It's never been a better time to be a Managed Service Provider (MSP). Why? Small and medium businesses (SMBs) use cloud-based services for their operations. Eighty-eight percent say they currently use an MSP or are considering one. But many obstacles remain even if SMBs are in high demand for MSPs. They need to keep their profits and revenue growing, focusing on cloud unit economics, customer pricing strategies, and efficient operations. To be the go-to choice for cloud services for SMBs, MSPs must meet customer needs in cloud migrations and financial management. Let's check out how FinOps contribute to successful cloud management and how MSPs can help with this goal. (This blog is just the beginning, get deeper insights in our white paper!) [CTA id="b1547947-bc88-4928-af34-4d0281703d76"][/CTA] Why FinOps so important for modern organizations FinOps is a practice that combines data, organization, and culture to help companies manage and optimize their cloud spend. Furthermore, it brings a holistic approach to cloud financial management and helps organizations maximize their ROI in cloud technologies and services by enabling teams to collaborate on data-driven spending decisions. The relationship between MSPs and FinOps As cloud finance and operations experts, MSPs can help customers optimize cloud costs, standardize operations, and make informed business decisions during their cloud journey. What does that mean? MSPs must be ready to offer FinOps services to customers who wanna level up their cloud financial management game. In a super competitive cloud services market, managed FinOps allows MSPs to stand out and build customer trust. What you need to know for FinOps success for you and your customers Picking the right partner solution is key to nailing your FinOps game, no doubt about it.  Since FinOps is a new approach to cloud management, limited solutions are aligned with its phases and capabilities, despite a tooling landscape with over 100 vendors. Key tool categories to look for when selecting a cloud finance solution When evaluating FinOps platforms, ensure they are designed specifically to deliver managed services. Make sure the FinOps platforms you're considering check all the boxes on this list: Connect to major cloud service providers (AWS, Azure, and Google Cloud) to monitor and manage spend in complex multi-cloud environments. Integration to combine all cloud spending into a single platform is crucial for providing complete multi-cloud visibility and optimizing resources. A FinOps platform to help you successfully implement a robust tagging strategy for every customer and accurately allocate 100% of their costs across all accounts and environments. Automated monitoring for cost anomalies. Cloud cost anomalies are unexpected variations in cloud spending that exceed historical patterns. Assess how effectively the platform enables waste reduction. It should automatically identify and tailor waste reduction recommendations for each customer, including idle resources, rightsizing, and commitment utilization. FYI, Anodot checks all these boxes and then some! Meeting these requirements is integral to FinOps and accelerates cloud-based business value. Understanding where costs are incurred, who generates them, and how they contribute value is key to achieving this goal. Improving margins and customer experiences To make the most of your margins, MSPs must accurately and efficiently invoice customers using a clear pricing strategy. For many MSPs, rebilling can be a real-time suck and eat into already low margins. It gets even trickier with the mixed and unmixed rates from cloud providers, which leads to monthly invoice explanations to clients. Flexible billing solutions for MSPs to embrace Allocate usage and costs to customers. Block out margins and bill customers with adjusted rates Easily add any billing rule and/or credit type Add charges for support and value-added professional services Control usage of high-volume discounts, reallocate SP/RI, and manage credits The importance of real-time visibility into cloud costs Additionally, Managed Service Providers (MSPs) need complete visibility into their usage, costs, and margins. Gain a comprehensive view of customer costs, margins, and usage across the portfolio. Access a detailed billing history with a breakdown of each customer's margin to the Service Provider (SP) and Reserved Instance (RI) level. Justify invoices by analyzing bills from both the partner and customer perspectives. Easily switch between cost views with and without margins. MSPs go further with FinOps practices MSPs who prioritize FinOps make systems more appealing to customers. Why? It demonstrates your commitment as a partner who helps them save money and time in cloud management. Plus, it helps you become a fierce competitor among other MSPs. Remember to find a vendor to help optimize cloud spending while aligning FinOps, DevOps, and finance teams—without adding operational complexity or burdening management. (Hey, that's us!) Looking for a more in-depth analysis of how FinOps can advance MSPs? Check out our white paper!

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 or a detailed review for a deeper dive. 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. 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. That's why FinOps is such a life-saver when it comes to cloud costs, companies maximize cloud investments, achieving more with fewer resources. Almost 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] 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 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.