The public cloud market is expected to grow significantly in 2023, and it's no surprise. Gartner forecasts that end-user spending on public cloud services will rise by 21.7% to a total of $597.3 billion in 2023, up from $491 billion in 2022!  That's why in June 2023, we launched our Anodot 2023 State of Cloud Cost survey to explore the impact of mature FinOps platforms on cloud spend control, time to detect cost anomalies, realized cost savings, easiest-to-use optimizations, and their influence on overall cost savings. In this recap, we'll give you a quick snapshot of what to expect in our report. But we really encourage you to check out our in-depth report for a deeper dive. Trust me, you'll get loads more insights on cloud costs than this blog could ever give you! Top challenges in cloud Making smart decisions on cloud usage and costs relies solely on the ability to extract detailed data. So what are the biggest obstacles the market is currently facing when it comes to getting this crucial information? Let's take a look at the top three! [CTA id="84cdfc87-6078-4012-8859-b72cb2586405"][/CTA]   True Visibility: Our reporting found that having clear visibility into cloud usage becomes a leading issue for our customers. This includes tracking resource utilization, monitoring costs, and optimizing cloud services.  Complex cloud pricing: Dealing with complex, proprietary billing data and different pricing models from providers can make it even trickier to normalize data and reconcile costs.  Complex multi-cloud environments: Take the two big challenges of true visibility and complex cloud pricing, mash them up, and what do you get? Complex multi-cloud environments. Basically, the word "complexity" shows up way too often when we're talking about cloud cost! Cloud waste stats: In our survey, 67% said less than a third of their cloud spending is wasted, up from 56% last year, showing improved FinOps adoption and growing awareness of cloud waste which is good news! The bad news? 20% of respondents remain unfamiliar with the cloud waste they possess. This highlights the need for efforts to address this issue! Learn more on cloud waste costs. Most organizations want to measure unit costs Unit economics metrics are a MUST for engineering teams. Why? It allows them to gauge the business value associated with cloud expenditures quantitatively. With these metrics, teams can make informed, data-driven decisions in the realm of Financial Operations (FinOps), ensuring optimal allocation of resources and maximizing returns. The market definitely expressed interest in measuring unit costs; about 70% of respondents said they wanted to measure unit economics metrics but were not there yet. What about the other 30%? For those who do measure unit costs, 65% do so automatically with a tool they built in-house (45%) or a 3rd party solution (25%) like us! The remaining 35% rely on manual processes that use a combination of tools or spreadsheets to calculate their unit economics metrics, which blows our minds that some operations are still being done manually! Three is not a crowd: Over half of the respondents reported using third-party solutions to allocate direct and shared costs to business units, up from 38% last year, indicating that FinOps platforms are becoming increasingly popular with organizations. Find out more on how third-party tools are transforming cloud costs.💡 Cloud costs are on the rise, but less so for Anodot customers Organizations aspire to effectively manage cloud expenditure, yet struggle to achieve this goal. According to Flexera's 2023 State of the Cloud Report, a whopping 82% of companies say controlling cloud spend is their biggest challenge, surpassing security for the first time.  That's why FinOps is such a life-saver when it comes to cloud costs, companies maximize cloud investments, achieving more with fewer resources. When comparing our customer data to Flexera's, most half of Anodot's customers increased cloud spending by over 10% in the past year. But the best part? Over 45% reduced cloud spending through cost optimization, scaling adoption at the same or lower cost with Anodot! And the savings keep coming: Over 60% of customers saved more than 5% of the annual cloud spend through cost optimizations in the last 12 months with us. Additionally, over 40% saved more than 10%, and over 20% saved more than 20%. See more of our cloud saving stats in our report! 💰 [CTA id="84cdfc87-6078-4012-8859-b72cb2586405"][/CTA] The easier the better The concept of FinOps makes the spend smarter in all aspects. When it comes to innovation, don't cut corners for the possibility of a few savings. FinOps can handle commitment-based discounts while engineering teams can take care of limiting costly resources. Our data shows the popular use of commitment-based discounts for easiest-to-implement optimizations. Commitment-based discounts were implemented 47% of the time, accounting for 43% of savings. Terminations of idle resources were implemented 33% of the time, accounting for 42% of the savings. Most 3rd party tools claim to reduce costs through rightsizing, our data indicate limited impact and implementation difficulties. Don't let the word "commitment" scare you! These plans save you significant amounts of money on cloud costs! Third-party tools are spot on with cloud anomalies: According to our market survey, 32% relied on third-party tools while 16% used in-house solutions. Unfortunately, a whopping 20% could only spot a cost anomaly after receiving the bill. Don't miss out on our report for more of these findings! 😉 Anodot customers are beating anomalies at their own game Detecting anomalies before they have a chance to do any real damage is the best way to avoid costly bills. So, how fast can these anomalies be found? Based on FinOps Foundation’s 2023 report, respondents' ability to detect cloud cost anomalies within hours remained similar to that of 2022. Here's what separates Anodot from others: 84% of our customers were able to spot anomalies in mere moments or within a few short hours, saving time and money simultaneously! Anodot keeps FinOps in practice:  A remarkable 80% of surveyed Anodot customers described their cost optimization endeavors as proactive. Final thoughts:  And that's your preview of our 2023 State of Cloud Cost Survey Report. We covered multiple aspects of cloud spending by using our general market survey and our own data findings.  Notable standouts include: The rise of third-party solutions  The increasing challenge of true visibility into cloud costs Cloud spending and savings are more frequent with Anodot customers. Want more of these findings? I bet you do! Check out our comprehensive report to get the full picture on 2023 cloud costs!

The world of FinOps can be pretty complex. Don't get us wrong, it's a fantastic way to align IT and finance teams for maximum efficiency in cloud operations. But for newcomers, it may feel a bit overwhelming at first. That’s why following influential leaders in the FinOps space is a must. The tips, insights, and guidance from these top FinOps performers can give you the confidence and motivation to lead FinOps at your company.  So, let’s look at the key industry players in FinOps in 2023. (In no particular order. They’re all equally fabulous!) 1. Corey Quinn Who says you can’t talk about FinOps with a little flare? Corey Quinn is one of those personalities who tells it like it is without losing his charismatic charm. His “Last Week of AWS" blog offers the trends and news within the cloud ecosystem. Along with his casual demeanor, his writing welcomes readers of all levels of FinOps experience. If you prefer audio learning, check out his podcast "Screaming in the Cloud." 2. Sam Charrington Sam is an avid data scientist advocate. He's on a mission to help folks grasp the cloud better by getting a good handle on tools like ML and AI. His awesome TWIML AI Podcast brings together brilliant minds in ML, data science, and research. Sam keeps you current in emerging cloud technologies so you never miss a beat! 3. J.R. Storment If anyone comes close to being the father of modern FinOps, it’s J.R. Storment. He’s the creator of the FinOps Foundation, one of the leading resources for FinOps practitioners. The foundation aims to bring real stories and practices to cloud management. Instead of making the organization for those who can “only speak the language,” it makes it accessible to anyone wanting to deepen their expertise in cloud cost management.  More impressive? He’s also co-authored one of the most essential books for FinOps practitioners: Cloud FinOps: Collaborative, Real-Time Cloud Value Decision Making. 4. Ben Lorica You might not find a more extensive podcast library on data, machine learning, and AI than Ben Lorica’s “The Data Exchange.”  With regular updates, Ben talks with guests about the latest trends and techniques in data science. For super detailed show notes of each episode, we highly recommend visiting https://thedataexchange.media/. This bonus resource has got you covered with all the info you need to dive deeper into any episode. 5. Scott Chancellor Scott's got an impressive resume. He’s spent 7 years as the Director & General Manager of AWS Insights, and now he's the CEO of Humu, an ML behavioral tech company. With over 20 years of experience in cloud financial services, you know you can rely on this FinOps expert's input. You can follow his LinkedIn page to get the scoop on the happenings in cloud management regularly. So, how do you feel now about diving into the world of FinOps? Seems less intimidating now, right? With influential leaders like these to follow, successfully applying FinOps to cloud management seems much more doable. When you're all set to level up your cloud management game, Anodot can be your go-to platform for transforming your cloud costs.

“Time is money” couldn't be truer than in managing cloud costs. By way of proactive anomaly detection, a chance is given to save time that could have been spent on issue recognition and resolution. Anomaly detection for the Cloud can be tricky since there can be changes in prices & data on billing history anytime. Not to mention, seasonality can mess things up as well. Detecting anomalies in cloud environments is crucial to ensure security, optimize performance, and control costs, among other things. It helps cloud providers, and users manage and safeguard their resources, data, and services in an ever-evolving and dynamic cloud landscape. Translating this capability to numbers, we've detected and saved $7M In just the few months of running anomaly detection for a small subset of our customers. What’s next: Let’s go over the challenges of anomaly detection in cloud environments and how Anodot’s sophisticated algorithm stands apart from the rest. The Challenges of Anomaly Detection in Cloud Environments Cloud cost data can be interesting (to say the least) with its dynamic nature that's affected by changing prices and updates to billing history.  Anomaly detection in cloud environments can be vexing and frustrating, presenting various challenging issues. Such as: Data volume and velocity: Have you ever dealt with the overwhelming amount of data cloud services generate? It's hard to keep up! Not only that, but the data is also subject to rapid changes, which makes real-time monitoring essential for identifying anomalies. Unstructured and inconsistent data: When it comes to cloud environments, there's a whole bunch of systems and platforms that generate all sorts of data—from system metrics and logging data to pricing changes and usage data. And all of that data is tricky to standardize and analyze. False positives: While anomaly detection aims to identify unusual patterns in data, it can often produce false positives due to incorrect assumptions about baseline behaviors. Organizations must be prepared to address these challenges when using anomaly detection for cloud environments, as they are essential for maintaining secure, reliable services. Anomaly detection can pose challenges for cloud data analysis, but it's a must-have tool for keeping your services secure and reliable. So search for a scalable solution with robust and accurate ML-based anomaly detection algorithms. (The search is over; we’ve got you covered!) Why traditional anomaly detection methods may struggle with cloud data In today's world, with data coming from multiple sources more than ever, it's essential to have a reliable algorithm (like Anodot 😉) for detecting anomalies in cloud costs. Without it, detecting and repairing errors can consume an excessive amount of time. Here's why old-school anomaly detectors just can't keep pace with today's cloud costs: Lack of Contextual Information: Traditional anomaly detection methods mainly rely on statistical or mathematical models that analyze data distribution and patterns. But let's face it, in cloud environments, context is king. Understanding anomalies is all about the interrelationships between components, usage patterns, and user behavior. Without this information, accurate anomaly detection is far from guaranteed. Dynamic and Evolving Nature: When dealing with cloud environments, things are constantly changing! Traditional anomaly detection techniques can’t keep up with this data since they use preset thresholds. That means they either rate a lot of false positives or, worse, can’t spot emerging anomalies! 😱 Concept Drift: Cloud environments undergo concept drift, meaning the data distribution and characteristics change over time. Traditional anomaly detection methods rely on historical data and assume stationary data distributions. Hence, they may not adapt well to the evolving nature of cloud data and miss out on spotting new and emerging anomalies. To solve these challenges, we've developed advanced anomaly detection techniques like machine learning algorithms that work for any metric source and automatically discover cross-siloed correlations. Anodot is basically taking anomaly detection in the cloud to the next level!  Facts, Figures, and Real-World Examples To truly grasp the impact of cost anomalies, you gotta see them in action. When these things mess with your finances, it's time to identify pain points and take action. Analyzing these hiccups can uncover helpful insights to create a bulletproof financial strategy and, ultimately, success! So let’s look at some examples of what these anomalies may look like:   Example 1:    One mistake by an engineering team in charge of one of the apps led to unintended consequences. The team released a version of the app that started making too many network calls, resulting in a spike in costs across multiple services. Example 2:  Every month, there’s a charge for a significant amount for the service and operations used in the above example. However, in  February, there was an anomalous spike in the fee, considerably more than the average charge.  Example 3:  There was a sharp increase in cost per usage. After some investigation, it was discovered that the pricing had erroneously been assigned to the more expensive tier, resulting in incorrect charges.  Impact of these detections on businesses and their operations What can we learn from these examples? Detecting and rectifying anomalies before they occur is incredibly valuable. Not only does it save on cloud costs, but it also ensures peace of mind, knowing that you won't be caught off-guard by any unforeseen spikes in the future.  Being proactive in monitoring your cloud environment helps identify anomalies and inform decisions for service optimization. Put checks in place to monitor unexpected spikes or drops in cost to control costs. Did you know? - with Anodot, you can optimize cloud resources & save costs- no need to learn any complicated ML. Interested? Let's take this conversation somewhere else!  Final Thoughts  What have we gathered? We've learned a lot about anomaly detection, haven't we? We've seen that it's a powerful tool for cloud infrastructure monitoring and cost optimization. But we also have to be wary of using a solution that’s out of date, or it could mean a financial loss in cloud costs.  However, when using the right vendor, anomaly detection can be an effective way to save on costs and ensure your cloud environment remains stable.  With Anodot, you don't have to learn any complicated ML techniques because: Our solution is super comprehensive. It reduces all types of false positives! It's super easy to use and integrates smoothly across various sources. Not to brag, but we are also the fastest and most efficient anomaly detection algorithms in the world. 🏆 So, let's kick off your journey toward saving cloud costs today! Let's chat.  

Companies that use artificial intelligence and machine learning to independently monitor databases and the data that's being stored are reaping huge wins in saved time and costs. And it's typically the DataOps teams that can take this project on to success.

Companies that use artificial intelligence and machine learning to independently monitor databases and the data that's being stored are reaping huge wins in saved time and costs. And it's typically the DataOps teams that can take this project on to success.

Cloud tools are becoming indispensable for modern-day FinOps. They can improve efficiency and agility and deliver better client results. But what native cloud tools are right for you, and how can they benefit FinOps? Let's find out. When managing financial operations in your organization, using native cloud tools is a must. Let's take a closer look at some key advantages: Cost Optimization: Native cloud tools have the functionality that helps monitor and optimize your cloud costs. They deliver deep insights into cost usage patterns, resource allocation, and cost drivers, giving you the power to spot overspending and underutilization. Armed with this information, you can make data-driven decisions, like optimizing cloud resources, resizing instances, and using cost-effective purchasing options. In other words, more money in your pocket!  Real-Time Visibility: Cloud-native tools offer real-time visibility into cloud financials by tracking costs, usage, and performance metrics across multiple resources. Rapidly detect cost spikes before they become a problem! And leveraging third-party tools like Anodot on top of native cloud tools can bring even deeper insights into visibility. Budgeting and Forecasting: Cloud apps make budgeting and forecasting simple. You can set spending limits and get alerts when you're approaching them. Align costs with financial targets and plan future expenses. Cost Allocation and Showback/Chargeback: Native cloud tools can help simplify cost allocation by allowing you to assign costs to teams, depts., projects, or cost centers. This feature is handy when multiple stakeholders want to track their respective cloud costs. Collaboration and Accountability: Native cloud tools facilitate collaboration among finance, operations, and development teams, with better communication and collaborative decision-making regarding cost optimization strategies. Collaborating efficiently with colleagues can help avert disasters before they arise! Exploring the Different Types of Native Cloud Tools Available to FinOps Teams There are different types of native cloud tools that FinOps teams can use to maximize related to cost management, optimization, and financial analysis. Let’s look at a few examples:  Resource and Instance Management Tools: Oversee and refine cloud resources like virtual machines, databases, and storage. These tools identify underutilized resources and recommend necessary actions. Examples of such devices include AWS EC2 Instance Scheduler, Azure Advisor, and Google Cloud Operations Suite. Tagging Tools: Allocate costs to departments, teams, projects, or cost centers. By applying consistent tags to cloud resources, these tools enable you to track and monitor your cloud spend without breaking a sweat. Some examples include AWS Resource Groups and Tag Editor, Azure Cost Management and Billing API, and Google Cloud Resource Manager. Rightsizing and Instance Optimization Tools: These tools suggest ways to tweak things to save finances by analyzing how you use the cloud. They'll detect spikes in your FinOps that drain resources. Some examples of these lifesavers include the AWS Trusted Advisor, Azure Advisor, and Google Cloud Operations Suite. Managing Cloud Costs Tools: Organizations can optimize their use of cloud services by aligning their financial targets with cloud spending, ensuring that costs don't spiral out of control. This approach allows businesses to make informed decisions by tracking expenses and understanding the impact of services such as AWS Budgets, Azure Budgets, and Google Cloud Budgets. To optimize cloud costs, native cloud tools can cater to specific functions like financial analysis and cost management. Each tool works hand-in-hand with the other Ais, APIs, or apps to make cloud management a seamless process (BTW, We integrate with native tools from tech giants like Google, AWS, and Azure. You can read all about it in our whitepaper.) Best Practices for Maximizing the Benefits of Native Cloud Tools for FinOps Teams FinOps teams are crucial for handling and fine-tuning cloud expenses and resources. Using native cloud tools, FinOps teams can boost efficiency while keeping costs in check and maintaining high performance. Here are some best practices you can utilize today: Understand the Cloud Environment: To make informed decisions, know the features, pricing models, and services offered by your cloud provider. For instance, AWS, Azure, and GCP are some of the major cloud providers today that provide a broad range of services. A thorough understanding of these offerings enables users to select the most suitable platform for their business needs. (You can learn about it here)  Utilize Cost Management Tools: When using a cloud provider for your business, cost management needs to be a top priority. As your cloud environment scales, your expenses rise too, and unmonitored cloud usage can result in surprising bills. To keep track of your spending and ensure cost efficiency, it's best to rely on your cloud provider's cost management tools. (You dive deeper into these insights here!) Leverage Cloud Provider Support and Expertise: When managing cloud costs, don't hesitate to connect with your cloud provider's customer support and account teams. They are the pros and can offer valuable insights and recommendations explicitly tailored to your usage patterns. Take advantage of their expertise! And don't forget that there are also industry experts in cloud cost management, like us! We're always here to assist and committed to helping you optimize your cloud resources and achieve your business objectives. [CTA id="7ace1417-3b85-473a-bc07-acf3753c3270"][/CTA] Final Thoughts  Adopting native cloud tools can elevate the agility of FinOPs while ensuring they remain up-to-date with the latest industry trends. The improved service delivery with better client results is a win-win situation for all.  Discover invaluable insights into efficiently utilizing cloud-native FinOps tools with our white paper. Learn when it's best to consider investing in third-party platforms, such as Anodot, which can help you surpass limitations inherent in native solutions. You can also get the latest tips and trends on cloud costs by checking out our blog.  

In the complex and dynamic realm of data analytics, real-time anomalies serve as insights to issues a business faces. A pervasive and enduring conundrum persists: accurately discerning between anomalies of significant importance and those of lesser consequence. This distinction is a nontrivial task as not all anomalies bear the same weight. Certain deviations signal crucial disruptions demanding immediate rectification, while others merely reflect inconsequential deviations that bear no impact on the business. To address this predicament, Anodot has devised a transformative solution in the form of a feature termed "Alert Tuning Recommendations." Leveraging a sophisticated semi-supervised Machine Learning (ML) approach, and one of the largest labeled anomalies datasets known, Alert Tuning Recommendations strives to meticulously differentiate essential anomalies from insignificant ones. The paramount objective of this novel feature is to substantially minimize the occurrence of false positive alerts, whilst ensuring genuine alerts are not overlooked. By achieving this balance, this technology guarantees that users' attention remains unequivocally focused on the most impactful business issues.   Underlying Mechanism of Alert Tuning Recommendations The Alert Tuning Recommendations feature operates via a user-centric, semi-supervised learning process that can be elaborated in three distinct steps: 1. User Feedback on Anomalies: The first step engages users in an active manner, requesting their subjective evaluation of the anomalies detected by Anodot's platform. Based on individual preferences and the impact on their specific use case, users can classify the anomalies into "good catches" or "not interesting." For example, in the context of monitoring sales for a specific product, a negligible drop might be classified as "not interesting" due to its limited significance on overall sales performance. Figure 1 shows examples of a “good catch” and a “not interesting” alerts, with the main difference between the two being the total impact of the anomalies. Figure 1: Two anomalies labeled by the user. The top is a good catch because of the volume of drop in purchases, while the second is marked as Not Interesting because the volume of the drop has minimal business impact.  2. Anomaly Feedback Aggregation by Anodot: Subsequently, the platform assimilates the feedback provided by all the users, thereby gathering a comprehensive pool of classified anomaly data. This extensive, rich dataset serves as the cornerstone for the ensuing steps, ensuring the recommendations are grounded in substantial real-world feedback. 3. Activation of the Semi-supervised ML Auto-tune Service: This final stage deploys the semi-supervised ML auto-tune service, which utilizes the amassed user feedback to fine-tune alert parameters via a two-step mechanism: a. Development of an XGBoost Classifier: Leveraging the prowess of the XGBoost algorithm, the platform constructs a classifier. This classifier is designed to segregate anomalies into "good catches" or "not interesting," mirroring the classifications provided by users. Remarkably, the classifier's training process is informed by an expansive dataset comprising over 100K anomalies that have been labeled by users across a span of three years, making it one of the largest labeled time-series anomaly datasets known to date. This classifier utilizes various inputs for the training process, including attributes of the anomaly, metadata associated with the time series, and other intricate features, thereby ensuring a holistic analysis. b. Auto-tuning Alert Parameters: For each set of time series metrics that a user expresses interest in monitoring, the system undertakes a rigorous exploration of past anomalies. Unlabeled anomalies are probabilistically labeled by the trained classifier, while known user feedback is assigned to the labeled anomalies. Then, employing these labeled anomalies, the system determines the key attributes that characterize a "good catch" anomaly, heavily weighting towards anomalies reinforced by user feedback. Consequently, an optimal assortment of anomaly characteristics that warrant alert triggers is selected, effectively reducing false positives without curtailing the true positive rate.  The culmination of this process is a set of recommendations for alert attributes, which is then presented to the user through the Anodot interface. Figure 2 shows a screenshot of recommendations for a user, with the ability to accept or ignore a recommendation (or part of a recommendation).   Figure 2: The auto-tune recommendation screen. The user can select which recommendations to accept or reject and is shown the expected reduction in false positive rate. The Implications and Impact of Alert Tuning Recommendations The Alert Tuning Recommendations feature signifies a notable stride in data-driven decision-making. With its deployment across Anodot’s customer base, this feature has evidenced its efficacy by markedly reducing false positive rates, in certain instances by a striking margin of over 60%. This feature thus capacitates users to concentrate their attention exclusively on alerts of genuine significance, enhancing both efficiency and productivity. The amalgamation of human feedback and cutting-edge ML techniques, as exemplified by Alert Tuning Recommendations, exemplifies Anodot’s commitment to advancing user-focused solutions that effectively harness the power of data to drive decision-making.   Concluding Remarks As we traverse an increasingly data-centric world, the capacity to segregate valuable signals from superfluous noise assumes paramount importance. Anodot's Alert Tuning Recommendations represents an innovative solution to this quandary. By integrating user feedback with ML-driven analysis, Anodot is consistently redefining the boundaries of anomaly detection and alerting. By juxtaposing human intuition and AI precision, this feature ensures your focus remains trained on the most pertinent data points, thereby streamlining operations and enhancing efficacy.

Missed our latest webinar on FinOps for MSPs? We’ve got you covered! This blog post will cover what the FinOps experts discussed and the main things to remember. FinOps are revolutionizing MSP operations by adding a data-driven approach to cost management. This method helps MSPs optimize their cloud usage, provide white-glove support to customers, and give visibility on their expenses. Why it matters:  Competition among MSPs is fierce as businesses aim to maximize the value of their IT investments. You must prove your worth to prospects and customers to stand out from other providers.  Enabling FinOps can be a highly effective strategy to distinguish your cloud reseller business, and we’ll show you why. In this webinar recap, we’ll highlight: How MSPs can add value with FinOps services FinOps success from an MSP perspective  Q&A  Not much of a reader? Watch it on demand!   Empowering MSPs with FinOps Services Our presenter, Melissa Abecasis, has been there. As a former worker of an MSP, she's dealt with various FinOps issues, making her an expert solution finder. Below are some examples of FinOps challenges and specific tactics to resolve them.    Roadblock: Many recruits needed a FinOps background. This means more time needs to be devoted to educating them. MSPs need experienced consultants and need them fast. Training slows down operations on both the vendor and customer sides. Customer satisfaction is at risk when services are prolonged.  Solution: Find a tool that expedites the learning curve for FinOps engineers. A platform that can give real-time guidance and explanations to customers. Psssst… Anodot’s Recommendations feature can help with this!    Roadblock: Unsatisfied customers are getting repetitive recommendations from their reps. MSPs need to show their insights are relevant. The value of service decreases for the customer. Shaken trust in vendor’s credibility.  Solution: A customizable feature that can automate reminders so the information given is timely and accurate. BTW: Anodot's Exclude feature lets you add notes and tweak the timing to bring up specific points again when they make the most sense to the customer.   Roadblock: Cloud costs are increasing for the customer.  The customer seeks validation of the value offered by the MSP. The customer wonders if having a FinOps team is worth the investment. There is no evidence to support an ROI for this service. Solution: Demonstrate to your customers that your work saves money by providing insights that your services are still cost-effective even if cloud costs are going up (FYI: Anodot has a tool that automatically tracks actions, so your MSP teams don’t have to do it manually).    Watch Melissa's full presentation in the on-demand webinar.     Achieving Financial Operations Success from an MSP Perspective Validation is vital when proving that FinOps adds value to cloud-based environments. That’s why first-hand experience with FinOps significantly increases its credibility.  Sergio Gonzaga, Solutions Architecture Lead at CloudZone, tells about his FinOps journey with his MSP company. Here are some essential points he covered:   Highlight 1:  Flexible billing and custom views can help MSP customers understand if the services they are utilizing (or not) are within budget.  Custom views help see spending across departments and business units Tech strategy can be implemented based on the insights. A better comprehension of cost variations across different tiers or sizes for the same services.   Highlight 2: Tracking cost progress is vital to understand cost impacts during production.  Relevant dimensions can ensure that MSP spending is reasonable.  Tailored services for launching a custom namespace can be adjusted as needed. Monitoring cost per app, component, or by the team can give better support to customers with ML ops.    Highlight 3: Accuracy is critical when providing software-as-a-service subscriptions. Context support in a multi-tenant solution is crucial.  The location of customers in different regions can incur high costs. With Anodot's insights, informed decisions can be made and support overall business decisions.   You can watch Sergio’s entire session by viewing the on-demand webinar.   FinOps for MSPs: Q&A  Our attendees had some outstanding questions during our webinar. Here are a few of the top questions they had: What’s Anodot's level of support for MSPs regarding tool learning? Melissa: Our customer success team is included for all customers, and we offer one-on-one training and training programs to ensure you understand and get value from the tool. We are developing a FinOps training program to support your new employees using the tool with a cloud background. Our support person is closely connected with customer success and R&D teams to ensure smooth and swift operations. Does Anodot have a way to double-check the margins from their reporting?  Melissa: Yes, we provide options to break down margins when moving from partner to customer. There are two ways to do this.  First, by clicking on the "I" icon in the billing history, you can see a breakdown of all line items, how the margin was calculated, where it was purchased, which account received it, and how much they received that did not belong to them.  Second, the cost usage explorer breaks down costs and allows you to move from partner to customer for specific services down to the resource level. Finally, you can contact us through email, Slack, or phone to fully understand where the margin comes from. How well can Anodot capture SaaS service costs from AWS, Azure, and GCP? Melissa: This year, we will introduce specific SaaS cost support and provide details at a later date. We are committed to applying FinOps to cost areas, including SaaS. With business mapping, we can see a split of managed services in AWS versus non-managed services. This helps us compare which costs more and decide on the architecture.   Final thoughts  MSPs can reap the rewards of a FinOps-oriented framework. It's a great way to save on cloud costs, up customer ROI, and ensure you deliver value to your end users.  Wanna learn more?  Grab our FinOps guide for MSPs FYI: Anodot’s cloud cost management solution can help align MSP cloud costs with key business dimensions. Let’s talk!

The public cloud can deliver significant business value across infrastructure cost savings, team productivity, service elasticity, and DevOps agility. Yet, up to 70% of organizations regularly overspend in the cloud, minimizing the gap between cloud costs and the revenue cloud investments can drive. Cloud cost management, or the practice of FinOps, is targeted at helping businesses maximize the return on their investments in cloud technologies and services by helping engineering, finance, technology and business teams to collaborate on data-driven spending decisions.  A successful cloud cost management strategy will use cost management tools (also known as FinOps tools) to manage cloud costs, and continuously optimize cloud spend to increase cloud efficiency. These include tools offered by the major public cloud service providers — AWS, Azure, and Google Cloud.  Anodot has developed a comprehensive white paper exploring the capabilities and limitations of each and how third-party solutions like Anodot can help drive FinOps success. What are FinOps Tools?   A FinOps tool is a cloud provider or third party software that helps you improve your cloud spend. This includes cloud cost management tools, which provide real-time data on cloud usage, giving you a clear look into areas of inefficiency and providing recommendations to optimize spend.  Key Functions and Features of FinOps Tools   A good FinOps tool will help you collect data on how your company uses the cloud, analyze that data, and provide recommendations on how to improve your spending. The information is packaged in such a way so that it can be shared with anyone from shareholders to those uninvolved with the nitty-gritty of the cloud, making it easy for you to break down where you need to cut or add, and why.  Other FinOps features include:  Improved cloud spend transparency. Benchmarking for cloud spend. Bettered cloud spend accountability.  Key AWS FinOps Tools   Amazon Web Services (AWS), the largest public cloud service provider, devotes one-sixth of their Well-Architected Framework to avoiding unnecessary costs. The Well-Architected Cost Optimization Pillar focuses on operationalizing using a range of discrete AWS-native tools and offers little insight for businesses with modern multi-cloud strategies.  AWS offers the most extensive suite of cost management and billing tools, including: AWS Cost Explorer  AWS Cost Explorer is a tool that enables you to view and analyze your costs and usage using the main graph, the Cost Explorer cost and usage reports, or the Cost Explorer RI reports. View historical data for the last 12 months, forecast your spending for the next 12 months, and get recommendations for which RIs to purchase.  Using Cost Explorer, you can identify areas that need further investigation and see trends that can help you better understand your costs. The Cost Explorer also provides preconfigured views that provide an overview of your cost trends and allow you to customize them. AWS Cost and Usage Report + Cloud Intelligence Dashboards The Cost and Usage Report, or CUR, is the foundation for AWS (and all third-party) cost management capabilities, and provides the most comprehensive set of usage and cost data available, including additional metadata about AWS services, pricing, Reserved Instances, and Savings Plans. The Cloud Intelligence Dashboards are a collection of Amazon QuickSight dashboards that are based on the CUR reports. They offer powerful visuals, in-depth insights, and intuitive querying without having to build complex solutions or share your cost data with third-party companies.  The cloud intelligence dashboards are built on native AWS services and take anywhere from 1-2 hrs to install and onboard per dashboard. Dashboards come in three main forms: Cost and Usage Report Dashboards Compute Optimizer Dashboard Trusted Advisor Organizational Dashboard AWS Budgets, AWS Budget Actions and AWS Cost Anomaly Detection AWS Budgets — establish and enforce budgets for certain AWS services, and send messages or emails through the Simple Notification Service (SNS) when you reach or exceed your budget. Budgets allows you to specify an overall cost budget or relate the budget to certain data points, including data usage or the number of instances. The dashboard shows views similar to Cost Explorer, showing the use of services against budgets. AWS Budget Actions — configure actions that will be applied automatically or via a workflow approval process once a budget target has been exceeded. There are three action types: Identity and Access Management (IAM) policies, Service Control policies (SCPs), or target running instances (EC2 or RDS). Actions can be configured for actual (after they’ve occurred) or for forecasted (before they occur) budgeted amounts. AWS Cost Anomaly Detection — develop your own contextualized monitor and receive notifications of any anomalous spending, through a series of simple steps. When you have set up your alert and monitor preference, AWS can provide you with daily or weekly alerts via email or SMS. These include summary and individual alerts. You can monitor and carry out your own anomaly analysis using AWS Cost Explorer. [CTA id="89a76a81-7f5c-479e-bca6-66d32f9e02bb"][/CTA] Key Microsoft Azure FinOps Tools   Microsoft Azure Cost Management is a set of FinOps tools that enable you to analyze, manage, and optimize your Azure costs that is offered at no additional cost. Unlike AWS, which often ignores other cloud providers, Microsoft also offers paid cost management for other clouds, namely Cost Management for AWS, which is charged at 1% of the total AWS-managed spend.  Microsoft Azure Cost Management is a more limited suite of tools when compared to AWS and consists of the following features: Azure Cost Analysis Azure Cost Analysis lets you explore and analyze your organizational costs. It shows you the cost, forecast, budget (if used) and provides dynamic pivot charts breaking down the total cost by common attributes such as service name, location, or account name.  Azure Cost Alerts Azure Cost Alerts help you monitor your Azure usage and spending with cost alerts. When your consumption (budget or usage) reaches a predefined threshold, alerts are generated by Cost Management. There are three main types of cost alerts: budget alerts, credit alerts, and department spending quota alerts.  Azure Budgets Azure Budgets help you proactively manage costs by setting thresholds and inform others about their spending using alerts. Budgets are created using the Azure portal or the Azure Consumption API. Budget alerts support both cost-based and usage-based budgets. In the Azure portal, budgets are defined by cost. Using the Azure Consumption API, budgets are defined by cost or by consumption usage.  Azure Advisor Recommendations Cost Management works with Azure Advisor to help you optimize and improve efficiency by identifying idle and underutilized resources. Advisor makes recommendations for: buying reservations; resizing or terminating underutilized VMs; deleting unused network resources such as public ip addresses and express route circuits; and provisioning optimal cosmos DB request units.  Key Google Cloud FinOps Tools   Google Cloud Platform (GCP) is one of the most used cloud platforms on the globe. Google offers a variety of cost management tools and 24/7 billing support at no additional cost for Google Cloud customers. Like other public cloud providers, you will be charged for using Google Cloud services such as BigQuery, Pub/Sub, Cloud Functions, and Cloud Storage. Google Cloud Cost Management offers even fewer features than Microsoft Azure Cost Management, with only three features: Google Cloud Billing Reports Google Cloud Billing reports give you an at-a-glance and user-configurable views of your cost history, current cost trends, and forecasted costs in the Google Cloud console. Several different reports are available for your billing data analysis needs. Google Cloud Billing Budgets Google Billing Budgets trigger alerts to inform you of how your usage costs are trending over time. Budget alert emails are notifications only and do not automatically prevent the use or billing of your services when the budget amount or threshold rules are met or exceeded. Google Cloud Recommender Google Cloud Recommender is a service that provides recommendations and insights for using resources on Google Cloud. These recommendations and insights are per-product or per-service, and are generated based on heuristic methods, machine learning, and current resource usage.  On an ongoing basis, Recommender analyzes current usage of your Cloud resources for available recommenders and insight types and provides recommendations and insights designed to optimize usage for performance, security, cost, or manageability. How To Choose The Right FinOps Tool For Your Needs   When considering the best FinOps tool for your organization, you'll want to keep the five features in mind:  Analytical ability. Is the tool you're considering able to accurately forecast so you know how your yearly budget and spending might evolve? Ensure it provides trend analysis so you know you're spending at the most efficient level, and graphics that break numbers down so the data is easy to explain to stakeholders.  Easy scalability. Your cloud monitoring tool should scale with your company. If you're midway through a cloud migration, you'll want a tool that can support you through the migration. If you need to scale your business back, your chosen tool should be able to keep pace.  Straightforward integration. Your chosen tool should integrate easily with your cloud provider (ex: AWS, GCP, Azure) so that all of your tools continue to operate at optimal performance post-integration.  Full automation. One of the biggest appeals of a FinOps tool is its ability to automate simple but tedious tasks like cost management, budgeting, and tracking. Seamless user experience. Since FinOps tools offer so many capabilities, sometimes the learning curve can be steep. You'll want to look for a tool that offers a user-friendly experience with intuitive dashboards and a simple interface.  No matter your company size or goals, these five needs will remain the same. Keep these top of mind and you’ll find a FinOps tool that perfectly fits your organization (trust us, we’re the experts!).  Overcome gaps in native solutions with Anodot    Anodot’s cloud cost management solution 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, utilization, and performance of their cloud services. With continuous monitoring and deep visibility, businesses gain the power to align FinOps, DevOps, and Finance teams and reduce their total cloud bill.   Multicloud Visibility - Anodot seamlessly combines all of your cloud spend into a single platform. Monitor and optimize your cloud cost and resource utilization across AWS, GCP, and Azure.  Eliminate Waste –  Anodot’s easy-to-action savings recommendations enable your DevOps team to easily implement spending and service changes that can drive significant savings.  Allocate Costs – See cost causation and allocate spend by service, business unit, team, and app with deep visibility across AWS, Azure, GCP, and pod-level Kubernetes.  Enable FinOps – Avoid bill shock with near real-time alerts and insightful, ML-driven forecasting.  Anodot also provides granular insights into Kubernetes that no other cloud optimization platform offers. Businesses can easily track spending and usage across clusters with detailed reports and dashboards. Anodot for Cloud Costs’ powerful algorithms and multi-dimensional filters enable a deep dive into performance and identify under-utilization at the node level.