Most customers running Kubernetes clusters Amazon EKS are regularly looking for ways to better understand and control their costs. While EKS simplifies Kubernetes operations tasks, customers also want to understand the cost drivers for containerized applications running on EKS and best practices for controlling costs. Anodot has collaborated with Amazon Web Services (AWS) to address these needs and share best practices on optimizing Amazon EKS costs. You can read the full post here on the AWS website. Amazon EKS pricing model The Amazon EKS pricing model contains two major components: customers pay $0.10 per hour for each configured cluster, and pay for the AWS resources (compute and storage) that are created within each cluster to run Kubernetes worker nodes. While this pricing model appears straightforward, the reality is more complex, as the number of worker nodes may change depending on how the workload is scaled. Understanding the cost impact of each Kubernetes component Kubernetes costs within Amazon EKS are driving by the following components: Clusters: When a customer deploys an AWS EKS cluster, AWS creates, manages, and scales the control plane nodes. Features like Managed Node Groups can be used to create worker nodes for the clusters. Nodes: Nodes are the actual Amazon EC2 instances that pods run on. Node resources are divided into resources needed to run the operating system; resources need to run the Kubernetes agents; resources reserved for the eviction threshold, and resources available for your pods to run containers. Pods: A pod is a group of one or ore containers and are the smallest deployable units you can create and manage in Kubernetes. How Resource Requests Impact Kubernetes Costs Pod resource requests are the primary driver of the number of EC2 instances needed to support clusters. Customers specify resource requests and limited for vCPU and memory when pods are configured. When a pod is deployed on a node, the requested resources are allocated and become unavailable to other pods deployed on the same node.  Once a node's resources are fully allocated, a cluster autoscaling tool will spin up a new node to host additional pods. Incompletely configuring resource specifications can impact the cost within your cluster. Tying Kubernetes Investment to Business Value with Anodot  Anodot's cloud cost management platform monitors cloud metrics together with revenue and business metrics, so users can understand the true unit economics of customers, applications, teams and more. With Anodot, FinOps stakeholder from finance and DevOps can optimize their cloud investments to drive strategic initiatives. Anodot correlates metrics collected with data from the AWS Cost and Usage Report, AWS pricing, and other sources. This correlation provides insight into pod resource utilization, nodes utilization and waste. It also provides visibility into the cost of each application that is run. Anodot's cost allocation feature enables users to produce rule-powered maps that associate costs with business cost centers. Simple or complex rules can be defined for tags, namespaces, deployments, labels and other identifiers. Users can visualize the maps and create dashboards to better understand cost per department, application, or unit metric.  

The total transaction value of digital payments is projected to exceed $1.7 billion by the end of 2022. Each one of these transactions generates masses of data that contains critical insights for merchants, payment service providers, acquirers, fintechs, and other stakeholders in the payments ecosystem. Having real-time access to these insights has the power to drive growth through customer and market understanding. It also has the power to protect against tremendous revenue loss by mitigating the risk of payment issues and fraud. The payments data mandate   Real-time payment monitoring and detection of transaction incidents is one of payment data’s most important mandates. Whether there’s an increase in payment failures, a drop in approval rates, or other issues — operations, payments, and risk managers must be able to see what went wrong, where, and why.  This is the only way they can accelerate root cause analysis and quickly triage and resolve payment incidents.  But gaining complete visibility into the payments ecosystem in real time in order to detect anomalies immediately is a great challenge, though one that no organization can afford to ignore.  Time could not be more of the essence. Consider what happens if there is a glitch in an API to a backend payment system that is crucial for approvals. If transactions can’t access the relevant API, the payment acceptance rate will plummet and revenue will be lost during the unexpected downtime.  So, while organizations are collecting large volumes of data every day, if the data can’t be used to protect the organization against payment incidents and potential loss, the value of the data will never be realized and losses will continue to impact business health.  Challenges of optimizing payments data   The bridge between collecting payment data and using it effectively is full of obstacles, which primarily fall into three categories – access, process and infrastructure, and complexity. Access to user data User onboarding: Aggregating user data is complicated by the fact that assuring a good user onboarding and registration experience typically requires asking as few questions as possible (to avoid abandonment due to complicated and time-consuming processes). Owning the relationship: Most payments stakeholders don't necessarily own the end user relationship. This means they don’t have access to the relevant user data, making it all the more difficult to detect which user activities are anomalous. Tokenization: Access to user data is also hindered when using external tokenization, which keeps most of the user and card information with the tokenizer rather than with the merchant or payments service provider. Data privacy: Detecting anomalous behaviors requires aggregating data about user behavior. However, data privacy regulations and regulators limit the usage of personal user information. Equal access: Even when the right user data is being collected by the organization, not all departments have equal access to it, nor is it shared sufficiently and frequently enough by those who do have access.    Process & infrastructure related Processes are manual resulting in monitoring and detection that are slow and error-prone with real-time outcomes being impossible to achieve. Real-time collection and analysis for timely decision making is impractical due to the complexity involved with the implementation and application of the numerous APIs required for collection. Intelligent insights provided in real time are typically out of reach since no one-size-fits-all solution can address the variety of incidents that occur during the specific recovery and handling processes of each organization.   Complexity The payment ecosystem is continually growing with more systems and data sources than ever, making it very difficult to collect and connect relevant payments data. Sources and data formats are fragmented, also making the task of aggregating data into one coherent source of truth a difficult task. Different payment methods and flows carry different data sets impacting the ability to unify operational data. Not all data is being collected via APIs leaving a lot of gaps since not all the data can be gathered.   Getting the most out of payments data   The goal of overcoming these challenges is to be able to get the most out of payments data. In order to optimize payment operations, teams should be able to:   Leverage data for actionable insights specifically into user activity in order to detect anomalous behaviors. Access all relevant user data, which is enabled by integrations that entail implementing every relevant API, not only those which are related to payments instructions. Gain a fuller picture of user behaviors for better understanding what is anomalous, which is enabled by embedding external data sources into the existing data management environment. Analyze data to build forecasts regarding activity, money flow, user behaviors, seasonality, and more, and not only for understanding what has happened, which drives a better understanding of potential risk. Make intelligence-driven decisions and remove the burden of manual work from payments personnel, which is enabled by AI and machine learning. Better understand the scope and patterns of user behaviors and payments trends, which is enabled by analyzing data across multiple time periods and granularities. Anodot for payment intelligence    Anodot for payment monitoring and real-time incident detection overcomes the challenges to payment operations, incident detection and remediation. Anodot’s AI-powered solution autonomously monitors the volume and value of payment data, including transaction counts, payment amounts, fees, and much more.  The solution delivers immediate alerts when there are payment approval failures, transaction incidents and merchant issues. Our patented correlation technology helps to identify the root cause of issues for accelerating time to remediation.  Anodot automates payment operations, seamlessly integrating notifications into your organization’s workflow. And by filtering through alert noise and false positives to surface the most important issues, it minimizes the impact on revenue and merchants. Turnkey integrations aggregate data sources into one centralized analytics platform. With impactful payment metrics and dimensions pre-configured into the solution, anyone in the organization can leverage data for insights and actions. 

Top 5 FinOps Tips The efficiency, flexibility and strategic value of cloud computing are driving organizations to deploy cloud-based solutions at rapid pace. Fortune Business Insights predicts the global cloud computing market will experience annual growth of nearly 18% through 2028. As the cloud becomes one of the most expensive resources for modern organizations, cloud financial management, or FinOps, has become a critical initiative. FinOps is a practice that combines data, organization and culture to help companies manage and optimize their cloud spend. There is no one-size-fits-all approach to FinOps and cloud costs management, but there are specific actions practitioners can take to make the most impact. Anodot's FinOps specialist, Melissa Abecasis, shares her top 5 tips for FinOps success in this video. They include: 1. Tag Resources A well-defined cloud tagging policy is the backbone of cloud governance setup and Melissa says it's never too early to start tagging your resources to enable accurate cost chargeback and showback. When deciding what tags to implement, it’s best to start with a simple list  to make it easier to  get into the habit of tagging resources. Tags by application, owner, business unit, environment and customer are all commonly tagged resources. 2. Savings Plan Commitments Melissa suggests that if organizations know they will be using AWS in the next year or so, there is no reason not to take advantage of AWS Compute Savings Plans. The plans allow subscribers to pay lower costs in exchange for committing to use particular AWS services for one to three years. Melissa says commitment savings can be as high as 50%, so even if you have 10% or 20% underutilization of a resource, you're still achieving significant savings. 3. Private Pricing AWS provides private pricing for a variety of services. The most common include Cloudfront, Data Transfer and S3. Melissa says you may be eligible if  have usage of: more than 10 terabytes for Cloudfront data transfer out, 500 terabytes of interagency data transfer, 500 terabytes of data transfer out or petabyte of S3 per month. If these apply to your organization, Melissa suggests speaking with your AWS account manager about the significant savings you can achieve through private pricing. 4. Don't Ignore Smaller Costs An organization's  top 10 cloud services typically account for 70% - 90% of total cloud costs. But Melissa urges FinOps practitioners not to ignore the smaller services. For services that cost $100 to $1,000 a month, it's still worth gaining visibility into each to determine if there are forgotten backups that are no longer needed or testing environments that are not being used. Those small wins can add up to thousands of dollars a day. 5. Create Company Awareness From day one, Melissa says it's important to create company awareness of cloud operations. FinOps teams should assign who is responsible for the cloud service, who is going to check the bill at the end of the month and who is going to learn and implement the strategies necessary to optimize costs and reduce cloud waste. [CTA id="6c56537c-2f3f-4ee7-bcc1-1b074802aa4c"][/CTA] Achieve FinOps Success with Anodot Anodot is the only FinOps platform built to measure and drive success in FinOps, giving you complete visibility into your KPIs and baselines, savings recommendations to help you control cloud waste and spend, and reporting to make sure you improve your cloud efficiency. 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.  Savings Recommendations  Continuously eliminate waste and optimize your infrastructure with personalized recommendations for unknown saving opportunities that can be implemented in a few steps. 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.   

AWS re:Invent, one of the biggest tech events in the world, is just weeks away. While there are thousands of sessions to choose from, there's bound to be high interest in sessions focused on cloud cost optimization and management. That's because maximizing cloud efficiency and reducing waste is ranking as a top priority, and a challenge for organizations of all sizes. If cloud cost savings is important for your business, we've compiled a list of the best sessions to attend. You can find the sessions listed below by visiting the AWS re:Invent Session Catalog where there is an option to search by keyword and register. Anodot is leading one of the sessions — focusing on the power of insight to accelerate AWS — and will be exhibiting at booth #2540 where you can get one-on-one time with one of our cloud experts to learn how to optimize your AWS cloud spend. Book a meeting here to secure your spot! Monday, November 28 Spot the savings: Use Amazon EC2 Spot to optimize cloud deployments   CMP324-R | Time: 1:00 - 3:00 PM | Session Type: Workshop  Amazon EC2 Spot Instances are spare compute capacity available to you for less than On-Demand prices. EC2 Spot enables you to optimize your costs and scale your application’s throughput. This workshop walks you through the APIs and commands used to create Spot Instances: You create an EC2 launch template and then use the launch template to launch Spot Instances using EC2 Auto Scaling groups, EC2 Spot Fleet, EC2 Fleet, and EC2 RunInstances API. Also learn how to implement Spot functions such as Spot placement score, attribute-based instance selection, and AWS Fault Injection Simulator. Cloud metrics strategy and customizable billing   COP202-R | Time: 2:30 PM - 3:30 PM |  Session Type: Chalk Talk A well-defined cloud metrics strategy helps organizations evaluate the efficiency of cloud resource utilization and tell a cloud value story that is aligned with business outcomes. The ability to customize pricing and billing views allows you to charge back to your end users in a streamlined process. Join this session to learn how you can construct KPI strategies and accountability with services such as AWS Billing Conductor and start running your IT department like a business. Visualizing AWS Config and Amazon CloudWatch usage and costs   COP215-R | Time: 2:30 PM - 3:30 PM | Session Type: Chalk Talk  In this session, explore dashboards that you can deploy into your own account to get a real-time view of some of the typical main contributors to AWS Config and Amazon CloudWatch costs. The dashboards are designed to help you identify high-cost areas and see the impact of any changes made over time. You can deploy the dashboards into your own account and explore how to create and modify them for your own needs. How to save costs and optimize Microsoft workloads on AWS   ENT205 | Time: 4:00 - 5:00 PM | Session Type: Breakout Session Customers have been running Microsoft workloads on AWS for 14 years—longer than any other cloud provider—giving AWS unmatched experience to help you migrate, optimize, and modernize your Windows Server and SQL Server workloads. In this session, learn best practices and see demos on how to right-size your infrastructure and save on Microsoft licensing costs; how to configure your workloads to run more efficiently; how to avoid expensive and punitive licensing restrictions; and how AWS offers you the most and highest performing options for your Microsoft workloads. How SingleStore saves 56 percent on Amazon EC2 with no DevOps hours invested    PRT095 | Time: 5:10 - 5:25 | Session Type: Lightning Talk  For SingleStore, the cost of data is high. To manage the cost of running their SQL distributed database, SingleStore aimed to drive cost efficiency as far as they could. SLA requirements for data continuity prevented them from utilizing highly discounted Amazon EC2 Spot Instances. But the long-term commitments associated with other discount programs made them too risky to cover fluctuating workloads. In this lightning talk Ken Dickinson, VP of Cloud Infrastructure at SingleStore, explains how SingleStore was able to ramp up their Amazon EC2 savings even further. Learn how they covered 99 percent of their workloads to help them break through their previous savings ceiling. This presentation is brought to you by Zesty, an AWS Partner. Tuesday, November 29 Continuous cost and sustainability optimization   SUP304 | Time: 11:45 - 1:45 | Session Type: Workshop In this workshop, learn best practices for cost and sustainability optimization. Shift costs and sustainability responsibilities from the Cloud Center of Excellence (CCoE) to end users and application owners aided by automation at scale. Learn about cost efficiency and implementing mechanisms that empower application owners to have clear, actionable tasks for cost and sustainability optimization building upon real-world use cases. You must bring your laptop to participate. How to use Amazon S3 Storage Lens to gain insights and optimize costs   STG335 | Time: 2:00 PM - 3:00 PM | Session Type: Builder's Session  As your dataset grows on Amazon S3, it becomes increasingly valuable to use tools and automation to manage and analyze your data and optimize storage costs. In this builders’ session, learn about Amazon S3 Storage Lens which delivers a single view of object storage usage and activity across your entire Amazon S3 storage. It includes drill-down options to generate insights at the organization, account, Region, bucket, or even prefix level. Walk through S3 Storage Lens, and learn how to get started with this feature with your own storage. You must bring your laptop to participate. Multi- and hybrid-cloud cost optimization with Flexera One    Time: 2:40 PM - 2:55 PM | Session Type: Lightning Talk In this talk, Flexera discusses and demonstrates the Cloud Cost Optimization (CCO) functionality of the Flexera One platform. See how CCO allows you to achieve a true single-pane-of-glass view for all multi-cloud resources, including global regions of major cloud providers and emerging and niche cloud offerings. Using CCO’s Common Bill Ingestion functionality, any additional cloud resource costs (support costs, labor costs, VAT, etc.) can be ingested into the platform and viewed and analyzed alongside existing cloud resources. All phases of the FinOps framework are activated within CCO and will be included in this demonstration. This presentation is brought to you by Flexera, an AWS Partner. AWS optimization: Actionable steps for immediate results   STP210-R1 | Time: 3:30 PM - 4:30 PM | Session Type: Theatre Session Cash burn is a hot topic for startups, and late-stage funded ventures especially need to keep tabs on budget as they ramp up. AWS offers resources to make cost management, budget tracking, and optimization simple and attainable for startups of any size. In this session, get familiar with the different technical strategies, levers to pull, and commitment-based savings plans AWS offers. After this session, you will have an actionable plan with a combination of tactical and strategic initiatives that can help you reduce overall spend and increase your runway.   Scaling performance and lowering cost with the right choice of compute   CMP318-R | Time: 3:30 PM - 4:30 PM | Session Type: Chalk Talk  This chalk talk covers the latest innovations across Intel, AMD, and AWS Graviton compute options (i.e., Intel Ice Lake, AMD Milan, and AWS Graviton3) to help companies choose the optimal instance for their workloads. Learn about the price performance benefits enabled by AWS compute options and the AWS Nitro System across a broad spectrum of workloads. Simplify your AWS cost estimation   Time: 3:30 PM - 4:30 PM | Session Type: Breakout Session  Take the guesswork out of planning with AWS: accurately evaluate the cost impact of your AWS workloads as you grow and save on AWS. Join this chalk talk to learn how you can plan for changes to your workload and simplify your cost estimate. Understand how modifications of your purchase commitments, resource usage, and commercial terms affect your future AWS spend.   Optimize for cost and availability with capacity management   CMP319 | Time: 5:00 PM - 6:00 PM | Session Type: Chalk Talk  Managing your capacity footprint at the enterprise level can be complex. This chalk talk covers how to plan for, acquire, monitor, and optimize your capacity footprint to achieve your goals of maximizing for capacity availability while minimizing costs. Leave this talk with an understanding of how to use Amazon EC2 Capacity Reservations, On-Demand Capacity Reservations, and Savings Plans to lower costs so that you can focus on innovating. Visualize, understand, and manage your AWS costs   COP336-R1 | Time: 5:00 - 6:00 | Session Type: Builder's Session  Having actionable cost insights with the right level of cost reporting allows you to scale on AWS with confidence. Join this hands-on builders session to learn which resources are available for you to achieve cost transparency, dive deep into cost and usage data, and uncover best practices and dashboards to simplify your cost reporting. Explore AWS Cost Explorer and AWS Cost and Usage Reports (CUR) and then learn how to export and query CUR and visualize resource-level data such as AWS Lambda functions and Amazon S3 bucket costs using the CUDOS dashboard. Wednesday, November 30 Cloud FinOps: Empower real-time decision making   PRT322 | Time: 10:00 AM - 11:00 AM | Session Type: Breakout Session  As organizations align their processes to the realities of operating in the cloud, they seek to understand what they are spending and, more specifically, how they can analyze their infrastructure in their business context. FinOps practitioners can implement a dedicated solution to analyze data, manage anomalies, and measure unit costs. Join this session to learn how CloudHealth, a recognized leader in FinOps and cloud cost management, gives users the information they need to meet their organizational goals and objectives. This presentation is brought to you by VMware, an AWS Partner.  FinOps: The powerful ability of insight to accelerate AWS (sponsored by Anodot)   PRT035 | Time: 10:55 - 11:10 AM | Session Type: Lightning Talk  Attend this talk to learn how you can empower your business stakeholders with clarity and highly personalized insights to unlock all the cloud has to offer. Learn tactics and best practices for developing your AWS cost management strategy by minimizing noise and maximizing the relevance between your FinOps practice and your unique business objectives. This presentation is brought to you by Anodot, an AWS Partner.   Thursday, December 1 FinOps: Intersecting cost, performance, and software license optimization (sponsored by Flexera)   PRT306 |Time: 11:00 AM - 1:00 PM | Session Type: Workshop  Within the rapidly maturing FinOps discipline, cost is the driving force behind the optimization of cloud resources. Actions taken to optimize costs may be detrimental to application performance or be at odds with licensing restrictions for the software running on those resources. In this workshop, experts from Flexera and IBM Turbonomic identify overlooked aspects of cloud cost optimization and demonstrate how successful FinOps practices require visibility and continuous analysis of performance metrics and licensing constraints when optimizing cloud resources. You must bring your laptop to participate. This presentation is brought to you by Flexera, an AWS Partner. Building a budget-conscious culture at Standard Chartered Bank   CMP213 | Time: 2:00 PM - 3:00 PM | Session Type: Breakout Session  In this session, Standard Chartered Bank shares how FinOps has been embedded in the way they build systems. Critical large systems at Standard Chartered Bank—such as scaling applications, container platforms, and their grid for calculating risk and analytics—use techniques to reduce waste and optimize cost and performance at scale. These techniques include using an optimal combination of AWS Savings Plans and Amazon EC2 Spot Instances, building for elasticity, and applying automation to switch down systems not in use.

We are proud to announce that Anodot has been named Momentum Leader on G2's fall grid for Cloud Cost Management Software. G2 is the largest and most trusted software marketplace. More than 60 million people annually use G2 to make smarter software decisions based on authentic peer reviews. G2 is disrupting the traditional analyst model and building trust by showcasing the authentic voice of millions of software buyers. Global customers use Anodot's cloud cost management solution to monitor and manage their multi-cloud and Kubernetes spend in real-time. G2's grid report is based on user ratings and not information self-reported by vendors. G2 scores products and vendors based on reviews from verified users as well as data aggregated from online source. Here are some of Anodot's most recent reviews on G2: "The best FinOps application" "Simply the best cost management tool on the market"  "Great tool to save time and money"  Cloud visibility and cost control Keeping cloud costs under control is notoriously difficult. Cloud assets are fragmented across multiple teams, cloud vendors and containerized environments. Anodot provides granular visibility into cloud costs and seamlessly combines all of your cloud spend into a single platform. Users can monitor and optimize cost and resource utilization across AWS, Azure and GCP. Anodot's AI/ML powered solution automatically learns each service usage pattern and alerts relevant teams to irregular cloud spend and usage, providing the full context of what is happening so engineers can take action. By monitoring cloud metrics together with revenue and business metrics, Anodot enables cloud teams to understand the true cost of their SaaS customers and features. [CTA id="6c56537c-2f3f-4ee7-bcc1-1b074802aa4c"][/CTA] Cloud cost savings recommendations As companies progress along their FinOps journey, many will face competing initiatives. It can be challenging to prioritize cost optimization recommendations and make sure the right decisions are being made. Identifying the engineering efforts and potential savings can help your team determine priorities. Anodot offers 60+ best-in-class savings recommendations that are highly personalized to your business and infrastructure. CLI and console instructions are provided alongside each savings insight to enable engineers to take action in the way they find most comfortable. Kubernetes visibility for FinOps Kubernetes drives service agility and portability, but it can also be far more difficult to understand just how much each K8s-based application costs. Anodot provides granular visibility into your Kubernetes costs and combines it with your non-containerized costs and business metrics so you can get an accurate view of how much it costs to run a microservice, feature, etc. With Anodot's powerful algorithms and multidimensional filters, you can analyze your performance in depth and identify underutilization at the node and pod level. Cloud cost management with Anodot Bring finance, DevOps and business stakeholders together to collaboratively control and reduce spend across your cloud infrastructures with Anodot. See cost causation and allocate spend by service, business unit, team and app with deep visibility and granular detail. Continuously eliminate waste with easy-to-action savings recommendations Avoid bill shock and enable FinOps with near real-time anomaly detection and alerts Try Anodot's cloud cost management solution with a 30-day free trial. Instantly get an overview of your cloud usage, costs, and expected annual savings.

Smooth payment operations are critical for every merchant’s success. At its most basic level, a seamless and reliable payment process is the key to assuring transaction completion, which is at the very core of a merchant's financial strength.  However, when payment data systems fail to deliver insights about issues regarding approvals, checkouts, fees or fraud, the result is revenue loss and sometimes customer churn. While there are technology solutions that can process millions of transactions daily, there are many challenges to effective payment monitoring, leaving timely identification and speedy resolutions too often out of reach. Payment monitoring challenges There are many challenges to accurate and timely payment monitoring.  Among the most formidable are the increasing complexity of the payment ecosystem, the unreliability of static thresholds, the growing success rates of fraud attempts, and manual analysis processes that are too slow for assuring timely resolutions. Let’s take a closer look. The increasingly complex payments ecosystem  Today’s payments landscape is comprised of many different systems, technologies, methods, and players. There are credit and debit cards, prepaid cards, digital wallets, virtual accounts, mobile wallets and mobile banking, and more. To complicate matters, many organizations that process payments rely on multiple third-party payment providers, who are sometimes their direct competitors. There is an additional challenge for companies offering a localized experience to customers. Using local payment systems sometimes means relying on unstable payment networks and represents a measurable risk to the integrity of payment processes. This broad and ever-growing ecosystem can be confusing and difficult for merchants and payment services providers to orchestrate, especially when it comes to determining the optimal path for monitoring, detecting, and remediating issues with the payment process. Static thresholds  Merchants today typically either monitor transactions manually or receive alerts on payment issues based on static thresholds whose definitions are driven by historical data. But user behavior patterns are dynamic, which means that static, historically driven settings and definitions are not reliable for detecting (and handling) issues in real time. This frequently results in missing incidents or discovering incidents too late after the damage has been done.  The increasing success rates of payments fraud attempts The global ecommerce industry is poised to grow into a $5.4 trillion market by 2026 and with it – online and digital payment fraud is also growing exponentially.  Fraudster techniques have become increasingly more sophisticated and their success rates are higher than ever. Last year, $155 billion in online sales were lost to fraud, and this number is expected to continue to grow.  And according to the recent AFP Payments Fraud and Control Report, 75% of large companies with annual revenue at over $1 billion were hit by payment fraud in the past year, and 66% of mid-size companies with annual revenues at under $1 billion. Manual analysis Even when a payment incident is detected, understanding the root cause for accelerating remediation can still be very challenging. Whether at merchants or financial services organizations, those who are charged with understanding the root cause of payment issues and remediating them are typically faced with having to manually scour through multiple dashboards. This approach which is very time intensive is no longer viable. Decisions need to be made in real time and actions must be taken immediately. A delay in mitigation is not something any organization can afford, as it drives revenue loss. Rules based routing Many merchants and payment services companies route payments as driven by simple rules engines. However, this approach  is not designed to address today’s needs for fast, efficient, and smart routing. To overcome all of these challenges, what these organizations and every merchant needs is a way to detect payment issues faster and get alerts in real time when there are revenue-critical incidents in their payment operations.  This is where AI-powered payment monitoring comes in. [CTA id="3509d260-9c27-437a-a130-ca1595e7941f"][/CTA] AI takes payment monitoring to a whole new level When we introduce AI-powered analytics and real-time monitoring to the task, merchants are finally empowered to overcome the above-mentioned challenges and prevent revenue loss. They can monitor all of their payment data and capture continuous insights into their payment lifecycles. They can know instantly upon the appearance of a suspicious trend or when payments fail and receive real-time alerts that provide the full context of what is happening, including incident impact, timeline, and correlations. Additional benefits include: Faster root cause detection AI-driven payment monitoring learns the normal behavior of all business metrics, constantly monitoring every step in the payment lifecycle, and providing crucial workflow insights.  This happens automatically, where merchants and payment service providers get much-needed visibility into what happened, where it happened, why it happened, and what they should do next, for faster than ever time root cause detection. Real-time actions  When payment monitoring is driven by AI, monitoring and alerting is real-time, empowering organizations to detect and act upon any deviation from normal transaction behavior.  This way they can capture incidents before they impact the customer experience.  Noise alert reduction By learning what impacts customers and the business and what doesn’t, billions of data events can be distilled into a single, scored, highly accurate alert. This makes storms, false positives, and false negatives a thing of the past, and enables teams to focus on the incidents that bring a measurable impact to revenues and the customer experience. Moreover, users will no longer need to subscribe to alerts, which often wind up in the ‘graveyard of alerts’ folder, with no measurable value for the payments operation. Accelerated time to remediation When needed insights can be gathered automatically at the right time, there is no need to sift through endless graphs on multiple dashboards.  AI enables the correlation of anomalies for immediately identifying the contributing factors to incidents, and receiving the full context required for expediting the right remediation actions. How Anodot can help Anodot brings an AI-powered solution for autonomously monitoring the volume and value of a merchant’s payment data, including transaction counts, payment amounts, fees, and more. The solution detects payment incidents 80% faster and profoundly accelerates resolutions, sending immediate alerts when there are transaction or merchant issues, and payment or approval failures. Notifications are seamlessly integrated into existing workflows, with only the most important issues being surfaced to prevent time being wasted on false positives, and profoundly reducing alert noise by 90%. Anodot's patented correlation technology helps to identify the root cause of issues with 50% faster root cause analysis. The out-of-the-box solution comes with turnkey integrations, and is pre-configured with impactful payment metrics and dimensions. This way, organizations can accelerate ROI and time to value.  

7 KPIs for Measuring FinOps Success Public cloud can deliver significant business value across infrastructure cost savings, team productivity, service elasticity, and DevOps agility. Yet, up to 70% of organizations are regularly overshooting their cloud budgets, minimizing the gap between cloud costs and the revenue cloud investments can drive. Cloud cost management (the practice of FinOps — often assigned to a multidisciplinary, cross-functional group, also called “FinOps,” or a “Cloud Center of Excellence”) is targeted at helping businesses maximize the return on their investments in cloud technologies and services. Because managing cloud costs is such a relevant challenge, and is such an area of focus, it has been ascribed many names, including, “Cloud Financial Management,” “Cloud Financial Engineering,” “Cloud Cost Management,” “Cloud Cost Optimization,” and “Cloud Financial Optimization.” Every business with cloud infrastructure will have a cloud cost management strategy, and every successful strategy will include a practice of benchmarking and measurement to ensure progress and improvement towards increasing return on cloud investments. Cloud cost efficiency measurement Amazon Web Services, the largest public cloud service provider, devotes one-sixth of their Well-Architected Framework to avoiding unnecessary costs. While the Cost Optimization Pillar is comprehensive, it is largely written in broad strokes and generalizations, rather than identifying specific tactics and KPIs that can deliver FinOps success. Although valuable, the Well-Architected cost pillar focuses on operationalizing using a plethora of discreet AWS-native tools and offers little insight for businesses with modern multicloud strategies (even ignoring the existence of other public clouds). The FinOps Foundation, a program of The Linux Foundation, segments cloud financial management into FinOps Capabilities (grouped into overarching FinOps Domains) that each consist of “Crawl,” “Walk,” and “Run” operational maturity levels. The maturity level of each capability within a business is assessed according to goals and key performance indicators (KPIs). [CTA id="6c56537c-2f3f-4ee7-bcc1-1b074802aa4c"][/CTA] Simplified FinOps measurement strategies While some FinOps models cover a tremendous amount of ground, and often, even deliver specific KPI targets, they can require months of implementation and corporate cultural change efforts before returning value and meaningful data. This guide endeavors to simplify the measurement of FinOps efficiency into its most important metrics. This approach enables your business to assess the current impact of cloud cost management efforts at the macro level to deliver immediate insights, and can serve as a precursor to significantly more sophisticated and time-consuming FinOps strategies and measurement efforts. As your cloud consumption increases, measuring and tracking cloud efficiency will become a critical task. The following KPIs are critical to understanding the effectiveness of your FinOps efforts and driving incremental success: Percentage of Allocatable Cloud Spend Average Hourly Cost Cloud Unit Costs Percentage of Waste Blend of Purchasing Strategies Time to Address Cost Anomalies Forecasting accuracy Using FinOps to drive cloud efficiency The 7 KPIs above are probably the most important indicators of your cloud account's efficiency, but there are plenty more. Think about the KPIs you currently track in your organization as you review this list. Do you need additional tools or resources to track these KPIs? Defining the KPIs that can measure cloud efficiency is crucial for many organizations. Continuous cost monitoring allows for assessing what percentage of the costs are justified, and where improvements can be made. Cost efficiency is a shared responsibility across multiple levels of an organization. As the FinOps team or expert, it’s our responsibility to make sure we have the proper guardrails, cost monitoring, process optimization, and rate optimization in place. It is then up to the engineering teams using cloud services to make sure the solutions they architect and engineer are as cost-effective as possible. The road to cloud efficiency has many challenges, including: Visibility and cost allocation for multi-cloud and Kubernetes An increase in complexity due to using container-based applications and serverless technologies Identifying and avoiding pitfalls in FinOps adoption It is possible to overcome technology, visibility, and cost allocation challenges by using native Cloud Service Provider tools, building inhouse solutions, or purchasing FinOps tools — such as Anodot cloud cost management Anodot is the only FinOps platform built to measure and drive success in FinOps, giving you complete visibility into your KPIs and baselines, recommendations to help you control cloud waste and spend, and reporting to make sure you improve your cloud efficiency. Identifying and solving organizational challenges is not always easy. Here are a few things you can do from an operational perspective to take action today: You can make different stakeholders aware of their responsibilities by implementing a solid showback model. Cloud cost reporting with real-time data is crucial for the teams to understand how they are doing - make the information available directly to them. Communicate what efforts are being made and what savings can be expected. Mentor and support teams that are facing challenges in their cost efficiency instead of shaming and punishing them. Check out FinOps Foundation for great resources around training and buy-in. Book a demo with an Anodot cloud cost optimization expert to show your team what is achievable with a purpose-built FinOps solution

Cloud cost anomalies are unpredicted variations (typically increases) in cloud spending that are larger than expected based on historical patterns.  Misconfiguration, unused resources, malicious activity or overambitious projects are some of the reasons for unexpected anomalies in cloud costs. Even the smallest of incidents can add up over time leading to cost overruns and bill shock. As cloud billing data is collected and reviewed periodically, it is often difficult for FinOps teams to detect anomalies related to cloud costs in real or near time.  According to the State of FinOps 2022 report, 53% of organizations indicated that it takes days for their FinOps teams to respond to cost anomalies. It is probably because only 25% of companies have implemented automated workflows to manage anomalies.  Measuring cloud cost anomalies Anomaly management is composed of three distinct phases that should be measured separately: Time to detection (occurrence to discovery/acknowledgement)  Time to root cause (time of investigation) Time to resolution (total duration of the anomaly) Additionally, you should also measure the count of anomalies within a given period (e.g., day, week or month). Automated, machine learning–based anomaly detection systems, such as Anodot, allow the FinOps team to react quickly to avoid unexpected costs.  [CTA id="6c56537c-2f3f-4ee7-bcc1-1b074802aa4c"][/CTA] Managing cloud anomalies with Anodot Anodot's fully automated AI detects anomalies in near real time and alerts the appropriate teams only when risk is meaningful, enabling quick response and resolution. 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. Monitor and optimize your cloud costs and resource utilization across AWS, GCP, and Azure. Deep dive into your data details and get a clear picture of how your infrastructure and economies are changing. With Anodot, you can: Get complete visibility into AWS, Azure, GCP and Kubernetes costs: Understand, divide, track and attribute every dollar spent in context Easily customize reports and dashboards for FinOps stakeholders Manage Kubernetes spending and usage from the same view as your multicloud services Take action and continuously reduce your cloud costs: Pursue the most pertinent cost reduction opportunities with 40+ types of cost savings recommendations CLI and console instructions provided alongside each insight to enable engineers to take action Purchase services efficiently with analysis and customized recommendations Plan your usage effectively and prevent bill shock: Machine-learning based forecasting accurately predicts spend and usage, empowering you to anticipate changing conditions and deliver Detect, alert on, and resolve irregular spending and usage anomalies in near real time Assess enriched Anodot data via our powerful API and leverage within your other tools With Anodot, FinOps practitioners can continuously optimize their cloud investments to drive strategic business initiatives.

Companies are investing heavily in the cloud for the operational and financial benefits. But without a robust cloud cost management strategy in place, the complexity of cloud services and billing can to overspending and unnecessary cloud waste. Being able to accurately predict future cloud spend is one way to more optimize cloud spend and inform budgets. Ideally, finance, engineering and executive leadership agree upon and build allocation and forecast models from which to establish budgets that align with business goals. Once a strategy is in place, cloud cost forecasting accuracy is an important KPI to measure in order to understand cloud efficiency and FinOps success. Measuring forecast accuracy  To measure your forecasting accuracy, you’ll need to calculate the variance between your forecast and actual costs. Once the forecasted spend variance (%) is calculated, it can be compared against the FinOps Community of Practitioners' recommended thresholds: For FinOps practices operating at Crawl maturity, variance from actual spend cannot exceed 20% Variance of 15% for a FinOps practice operating at Walk maturity Variations of 12% for FinOps practices operating at Run maturity [CTA id="6c56537c-2f3f-4ee7-bcc1-1b074802aa4c"][/CTA] In the State of FinOps 2022 report, Run organizations reported 5% variance; Walk organizations reported 10% variance; and Crawl organizations reported 20% variance — a testament to the value of a growing FinOps practice. Accurate cloud spend forecasts require robust FinOps capabilities across the board, including complete multi cloud visibility and the ability to fully categorize and allocate cloud costs. Forecasting cloud costs with Anodot  Anodot’s AI-powered 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. 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: Deep visibility and insights - Report on and allocate 100% of your multicloud costs (with K8 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 40+ savings recommendations highly personalized to your business and infrastructure Continuous cost monitoring and control - Adaptive, AI-powered forecasting, budgeting and anomaly detection empower you to manage cloud spend with a high degree of accuracy and relevance Immediate value - You'll know how much you can immediately save from day one and rely on pre-configured, customized reports and forecasts to begin eliminating waste