IT, Finance, and business leadership  are refined to focus and scale operational efforts through continuous improvement by breaking down the silos between teams. To succeed in this journey, an organization must create a culture of FinOps which involves building a Cloud Cost Center of Excellence built around business, financial, and operational stakeholders and defining appropriate governance policies and models. FinOps phases by FinOps Foundation Anodot has developed a next generation Cloud Cost Management solution that is well aligned with the FinOps Framework and our customers' needs. Let's take a closer look at how Anodot supports the successful FinOps journey through the inform, optimize and operate phases, as well as aligns with the FinOps Foundation principles.   Inform — Visibility & Allocation Anodot provides full visibility into AWS, Azure, and GCP costs and usage data. Our dashboards and reporting are easy-to-use and accessible to anyone in the organization, and we process the data every few hours so it’s always up to date.  Using a robust data collection mechanism, we can support complex customer organization structures with multiple organizations, thousands of accounts, and millions of records. Additionally, we've developed advanced reporting capabilities to address some of the most complex challenges organizations face, such as Kubernetes cost monitoring, allocation, and optimization.  With Anodot, you can analyze Kubernetes clusters usage reports, drill down on node and pod utilization, and breakdown costs by namespaces, deployments and more. Anodot provides cross-organizational visibility into costs and usage data, tracks business KPIs, and is used by Finance teams for financial reporting, chargebacks, and cost allocation.   Optimize — Rates & Usage Anodot has developed the most advanced recommendation engine available on the market today. The engine tracks your usage data, utilization metrics, and pricing options across AWS, Azure, and GCP to support your FinOps journey, and pin-point and prioritize optimization efforts.  Anodot provides immediate (day 0) savings opportunities that go beyond compute and storage rightsizing with personalized cost optimization recommendations, waste trends, and exclusions for over 40 types of waste. Anodot’s recommendation engine allows our customers to take continuous action to avoid waste, overprovisioning, and save millions of dollars every day. “Anodot gives us visibility and control on cloud billing at a granularity that we have never seen before. The recommendations that they generate save us a huge amount in our cloud bill.” Rubi Cohen - Cloud Manager, Amdocs Operate — Continuous Improvement & Operations Anodot for Cloud Cost was developed with design partners which run large scale Enterprise-grade cloud operations, such as Amdocs and Nice. As part of this process, we partnered with leading CCoE teams to learn about their needs and developed the tools to enable cross-team collaboration, continuous improvements in KPIs, and organization accountability for cloud costs. With advanced budgeting, forecasting, and anomaly detection capabilities, we help operations better control cloud spend and respond to usage spikes immediately. ”Anodot gives me visibility into how much each of my SaaS customers costs within a dynamic microservice architecture . This information is key for our pricing strategy.” Mark Serdze - Director of Cloud Infrastructure, Trax Take your FinOps to the next level with Anodot Anodot’s alignment with the vision of the FinOps Foundation strengthens our ability to continue innovating for our customers and developing the best Cloud Cost Management  platform.  By seamlessly combining all cloud spend into a single platform our customers can optimize their cloud architecture across AWS, GCP, and Azure; make data-driven trade-offs; and get a handle on true cloud costs by focusing on FinOps to drive better revenue and profitability.  Getting started is easy! Try Anodot for Cloud Costs with a 30-day free trial to instantly get an overview of your cloud usage, costs, and expected annual savings — or Book a demo with our Cloud Optimization experts.

The core value Anodot delivers to customers is AI-powered, autonomous monitoring of critical business KPIs in order to protect revenue and manage costs. But Anodot's value extends beyond our product — to our people. Each Anodot customer has a dedicated Customer Success Manager (CSM) to ensure they are getting maximum value and ROI from Anodot's platform. We'd like to highlight one of our Customer Success Managers, Uriah Mitz, who is working with global eCommerce giant, PUMA. Uriah has more than 6 years of experience implementing AI and ML products. He tells us in his own words about his experience working with PUMA and helping them achieve their business goals. Customer Success My role as a CSM involves a deep understanding of the customer’s vertical, the customer’s environment and the customer’s needs in order to provide the best solution and get the most value from Anodot. A good CSM has to be customer-oriented and have a strong sense for people and business. At Anodot, our goal as CSMs is not trying to sell the customer additional products. Rather, we focus on leading and supporting the customer since from the kick off meeting until the customer is fully on-boarded and independent. https://youtu.be/f6UYebNtjos PUMA's pain points PUMA's Senior DevOps Manager, Michael Gaskin, was interested in Anodot based on the experience he had with another Anodot customer. Michael understood the difficulties he was facing and wanted to monitor all revenue aspects of Puma’s websites which were not clear enough. Before Anodot, Puma did not have a tool to distinguish what was normal, or abnormal, across their 45 eCommerce websites.  For example, one of the revenue incidents caught by Anodot was gift card purchases in Switzerland that were not working. In general, for a website that spans many countries, gift card purchases appeared to be working well, but shortly after we implemented payment types into Anodot we discovered the problem in Switzerland which could have cost Puma a lot if it was discovered later.  Onboarding with Anodot At Anodot, we first try to understand the primary pain points of the customer. When we fully understand the challenges, we discover with the customer the needed dimensions we want to measure. We build a diagram of the pain point, how we are going to tackle it based on the available data, where the data will be fetched and the time resolution we want to monitor. Integration with Anodot is very simple. We have plenty of data sources under our Business Collectors umbrella and we can connect to any data source in 3-4 minutes. After integrating the data we want to monitor. Our AI-powered system automatically starts to analyze business data, finding seasonality behaviors and detecting anomalies. At this point, the customer gets full training of the system, including how Anodot works, how to see the data, how to find the relevant anomalies, how to create new alerts, how to tackle complexed issues with influencing metrics, injecting events in timeline, etc. The average onboarding process usually takes up to 6 weeks. [CTA id="3509d260-9c27-437a-a130-ca1595e7941f"][/CTA] PUMA Use Cases With Puma, we integrated revenue measures first as this is was their initial goal for using Anodot. However, while working with data, we decided to expand our view to a much broader metrics than just revenue. We looked at the data and went backwards: How many transactions are made every minute? How many items in average for each transaction? What is the conversion rate? How many items added to the cart? What is the % of add to cart and items per transaction? What is the # of returning customers? We also added dimensions to all of those measurements (KPIs) such as payment method, currency, language, country, etc. All of these dimensions help Puma find the root cause of the problem related to the buying funnel (TTD) faster and to fix it much earlier than if they didn't have Anodot. (TTR) Future Focus - Future Verticals In addition to all of the above, we are currently working on adding another measure - the amount of website failures to measure the user experience in order to fix issues faster (improvement of TTD and TTR). In the near future we will add more use cases, such as customer experience, by measuring the processing time of the website. We will work on ads effectiveness by measuring the logins from ads worldwide and measuring the success rate of campaigns by adding events to Puma’s timeline to better understand sales behavior and much more. The Power of Anodot Anodot's AI-powered business monitoring solution opens a window to insight that no one has ever seen before in the business. By dicing data into multiple dimensions, problems that aren’t known and trends that no one has ever seen become crystal clear. No more wasting time attempting to understand the root cause of a drop in a static dashboard, no more time waste on false positives, or guessing invisible trends trying to be compared wrongly. Metric correlations in Anodot is a powerful tool which can help companies in any vertical understand business in a perspective never seen before. From a point of view of a CSM, it’s an exciting journey every time.

96% of companies now use or are in the process of evaluating Kubernetes. As the maturity and complexity of Kubernetes environments grow, costs quickly spiral out of control when an effective strategy for visibility and optimization is not in place. Managing Kubernetes (K8s) Costs is Critical to Realizing Cloud-Driven Revenue Growth The COVID-19 pandemic accelerated digital transformation, driving businesses to double down on the cloud to scale up services and support ‘never-seen-before’ load and demand (e.g., Zoom), and in some cases, efficiently scale down applications in response to changing user patterns (e.g., Uber). As a result, organizations have scrambled to modernize application development processes and re-architect static, on-premises monoliths as agile, microservice-powered cloud apps, fueling the adoption of containers and container orchestration tools like Kubernetes. All major public cloud providers now offer managed K8s services, and according to CNCF’s Annual Survey for 2021, 96% of organizations are already using or evaluating Kubernetes. The promises of Kubernetes are shorter software development and release cycles, easier application upgrades and maintenance, better utilization of cloud resources, on-demand scale, and portability between clouds — all potential drivers of corporate revenue growth. However, in practice, Kubernetes has introduced potent risks to revenue growth, primarily due to the complexity it drives: Lack of internal experience and expertise with K8s architecture and management have forced businesses to invest in training, outside services, and expensive consultant engagements High-profile attacks have heightened concerns about security, driving additional budget and investment against vulnerability testing, hardening, and policy enforcement Engineers and architects, who historically did not have to worry about operational costs, are now on the hook for the financial impact of their code’s resource utilization, their node selections, and pod/container configurations This guide is designed to help your cross-functional Kubernetes value realization team — whether you call it cloud FinOps, your Cloud Center of Excellence, or it is a simple partnering of DevOps and Finance — come together and remove barriers to maximizing the revenue return on your business’ investment in Kubernetes. Inform: Empower Kubernetes Stakeholders with Visibility Relevant to Their Role Stakeholders in managing your Kubernetes deployment costs extend far beyond your end users. Typical K8s cost stakeholder parties include: Application end-users Business unit leaders App users within each line-of-business Your application engineering team Your DevOps team and practitioners Kubernetes admins, engineers, and architects Your Finance or IT Finance team Any formalized FinOps organization with your business or Cloud Center of Excellence Delivering transparency and a single-source-of-truth system for Kubernetes usage data is table stakes for each of these personas, and is required to align business, operations, and DevOps teams. Dashboard, reports, and alerts are all common methodologies of providing visibility, and leading tools will enable customization of views per persona so that each user sees only the data that impacts their role. Specific visibility requirements will vary per persona and per team. Typical requirements include varying levels of granular visibility (from your clusters to their containers) and analytics across all your public clouds, including non-container resources and workloads. From a reporting and dashboards perspective, users demand instant data on current K8s cost trends and forecasted costs. Sophisticated multicloud cost management platforms like Anodot enable the per-role visibility business stakeholders need by: Visualizing and tracking Kubernetes spending and usage across clusters, namespaces, nodes, and pods Correlating cloud spending with business KPIs Enabling the deepest visibility, analysis, and breakdowns for the costs of non-K8s and Kubernetes cloud components as individual and shared costs, by cost center, and by other levels of categorization and virtual tagging Enabling you to unify Kubernetes label keys and traditional resource tag keys to build a combined allocation model Optimize: Leverage Intelligent Recommendations to Continuously Optimize Kubernetes Costs and Usage After enabling appropriate visibility across all your stakeholders, you and your FinOps team can finally take on the task of optimizing and reducing Kubernetes spending. With comprehensive K8s visibility, you can fine-tune Kubernetes resource allocation — allocating the exact amount of resources required per cluster, namespace/label, node, pod, and container.  Monitoring and configuring your Kubernetes deployments properly will improve infrastructure utilization, reduce instances of overprovisioning, and reduce application infrastructure costs. Actually implementing continuous optimization procedures proves challenging for many organizations, even with enough visibility. Prioritizing optimizations is a challenge, and in many organizations, getting the engineering buy-in and cycles to actually implement the infrastructure changes that have been identified as cost-saving measures is difficult (as evidenced by multiple FinOps Foundation studies that have identified “Getting Engineers to Take Action” as the recurring primary priority of FinOps teams). Anodot  provides a shared source of cost visibility and cost optimization recommendations, making continuous improvement a scalable task for multi-stakeholder teams by: Making next-step actions to implement optimizations blatantly evident (with explicit management console instructions or CLI commands) Specifically outlining the cost impact of each optimization change Helping your team to Identify anomalies and underutilization at the node and pod level in an ongoing way Check out these tips for maximizing cloud ROI Operate: Formalize Accountability and Allocation for Kubernetes Costs As a FinOps strategy leader, you must gain consensus and instill proper financial control structures for Kubernetes within your organization. FinOps strategies without accountability and alignment are doomed to failure. Financial governance controls further reduce the risk of overspending and improve predictability. This operating phase is where the rubber meets the road as far as what results you will gain from your Kubernetes FinOps efforts. If you have put the right controls in place and have an effective formalized cost management process, your team will be enabled to: Effectively and fully transition from the slow, on-premises CapEx model to the elastic, real-time OpEx model enabled by the cloud Move from the old-world paradigm of Engineering as requestors/Finance as approvers to Engineering and Finance acting as one Rully replace predictable, static hardware spend (with long procurement processes) with predictable budgets for on-demand (instant procurement) container resources All of which helps your organization transition from the antiquated physical infrastructure world with high cost of failure to a paradigm that enables affordable “fast failing” and agile experimentation. But, how do you ensure formalized accountability practices and procedures are in place? We have established that cost efficiency is a shared responsibility, with the FinOps team in charge of standards. Your FinOps stakeholders must stand up the proper guidelines, cost monitoring, alerting, and optimization processes. Within these constructs, Engineering is tasked with making sure their investments are cost-minded and efficient. There are additional specific actions you can take to enforce and enhance accountability and cost allocation practices, through: Organizing resources by application and, when possible, using dedicated clusters for each app Flexibly and strategically defining and assigning namespaces and labels to align usage with cost centers (application, team, or business unit), and unify this approach with traditional resource tagging so you can allocate costs, analyze by cost centers, and perform full allocation across K8s and non-Kubernetes workloads. Making sure that the teams that are driving costs (in DevOps/Engineering) have cost and usage information at hand, in addition to providing these same details to your product, project, and system owners and managers Delivering visibility into which committed-use strategies are in place: this can help incentivize Engineers to leverage Savings-Plan-ready instances over incumbent choices Regularly hosting review sessions with stakeholders to review high level dashboards and socialize the cost impact of optimizations Have a solid and comprehensive Kubernetes showback model in place, and leverage the aforementioned visibility and reporting capabilities (like those enabled by Anodot) to help your teams understand how they are doing in terms of costs. Chargeback approaches (where stakeholders are directly invoiced for their cloud spend impact) are appropriate for teams that have required visibility and education, but avoid creating a culture of Kubernetes cost shameback — which emphasizes inefficiencies and weaknesses rather than building communication, mentorship, and shared education efforts that enable cross-organizational wins. Above all, create a fluid flow of communication about what efforts are being made,  and what savings results are being achieved. Loudly champion any and all wins and successes. Cloud and Kubernetes cost management tools like Anodot help automate and centralize much of this work: Automated alerting and reporting can appear within the tools and interfaces your teams already use to show them usage and savings impact without forcing them to regularly open and consult another solution Calculate Kubernetes unit costs and answer the question, “for each dollar spent in K8s, how many dollars of revenue did we generate?” Help Engineers to take ownership of the cost impact of their choices by showing the results of cost-conscious resource provisioning and utilization [CTA id="dcd803e2-efe9-4b57-92d5-1fca2e47b892"][/CTA] Building Your Strategy for Operationally Maximizing K8s ROI A successful financial management strategy for Kubernetes infrastructures in the public cloud — whether on AWS, Azure, or GCP — requires educating and uniting stakeholders from parties as diverse as Finance and DevOps around shared goals and processes. Step 1: Understand Kubernetes Cost Drivers First, stakeholders from each line of business that consumes Kubernetes services and the FinOps governing team must develop at least a basic awareness and understanding of each K8s cost driver’s function and importance (both direct and indirect). Step 2: Align on K8s Optimization Strategy and Tools Next, these same stakeholders can evaluate different strategies for controlling and optimizing costs against each cost driver and identify those that make sense in accordance with the business’ specific focus and goals and objectives. At this time, it also makes sense to evaluate the Anodot Cloud Cost Management  tool that provides comprehensive, cross-cloud (multicloud) and cross-technology (AWS, Azure, GCP + Kubernetes) visibility, optimization, and forecasting capabilities. Anodot is often selected at this stage by organizations that are focused specifically on financial management of cloud and Kubernetes, and who prefer to have a single, focused tool that drives cloud and K8s ROI. Step 3: Implement a Continuous Kubernetes Optimization Practice Finally, a FinOps plan for operationalizing the selected strategies in an ongoing manner can be created by leveraging the Inform > Optimize > Operate cyclical framework. Detecting Kubernetes Cost Anomalies “Bill shock” is too common an occurrence for businesses that have invested in Kubernetes. Anomaly detection intelligence will continuously monitor your usage and cost data and automatically and immediately alert relevant stakeholders on your team so they can take corrective action. Anomalies can occur due to a wide variety of factors and in many situations. Common anomaly causes include: A new deployment consuming more resources than a previous one A new pod being added to your cluster Suboptimal scaling rules causing inefficient scale-up Misconfigured (or not configured) pod resource request specifications (for example, specifying GiB instead of MiB) Affinity rules causing unneeded nodes to be added Save your team the pain of end-of-month invoice shock. Any organization running Kubernetes clusters should have mechanisms for K8s anomaly detection and anomaly alerting in place. Anodot for Kubernetes Cost Management  Anodot’s cloud cost management solution gives organizations visibility into their Kubernetes costs, down to the node and pod level. By combining Kubernetes costs with non-containerized costs and business metrics, businesses get an accurate view of how much it costs to run a microservice, feature, or application. Anodot provides granular insights about your Kubernetes deployment that no other cloud cost optimization platform offers, with the ability to easily connect to AWS, Azure and GCP.  Anodot helps your FinOps and DevOps teams work together to identify and eliminate waste, so you can maximize the value you get from your cloud environments. Try Anodot with a 30-day free trial. Instantly get an overview of your cloud usage, costs, and expected annual savings.

As the complexity of Kubernetes environments grow, costs can quickly spiral out of control if an effective strategy for optimization is not in place. We've compiled expert recommendations and best practices for running cost-optimized Kubernetes workloads on AWS, Microsoft Azure, and Google Cloud (GCP).   What Is Kubernetes Cost Optimization?   Kubernetes cost optimization is the practice of maintaining Kubernetes infrastructure and workload performance while optimizing cost-efficiency to the max. In other words, it’s a way of improving your Kubernetes performance while maintaining reliability. This entails identifying areas of the Kubernetes environment that are less cost-efficient than others.  Cost optimization strategies include: Minimizing your number of servers and reducing environment services. Autoscaling your application or cluster to meet demands and saving costs by shutting down when demands decrease. Sharing resources across multiple servers.  Optimizing network usage.  Improving node configurations.  Optimizing storage space.  Regularly using sleep more.  The Importance of Kubernetes Cost Optimization   Kubernetes cost optimization is vital because of how much money it can save your organization while improving infrastructure value, operational efficiency, and scalability. It enables you to deliver high quality services while saving money on Kubernetes spend.   Without cost optimization, Kubernetes spend can become inefficient, leading to wasted resources, budgets, and your company time.  Which Factors Contribute to Kubernetes Costs?   Something important to note is that there is no one thing that leads to your Kubernetes bill breaking your budget. The tricky part of Kubernetes cost optimization is that often a lot of very small costs can pile up, unnoticed, in the background. The following are all factors that are likely contributing to your Kubernetes bill:  Compute costs. Since Kubernetes requires compute resources to power workloads and operate the control panel, it can be tricky to keep track of how much you're spending. Monitor how many applications you're running and keep an eye on the number of servers that you join to your clusters – because that's all going on your bill! Storage costs. Kubernetes storage costs vary depending on your chosen storage class and the amount of data you want to store. For example, costs vary enormously depending on if you use HDD or SSD storage.  Network costs. If you're using a public cloud to run Kubernetes, you need to pay networking costs. This includes degrees fees, fees which cloud provides require when you move data from their cloud to another infrastructure.  External cloud service costs. Depending on how many third-party services and APIs you use in your Kubernetes clusters, your external cloud services costs might be quite high. Your bill will increase depending on the type of service, the amount of data or calls exchanged, and the service-specific pricing model.    What Are Kubernetes Cost Optimization Tools?   If you're looking for the best way to improve your Kubernetes spend without spending hours of your time combing through data, you need a Kubernetes optimization tool. Kubernetes optimization tools provide a real-time view into your cloud usage. Expect granular levels of detail about cost and resource allocation, as well as spending anomaly detection and budget forecasting.  A Kubernetes optimization tool can improve anything from organizational visibility into the cloud, task automation for scaling and cost management, deployment scalability, to regular updates and support.  Considering adding a Kubernetes cost improvement tool to your digital suite? Anodot provides Kubernetes cloud cost management tool to help you optimize your cloud spend so you can put your dollars to work elsewhere.  Gaining Complete Kubernetes Cost Visibility   Gaining visibility into your container cost and usage data is the first step to controlling and optimizing Kubernetes costs. Visibility is critical at each level of your Kubernetes deployment: Clusters Nodes Pods (Namespaces,  Labels, and Deployments) Containers You will also want visibility within each business transaction. Having deep visibility will help you: Avoid cloud “bill shock” (a common compelling incident where stakeholders find out after-the-fact that they have overspent their cloud budget) Detect anomalies Identify ways to further optimize your Kubernetes costs For example, when using Kubernetes for development purposes, visibility helps you identify Dev clusters running during off-business hours so you can pause them. In a production environment, visibility helps you identify cost spikes originating from a deployment of a new release, see the overall costs of an application, and identify cost per customer or line of business. Detecting Kubernetes Cost Anomalies   “Bill shock” is too common an occurrence for businesses that have invested in Kubernetes. Anomaly detection intelligence will continuously monitor your usage and cost data and automatically and immediately alert relevant stakeholders on your team so they can take corrective action. Anomalies can occur due to a wide variety of factors and in many situations. Common anomaly causes include: A new deployment consuming more resources than a previous one A new pod being added to your cluster Suboptimal scaling rules causing inefficient scale-up Misconfigured (or not configured) pod resource request specifications (for example, specifying GiB instead of MiB) Affinity rules causing unneeded nodes to be added Save your team the pain of end-of-month invoice shock. Any organization running Kubernetes clusters should have mechanisms for K8s anomaly detection and anomaly alerting in place — full stop. [CTA id="dcd803e2-efe9-4b57-92d5-1fca2e47b892"][/CTA] Optimizing Pod Resource Requests   Have organizational policies in place for setting pod CPU and memory requests and limits in your YAML definition files. Once your containers are running, you gain visibility into the utilization and costs of each portion of your cluster: namespaces, labels, nodes, and pods. This is the time to tune your resource request and limit values based on actual utilization metrics. Kubernetes allows you to fine-tune resource requests with granularity up to the MiB (RAM) and a fraction of a CPU, so there is no reason to overprovision and end up with low utilization of the allocated resources. Node Configuration    Node cost is driven by various factors, many of which can be addressed at the configuration level. These include the CPU and memory resources powering each node, OS choice, processor type and vendor, disk space and type, network cards, and more.  When configuring your nodes: Use open-source OSes to avoid costly licenses like those required for Windows, RHEL, and SUSE Favor cost-effective processors to benefit from the best price-performance processor option: On AWS, use Graviton-powered instances (Arm64 processor architecture) In GCP, favor Tau instances powered by the latest AMD EPYC processors Pick nodes that best fit your pods' needs. This includes picking nodes with the right amount of vCPU and memory resources, and a ratio of the two that best fits your pod’s requirements. For example, if your containers require resources with a vCPU to memory ratio of 8, you should favor nodes with such a ratio, like: AWS R instances Azure Edv5 VMs GCP n2d-highmem-2 machine types In such a case, you will have specific nodes options per pod with the vCPU and memory ratio needed. Processor Selection   For many years, all three leading cloud vendors offered only Intel-powered compute resources. But, recently, all three cloud providers have enabled various levels of processor choice, each with meaningful cost impacts. We have benefited from the entry of AMD-powered (AWS, Azure, and GCP) and Arm architecture Graviton-powered instances (AWS). These new processors introduce ways to gain better performance while reducing costs. In the AWS case, AMD-powered instances cost 10% less than Intel-powered instances, and Graviton instances cost 20% less than Intel-powered instances. To run on Graviton instances, you should build multi-architecture containers that comply with running on Intel, AMD, and Graviton instance types. You will be able to take advantage of reduced instance prices while also empowering your application with better performance.  Purchasing Options   Take advantage of cloud provider purchasing options. All three leading cloud providers (AWS, GCP, Azure) offer multiple purchasing strategies, such as: On-Demand: Basic, list pricing Commitment-Based: Savings Plans (SPs), Reserved Instances (RIs), and Commitment Use Discounts (CUDs), which deliver discounts for pre-purchasing capacity Spot: Spare cloud service provider (CSP) capacity (when it is available) that offers up to a 90% discount over On-Demand pricing Define your purchasing strategy choice per node, and prioritize using Spot instances when possible to leverage the steep discount this purchasing option provides. If for any reason Spot isn't a fit for your workload — for example, in the case that your container runs a database — purchase the steady availability of a node that comes with commitment-based pricing. In any case, you should strive to minimize the use of On-Demand resources that aren't covered by commitments.  Autoscaling Rules   Set up scaling rules using a combination of horizontal pod autoscaling (HPA), vertical pod autoscaling (VPA), the cluster autoscaler (CA), and cloud provider tools such as the Cluster Autoscaler on AWS or Karpenter to meet changes in demand for applications. Scaling rules can be set per metric, and you should regularly fine-tune these rules to ensure they fit your application's real-life scaling needs and patterns. Kubernetes Scheduler (Kube-Scheduler) Configuration   Use scheduler rules wisely to achieve high utilization of node resources and avoid node overprovisioning. As described earlier, these rules impact how pods are deployed.  In cases such as where affinity rules are set, the number of nodes may scale up quickly (e.g., setting a rule for having one pod per node).  Overprovisioning can also occur when you forget to specify the requested resources (CPU or memory) and instead, only specify the limits. In such a case, the scheduler will seek nodes with resource availability to fit the pod’s limits. Once the pod is deployed, it will gain access to resources up to the limit, causing node resources to be fully-allocated quickly, and causing additional, unneeded nodes to be spun up.  Managing Unattached Persistent Storage   Persistent storage volumes have an independent lifecycle from your pods, and will remain running even if the pods and containers they are attached to cease to exist. Set a mechanism to identify unattached EBS volumes and delete them after a specific period has elapsed. Optimizing Network Usage to Minimize Data Transfer Charges   Consider designing your network topology so that it will account for the communication needs of pods across availability zones (AZs) and can avoid  added data transfer fees. Data transfer charges may also happen when pods communicate across AZs with each other, with the control plan, load balancers, and with other services.  Another approach for minimizing data transfer costs is to deploy namespaces per availability zone (one per AZ), to get a set of single AZ namespace deployments. With such an architecture, pod communication remains within each availability zone, preventing data transfer costs, while allowing you to maintain application resiliency with a cross-AZ, high-availability setup. Minimizing Cluster Counts   When running Kubernetes clusters on public cloud infrastructure such as AWS, Azure, or GCP, you should be aware that you are charged per cluster. In AWS, you are charged $73 per month per cluster you run with Amazon Elastic Kubernetes Service (EKS). Consider minimizing the number of discreet clusters in your deployment to eliminate this additional cost. Mastering Kubernetes Cost Optimization   Now that you have a better understanding of Kubernetes cost optimization strategies, it’s time to implement best practices for maximizing your Kubernetes ROI.  Optimize: Leverage intelligent recommendations to continuously optimize Kubernetes costs and usage After enabling appropriate visibility across all your stakeholders, you and your FinOps team can finally take on the task of optimizing and reducing Kubernetes spending. With comprehensive K8s visibility, you can fine-tune Kubernetes resource allocation — allocating the exact amount of resources required per cluster, namespace/label, node, pod, and container.  Operate: Formalize accountability and allocation for Kubernetes costs  As a FinOps strategy leader, you must gain consensus and instill proper financial control structures for Kubernetes within your organization. FinOps strategies without accountability and alignment are doomed to failure. Financial governance controls further reduce the risk of overspending and improve predictability. This operating phase is where the rubber meets the road as far as what results you will gain from your Kubernetes FinOps efforts. Learn details on these strategies to maximize K8s ROI here Anodot for Kubernetes Cost Optimization    Anodot provides granular insights about your Kubernetes deployment that no other cloud optimization platform offers. Easily track your spending and usage across your clusters with detailed reports and dashboards. Anodot’s powerful algorithms and multi-dimensional filters enable you to deep dive into your performance and identify under-utilization at the node level.  With Anodot’s continuous monitoring and deep visibility, engineers gain the power to eliminate unpredictable spending. Anodot automatically learns each service usage pattern and alerts relevant teams to irregular cloud spend and usage anomalies, providing the full context of what is happening for the fastest time to resolution. Anodot seamlessly combines all of your cloud spend into a single platform so you can optimize your cloud cost and resource utilization across AWS, GCP, and Azure. Transform your FinOps, take control of cloud spend and reduce waste with Anodot's cloud cost management solution. Getting started is easy! Book a demo to learn more.