The ROI of moving to the cloud As businesses shift to a digital-first environment, cloud computing will play a dominant role in delivering greater flexibility and faster innovation. In a recent report by Deloitte, nearly 90% of US-based senior decision makers proclaim cloud to be the cornerstone of their digital strategy. Covid accelerated cloud migration initiatives, with no signs of slowing down. Gartner forecasts worldwide end-user spend on public cloud services will grow by 20.4% in 2022 to a total of $494.7 billion.  The race to cloud is on at full speed ahead for a reason. The benefits are many and strategic, including: Business agility Rapid innovation Accelerated time-to-delivery Fast time-to-market Scale and elasticity Cost efficiency Reduced TCO Yet, with all the benefits, at least half of all organizations say they have yet to realize value from their cloud investments. This is because there are still many hurdles to cloud optimization, which makes effective cloud cost management and maximizing cloud ROI very challenging.  Challenges to achieving ROI Return on investment (ROI) is an indicator that signifies whether a business decision or investment led to a positive impact on the company. The measure of ROI is the increase in the value of an investment over time. At the heart of the challenge in achieving cloud ROI is cloud waste. The most common sources include: Scalability and the ability to provision limitless capacity: where the organization grants access to resources whenever they’re needed, but the corresponding cost allocation is neither apparent nor managed. Idle resources: when non-production resources that are being used for development, staging, testing, or QA, are being paid for as though they are being utilized 24/7, but they’re not. Unattached volumes: where volumes remain attached to terminated services and are not deleted even if they’re not in use. Unused virtual machines: that are not turned off once their work is complete. Oversized infrastructure: intended for planned capacity but is not being used.  Best practices for maximizing cloud ROI The only way for organizations to minimize waste and control spend is if they are able to detect when there is cloud waste, where it is, and how to best eliminate it , so they can better manage costs and ultimately maximize cloud ROI. As such, what organizations need first and foremost is visibility into cloud utilization and the ability to control waste whenever it’s detected.  Let’s take a look at three key best practices organizations can implement to gain such visibility and to finally get the control they need over their cloud costs. Gaining visibility with a Cloud Center of Excellence  An important driver of visibility is the Cloud Center of Excellence (CcoE), whose mandate it is to facilitate and nurture cooperation among business units across the entire organization, so they can: Share best practices Collaborate on optimizing the value of cloud computing Make data-driven decisions  Establish an organization-wide framework for cloud operations By facilitating cross-unit communications for sharing and collaboration, the CCoE provides more stakeholders visibility into cloud utilization. For example, if developers are putting workloads in the cloud, with a CcoE in place, they will have the framework (and motivation) to communicate this to other teams, who will now be aware, which is what visibility is all about. Driving visibility with FinOps The goal of FinOps, also known as cloud financial management, is to drive financial accountability while maximizing the business value of cloud. According to the FinOps Foundation, FinOps is also a cultural practice that enables businesses to get maximum business value by helping engineering, finance, technology and business teams to collaborate on data-driven spending decisions.  With a view to attaining visibility, one of the key tasks of FinOps is monitoring. So, if there is a sudden spike in the average hourly bucket size of a specific service over the course of a week, for example, with the right tools and processes in place FinOps will be able to detect the cost anomaly and relay this vital insight to the individuals charged with correction, thus granting them visibility.   From visibility to control  Now that we know how we can achieve greater visibility, the next step towards maximizing cloud ROI is to control costs.  Controlling costs is driven by reducing waste through corrective actions. For example, if you have detected waste from specific service types then you can visualize the business mappings between who and what are using which services. You can also monitor for better workload-to-instance options to better align needs with the services being consumed.  If you find that costs are outpacing utilization for certain services, you can size down overprovisioned infrastructure or terminate unused zombie infrastructure. And, if  the unit price is too high from your on-demand pricing plan, you can negotiate a discount with your provider for reserving capacity through savings plans, reserved instances, and committed use discounts. Forecasting Cloud Costs to Inform Budgets Accurately and continuously forecasting future cloud expenditures is the best way to anticipate results, create budgets and respond quickly enough to keep cloud costs from spiraling out of control. Accurate forecasting ensures lines of business are adhering to their budgets by alerting stakeholders when cost centers are exceeding predefined limits. Organizations with mature FinOps—a cloud financial management discipline and cultural practice—have teams that are collaborating to build forecast models from which to establish budgets that align with the goals of the business. Measuring cloud ROI The traditional ROI formula of return over costs is a difficult equation to apply to cloud computing. To calculate the true value of cloud adoption, organizations need to factor in benefits that are not always associated in dollar terms, such as increased business agility and elasticity. The best measure of cloud ROI is associated with factors that drive an organization to move to the cloud in the first place. The most common reasons associated with migration include: Moving workloads from data centers to cloud environments - Compare the costs of the equipment and maintenance of an on premise environment to the OpEx costs of cloud computing. Scalability - Quantify the cost savings associated with being able to quickly accommodate sudden spikes or short term surges in resource needs. Business agility - Faster responses to business requests and quicker development times result in greater cost and performance efficiencies. Efficiency - On premise hosting requires substation costs for resources that some organizations may not fully use while cloud computing is most commonly a pay for what you use model. Maximizing cloud ROI with Anodot Anodot is helping organizations around the world maximize cloud ROI by delivering visibility into cloud utilization and enabling them to control waste and spend. From a single platform, Anodot provides complete, end-to-end visibility into your entire cloud infrastructure and related billing costs. The AI-powered platform constitutes a complete cloud FinOps solution that correlates spend with business KPIs. It easily connects to any cloud provider and delivers complete visibility into AWS, Azure, GCP, and Kubernetes costs.  Reducing cloud waste with Anodot’s easy-to-action savings recommendations and avoid bill shock with anomaly detection and alerts.    From a single platform, Anodot provides complete, end-to-end visibility into your entire cloud infrastructure and related billing costs. By monitoring your cloud metrics together with your revenue and business metrics, Anodot enables cloud teams to understand the true cost of their SaaS customers and features, enabling ROI and the promised value of the cloud. 

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.  

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.