In our first blog post, we introduced the concept of cloud unit economics—a system to measure cost and usage metrics. It helps maximize cloud value for better outcomes per dollar spent. We reviewed what cloud unit economics is, why it’s crucial to FinOps success, and how it enables organizations to unlock the full business value potential of cloud computing. To quickly recap, cloud unit economics provides an objective measure of cloud-based SaaS development (e.g., cost to produce) and delivery costs (e.g., cost to serve) on a per-unit basis, directly supporting every FinOps principle, and depends on key interactions across all other FinOps domains. Cloud practitioners seeking to balance cost optimization and value delivery must understand cloud economics and embrace this FinOps capability.  In this blog post, we will take a deep dive into the benefits of cloud unit economics, how to get started, and discuss the FinOps Foundation’s cloud unit economics maturity model. (Some of the information in this blog series has been adapted from Unit Economics Working Group by FinOps Foundation under the Attribution 4.0 International (CC BY 4.0) license.) What are the benefits of cloud unit economics? Unit economics and the measurement of unit costs are important elements of FinOps that enable enterprises to make informed, data-driven decisions about their cloud investments. Cloud unit economics is a method for maximizing value that allows you to: Focus on efficiency and value instead of total cost Communicate the cost and value of all your cloud activities Benchmark how well you're performing vs. your FinOps goals and the market Identify areas for improvement Establish efficiency targets Continuously optimize to maximize return on investment With cloud unit economics metrics, multiple stakeholders can engage in meaningful discussions about cloud investments, moving conversations from absolute spend to business value achieved per unit of cloud spend, enabling inter-departmental collaboration essential to FinOps success.  Additionally, cloud unit economics helps organizations quantify the impact of cloud spend on business performance, explain engineering contribution to gross margins, improve profitability analysis and forecasting, support data-driven pricing decisions, build cost optimization plans, and increase profit margins. Cloud unit economics is critical to understanding the connection between current business demand and cloud costs, how predicted changes in business demand will impact future cloud costs, and what future cloud costs should be if waste is minimized. Organizations that can successfully measure and integrate cloud unit economics into their FinOps practice can gain insights that will help them maximize the business advantage they obtain in the cloud. How to get started with cloud unit economics Cloud unit economics metrics don’t have to be about revenue—which may be challenging for many organizations due to their business type or maturity level. By measuring unit costs, organizations can quickly build a common language between stakeholders that helps ensure decisions are made quickly based on data-driven insights rather than guesswork or intuition.  You should start discussing cloud unit economics at the very beginning of the FinOps Journey—it is as important as it is complex to implement. To get started: Identify your first unit cost metric/s and build a unit cost prototype—cost per customer or tenant is a good metric to start with. Create a systematic way (e.g., automation) to collect and process the data from existing data sources including cloud bills, logs, data warehouses, and APM platforms. Share insights to build support and encourage unit cost integration in your FinOps activities. Make sure the FinOps team is responsible for maintaining a repository of cloud unit economics metrics and articulating their business value [CTA id="6c56537c-2f3f-4ee7-bcc1-1b074802aa4c"][/CTA] The FinOps Foundation's cloud unit economics maturity model can serve as a guide to planning your next steps, and achieving better adoption and use of cloud unit economics in your FinOps practice. Adapted Cloud Unit Economics maturity model by FinOps Foundation When initially adopting cloud unit economics, choose metrics that are supported by existing data sources and simplify unit cost models. Keep in mind, unit metrics should not be static, but should evolve to reflect business objectives and insights gained. In later stages, you may want to add new data sources, modify financial inputs, or add new unit metrics. The most important thing to do once you have your first metric/s is to incorporate unit costs into your FinOps activities: Make strategic decisions and plan optimization activities based on unit costs—rather than total costs Calculate unit forecasts and budgets based on unit costs Leverage unit metrics in usage and cost conversations with engineers Communicate value using unit metrics and build a culture of FinOps Cloud unit economics metrics link cloud spending to business value, allowing stakeholder groups to make informed decisions about how to use the cloud most effectively. Discussions about cloud unit economics should begin as soon as FinOps stakeholders are engaged. Delaying this activity usually results in higher cloud costs, decreased team motivation, and slower FinOps culture development. In the final part of this three-part series, we will discuss best practices for implementing cloud unit economics. Change the economics of your cloud with Anodot With certified FinOps platforms like Anodot, you can establish and mature FinOps capabilities faster. Anodot is the only FinOps platform purpose-built to measure and drive success in cloud financial management, giving organizations complete visibility into KPIs and baselines, advanced reporting capabilities, and savings recommendations to help control cloud waste and improve cloud unit economics. Anodot helps FinOps teams quantify the cloud’s role in financial performance, forecast profitability, and optimize their unit costs to maximize their profits. Learn more or contact us to start a conversation.

The cloud’s elasticity—the ability to scale resources up and down in response to changes in demand—as well as variable cost structures offer significant advantages, enabling enterprises to move from rigid capex models to elastic opex models where they pay for what they provision, with engineers in control and focused on innovation, becoming true business accelerators. But this benefit is also the cloud’s Achilles heel, because when engineers focus on speed and innovation, cloud bills soar, becoming one of the most expensive cost centers for modern enterprises. This creates financial and operational challenges that require the creation of systems to measure the variable costs and usage metrics associated with dynamic infrastructure changes. [CTA id="82139892-d185-43ce-88b9-adc780676f66"][/CTA] In this blog post (the first of a three-part series on cloud unit economics) we’ll introduce the concept of cloud unit economics as a system to objectively measure dynamic cost and usage metrics, and continuously maximize cloud value to deliver more outcomes per each dollar spent. Understanding cloud economics and embracing this FinOps capability is essential for cloud practitioners aiming to balance cost optimization and value delivery. By monitoring key unit economics metrics and implementing unit-metric-driven cost optimization strategies, businesses can unlock the full potential of cloud services while maintaining financial efficiency. (Some of the information in this blog series has been adapted from the Unit Economics Working Group by FinOps Foundation under the Attribution 4.0 International (CC BY 4.0) license.) What is cloud unit economics? Cloud unit economics and the measurement of unit costs is an important part of FinOps that enables enterprises to make informed decisions about their cloud investments. It’s the specific application of unit economics—direct revenues/costs measured on a per-unit basis—to cloud financial operations, which directly supports every FinOps principle and depends on key interactions across all other FinOps domains. It allows you to: Communicate the cost and value of everything your organization does in the cloud Benchmark how well you're performing versus your FinOps goals and peers Continuously optimize to deliver more value Unit economics metrics provide an objective measure of cloud-based SaaS development (e.g., cost to produce) and delivery costs (e.g., cost to serve). By understanding the economic principles underpinning cloud services, organizations can create cost-effective strategies that optimize their bottom line while at the same time leveraging cloud-based technologies to improve efficiencies and increase value for customers. If you're a MSP seeking to get your customers more value with FinOps, get more insights by watching our webinar. [CTA id="574cb89f-f2c3-4cc5-b4f5-a7c98f7f436a"][/CTA] Cloud unit economics are crucial to FinOps success By using CUE metrics, multiple stakeholders can engage in meaningful discussions about cloud investments, quantify the impact of cloud spend on business performance, and make better product and pricing decisions. Cloud unit economics move conversations from absolute spend to business value achieved per unit of cloud spend, enabling inter-departmental collaboration essential to FinOps success.  Cloud economics is a powerful tool that can be used to maximize the value of cloud computing and optimize an organization’s use of the cloud. By measuring unit costs, organizations can maximize profitability and value delivery while remaining within their budget constraints. Here’s why you should start measuring unit costs as early as possible: With the cloud, you’re buying time, not things. It is therefore crucial that you consider how to maximize your cloud technology investments by making data-informed decisions. The cloud relies on a variable cost, elastic opex model where enterprises pay for what they provision—with engineers in control, not procurement. To maximize your cloud investment, you must understand the TCO of the cloud (beyond compute, storage, and db) including shared costs and secondary services. Cloud pricing models have a dramatic impact on cloud unit economics. RI/SP and other commitment-based discounts can completely alter your cloud economics. Forecasting and budget management require a thorough understanding of cloud unit economics, not only for expected costs, but also for supporting future demand. It’s better to make strategic decisions and optimize costs based on unit costs rather than total costs. Building FinOps culture and communicating cloud costs and value with engineers is best accomplished with unit metrics. It’s important to note that data analysis and cost allocation are fundamental FinOps capabilities for effective unit cost measurement. You must establish ‌granular cost/usage visibility and allocation before you can start measuring unit costs. Cloud unit economics unlocks the value of cloud computing Cloud economics is a powerful concept in FinOps that can help organizations unlock the full business potential of cloud computing.  By leveraging cloud unit economics metrics, businesses can realize:  Lower cloud costs Motivate cloud stakeholders Quantify engineering contribution to gross margins Improve profitability analysis and forecasting Build better cost optimization plans Increase profit margins Moreover, having a common language between stakeholders helps ensure decisions are made quickly based on data-driven insights rather than guesswork or intuition. This is especially beneficial when trying to manage costs while still maximizing profits from new sources of revenue within budget constraints. Cloud unit economics metrics can help you focus on efficiency and value, enabling you to establish efficiency targets and identify areas for improvement. Despite its benefits, CUE is elusive for many FinOps teams. According to our market research, 70% of companies want to measure unit economics metrics but are not there yet. Where does your organization stand? In our next blog post in the series, we will take a deep dive into why cloud unit economics matters, its benefits, and how to get started, as well as FinOps Foundation maturity models. Improve your cloud unit economics with Anodot Certified FinOps platforms, like Anodot, can help you establish and mature key FinOps capabilities faster. Anodot is the only FinOps platform purpose-built to measure and drive success in cloud financial management, giving organizations complete visibility into KPIs and baselines, advanced reporting capabilities, and savings recommendations to help control cloud waste and improve cloud unit economics. Anodot helps FinOps teams quantify the cloud’s role in financial performance, forecast profitability, and optimize their unit costs to maximize their profits. Learn more or contact us to start a conversation.

Want to hear a sad but true fact? 70% of companies overshoot their cloud budgets.  Why is that? Although the cloud is a mighty tool for speed, scalability, and innovation, the inability to see costs can lead companies to limit cloud usage, which hampers innovation and puts them at a disadvantage against the competition.  Rather than limiting cloud usage, adopting the FinOps approach provides the insights you need to feel confident about your cloud costs. The goal of FinOps is not to reduce cloud costs but to maximize the value of your cloud technology investments. An organization can benefit from FinOps in several ways: Business executives can ‌leverage the cloud to gain a competitive edge.  Engineering can benefit from innovation, cost efficiency, and faster delivery. Finance teams can analyze, allocate, and forecast cloud costs more effectively, reducing budget variances. Procurement teams can negotiate better rates, maximize benefits, and procure cloud services more efficiently. So, how do you get started on a successful FinOps journey? In this blog, we’ll briefly explore how to implement a successful MVP FinOps strategy in your organization. (For a deeper dive on this, check out our whitepaper on The Business Value of Cloud and FinOps.) A quick recap on FinOps (the what, the why, the when) What is Finops?  FinOps, short for Financial Operations, is a discipline that encompasses managing and optimizing cloud costs. It focuses on ensuring transparency, accountability, and efficiency in cloud spending.  Here are some key points about FinOps to keep in mind: - FinOps involves collaborating with cross-functional teams, including finance, operations, and IT, to drive financial accountability in cloud usage. - The main goal of FinOps is to strike a balance between cost optimization and innovation in the cloud, enabling organizations to maximize the value they derive from their cloud investments. - It involves implementing cloud financial management practices, such as budgeting, forecasting, cost allocation, and showback/chargeback to enhance cost control and decision-making. - FinOps also emphasizes using cloud cost management tools and automation to gain visibility into cloud usage patterns, identify cost-saving opportunities, and optimize spending. - Through adopting FinOps, businesses can achieve greater financial transparency, optimize cloud costs, and align cloud investments with their overall business objectives. Why do you need FinOps?   Finops combines financial and operational practices to optimize cloud spending and maximize ROI.  Here’s what your organization can gain with FinOps: - Scalability: Finops helps align cloud resources with business needs, allowing organizations to scale their operations efficiently. - Cost Optimization: By analyzing cloud expenses, Finops identifies opportunities to reduce costs and eliminate wasteful spending. - Budget Management: With Finops, businesses can set budgets, monitor spending against those budgets, and make necessary adjustments. - Data-driven Insights: Leveraging data analytics, Finops provides valuable insights on cloud usage, trends, and cost drivers. - Collaboration: Finops promotes cross-functional collaboration between finance, operations, and IT teams, fostering a holistic approach to financial management. When should you start FinOps?  There’s never a bad time to initiate a FinOps approach to managing cloud costs. The benefits (as mentioned above) can have an immediate, positive impact on a business’s bottom line. The sooner you optimize cloud spending, the sooner your business will reap those benefits.  The main challenge is getting your organization and team on board ASAP.  So, what is the best approach to building a FinOps practice? Start small and gradually increase scale, scope, and complexity to avoid overwhelming teams with change. [CTA id="47462b23-d885-42f9-9a91-7644f2c84e50"][/CTA] Building solid foundations in each FinOps phase with Anodot’s model Starting at a small scale with a limited scope allows you to assess the outcomes of your actions and gain insights into the value of further action. From this angle, you can introduce new principles in your organization without discouraging them with abrupt change. (It's a win-win situation!) Anodot’s MVP FinOps implementation model, presented in detail in the white paper, can help lay the foundation for active FinOps while keeping engineers focused on speed and innovation. The MVP FinOps approach is based on the three basic FinOps components—people, processes, and tools: MVP FinOps team The MVP approach begins with a small cross-functional group that gradually builds the FinOps practice by focusing on a specific challenge or activity.  Identify an organizational home, key team members, and stakeholders necessary for initial success. MVP operating model:  The MVP FinOps approach necessitates selectively prioritizing critical capabilities in building an early-stage FinOps practice. This includes visibility, cost allocation, and tagging strategy to ensure accountability. Other aspects like cloud usage optimization or chargeback & finance integration can be addressed passively. Adapt the inform, optimize, and operate lifecycle phases for simplicity and agility. MVP KPIs and tools:  The MVP approach also endeavors to simplify the measurement of FinOps efficiency into its most important metrics, enabling you to assess the current impact of your FinOps efforts at the macro level to deliver immediate insights. We’ll identify initial KPIs for measuring FinOps efficiency and discuss tooling considerations.  With Andoot’s MVP FinOps implementation model, you’ll be able to: Integrate FinOps values and culture throughout the organization without holding back your engineers. Lay the foundations for a dedicated FinOps team with a cross-functional working group to drive FinOps. Establish good cost allocation that enables tracking, reporting, and forecasting spend by cost center or business unit. Identify opportunities to spend more effectively and prioritize high-value/low-effort rate optimizations that can be transparent to engineering teams.  Avoid painful billing surprises by identifying irregularities in cloud use and spending with automated anomaly detection. Define the right unit economic metrics for your organization and measure FinOps efficiency with six additional KPIs. Leverage FinOps tools as force multipliers and build processes to support your FinOps goals. Want to learn more about Anodot's MVP FinOps approach and how to implement it? Download the Anodot white paper: "Adopting an MVP FinOps approach." [CTA id="3639b338-9c7f-4fb5-b5a2-226de67b8e42"][/CTA]   FYI: Keep an eye out for part 2, where we'll dive into the important components for achieving FinOps success and prioritizing maturity efforts based on your company's needs! Drive FinOps success with Anodot FinOps platforms are force multipliers that can help you establish and mature key FinOps capabilities more quickly. Anodot is the only FinOps platform purpose-built to measure and drive success in cloud financial management, giving organizations complete visibility into KPIs and baselines, advanced reporting capabilities, and savings recommendations to help control cloud waste and improve cloud unit economics. With Anodot, anyone can understand the true cost of their cloud resources, find ways to reduce cloud costs with advanced recommendations and make data-driven decisions to get the most out of their cloud investments with easy-to-use explanations. FinOps practitioners rely on Anodot to support their organizations' FinOps journeys to maximize the value of the cloud and establish a culture of cost awareness. Learn more at anodot.com/cloud-cost-management/, or contact us to start a conversation.

Amazon RDS or Relational Database Service is a collection of managed services offered by Amazon Web Services that simplify the processing of setting up, operating, and scaling relational databases on the AWS cloud. It is a fully managed service that provides highly scalable, cost-effective, and efficient database deployment. Features of AWS RDS Some features of Amazon Relational Database Service are: Fully Managed: Amazon RDS automates all the database operational tasks such as database setup, resource provisioning, automated backups, etc. Thus freeing up time for your development team to focus on product development. High Availability: Amazon RDS provides options for multi-region deployments, failover support, fault tolerance and read replicas for better performance. Security: RDS supports the functionality of data encryption in transit and at rest. It runs your database instances in a Virtual Private Cloud (VPC) based on AWS’s state-of-the-art VPC service. Scalability: Amazon RDS supports both vertical and horizontal scaling. Vertical scaling is suitable if you can’t change your application and database connectivity configuration. Horizontal scaling increases performance by extending the database operations to additional nodes. You can choose this option if you need to scale beyond the capacity of a single DB instance. Supports Multiple Database Engines: AWS RDS supports various popular database engines — Amazon Aurora with MySQL compatibility, Amazon Aurora with PostgreSQL compatibility, MySQL, MariaDB, PostgreSQL, Oracle, and SQL Server — and deploy on-premises with Amazon RDS on AWS Outposts. Backup and Restoration: Amazon RDS provides automatic backup and restoration capabilities and supports emergency database rollback. Monitoring Capabilities: AWS RDS provides seamless integration with AWS Cloud Watch which provides state-of-the-art monitoring and analysis capabilities. Managed database (RDS) vs. database self-management: When to choose which approach? Deciding between using a managed database or managing a database yourself hinges on several considerations, including infrastructure needs, budget, time, and expertise of your development team. At first glance, it might seem that a self-managed database is the most cost-effective way to go, but in the majority of cases, it is not true. It takes a huge amount of time and manpower to manage a scalable database that is truly cost-effective and efficient. Therefore it is often wise to let professionals from companies like Anodot do it for you. Anodot provides managed RDS service which is highly scalable and cost-effective. Moreover, Anodot's cost-saving recommendations cover RDS. Thus your development team can focus on product development rather than spending a massive amount of time managing the databases.     Both Managed and Self-managed databases have their pros and cons and the decision should be based on them: Pros of using Managed RDS Fully Managed: A managed RDS is a fully managed service that is very easy to operate and use. Monitoring and Analysis: Managed RDS comes with native built-in monitoring and analysis tools such as AWS Cloud Watch. These tools help derive useful insights from the system that can be used to improve the performance further. Scalability: A managed RDS instance provides vertical and horizontal scaling capabilities that can be invoked automatically or manually as per our requirements. High Availability: Managed RDS provide Multi-Availability Zones (multi-AZ) deployments across regions where the database instance are replicated across availability zones. This provides better fault tolerance and performance.  Native Integrations: A managed RDS instance provides native integrations with other useful tools and services provided by AWS. Backup and Storage: Automated data backups, storage, and restoration facilities are provided. Cons of using Managed RDS Configuration Restrictions: A fully managed RDS is not completely customizable and has many restrictions. Cost: A managed RDS is often more expensive than a self-hosted database, especially when the database size and number of instances grows. That’s why often it is a good idea to let domain experts specializing in native tools from companies like Andot handle the database management for you. Vendor Lock-In: Managed RDS has vendor lock-in i.e. migrating from such a database to another database is often very complicated and costly, as you are charged based on the usage. [CTA id="89ea4e30-a9b9-468c-959d-cc70c06293e3"][/CTA] Pros of using Self-Managed Databases No Configuration Restrictions: A self-managed database provides you full control of your database configurations. Setup and Version Control: Self-managed databases provide setup and version control flexibility. Cost-efficiency: Self-managed databases are often much more cost-effective than a managed RDS. No Vendor Lock-In: Self-managed databases have no vendor lock-in so it’s easier to migrate across databases and hosting providers. Cons of using Self-Managed Databases Scalability: In a self-managed database you have to handle all scalability operations such as sharding and replication on your own. Operational Overhead: Setting up data backups, firewalls, and security rules has to be done and managed by your dev team. Data Security: Each and every aspect of database security i.e. securing the database instances, setting up access control, and encryptions at different stages have to be set up and managed by you. Monitoring and Analytics: In a self-managed database you have to set up your own monitoring and analytics tools. Cost Overhead: If your database becomes too big and your development team doesn’t have enough experience managing such a vast amount of data you might need to spend a large amount of money on hiring more senior engineers. This increase in human capital expenses might end up costing you a large amount of money. To summarize, managed RDS should be used in the following scenarios: When you lack in-house expertise to manage a highly scalable database. When you want to reduce the operational overhead of your development team. When you need a database with good performance and high availability without doing too much manual intervention. When you want to avoid setting up custom monitoring and analytics tools and prefer the integrated tooling a managed database system comes with. Whereas, you should manage your database yourself in the following scenarios: When you have in-house expertise to manage databases at scale. When you want to reduce your database costs. When you need some custom database configurations that are not provided by a managed database provider. When you are willing to assign dedicated resources to set up, update, and maintain your database infrastructure.

Transform your approach to cloud cost management with thisAI-driven tool that delivers instant, actionable insights, simplifying complex pricing and identifying hidden expenses for more effective resource allocation.

Download this report to learn about the time of cost anomaly detection, realized cost savings and more. Learn what the top industry players and over 1000 Anodot customers are challenged by and how they optimize their cloud costs.

Managing cloud costs has become a must for FinOps-focused businesses. Gotta keep a close eye on those expenses! So, what is the best way to do it? Find a platform that can help you get cost visibility and catch any cloud costs anomalies before they turn into a money waste! With tons of FinOps tools, how do you figure out which one suits your needs? And what exactly should you be looking at? We get it! There’s much to consider when picking the best platform to get those cloud cost insights. Alright, let's dive into what makes Anodot stand out and check out the pros and cons of other FinOps tools. What makes Anodot the best FinOps tool?  First off, we’re a leading company specializing in real-time analytics and automated anomaly detection. Our AI platform detects and resolves issues preemptively, empowering businesses to optimize performance and make data-driven decisions. What makes us unique?  Focus: We've got you covered with support for AWS, Azure, and GCP. One tool to handle all your FinOps needs. Data: Data gets updated once the billing invoice is refreshed, and we keep at least 12 months of historical data stored. K8s: Visualize costs at different levels: namespace, cluster, node, pod stack, and by object labels. What makes our features similar to our competitors better? Visibility: Top-notch (according to up-to-date info), with multi-cloud capabilities and shared costs. Recommendations: Over 40 types of cost-reducing recommendations (over 60 types for AWS!) with remediation instructions through CLI and AWS Console.  API: Easy to use and operationalize (many customers consume our data through API). MSP Compatibility: Our MSP-ready solution has multitenancy, customer invoicing, and discount management rules.  That's what sets us apart! But hey, who else is out there in this space? Let's find out! FinOps Tool Alternatives CloudZero:  Background: A cloud platform offering solutions like cloud cost monitoring, optimization, and insight reporting for businesses. Headquarters: Boston, Massachusetts  Est. Employees: 100-250 Funding: $45M Data by Owler NetApp Spot CloudCheckr CMx:  Background: CloudCheckr is a cloud management platform offering cost optimization, activity monitoring, and compliance solutions for businesses. Headquarters: Rochester, New York Est. Employees: 100-250 Funding: $67.4M Data by Owler VMware CloudHealth:  Background: A cloud platform that offers solutions such as financial management and compliance for businesses. Headquarters: Boston, Massachusetts Est. Employees: 250-500 Funding: $85.8M Data by Owler What are the pros and cons of these FinOps tools?  CloudZero Pros Automation: CloudZero automates cost tracking across AWS, Azure, and Google Cloud. Cross-account support: Can analyze costs for individual accounts or across all accounts in one view Cons Limited feature set: CloudZero doesn't offer as many features as the other services, such as forecasting and budgeting capabilities. Area of specialization: Exclusively AWS, K8s, and Snowflake Looking for a tool that also specializes in MSP support? [CTA id="870a5fee-2fb6-4bd3-96bf-28b441372e04"][/CTA]   NetApp Spot CloudCheckr CMx Pros Spot, a highly regarded and unique solution, is becoming increasingly integrated.  Can control cloud costs in tandem with usage and performance. Cons Customers may be frustrated with the pre-NetApp version of the solution Limited scope: It is focused mainly on cost optimization rather than broader cloud management activities. VMware CloudHealth Pros Best offering for VMware-based public clouds and organizations transitioning to cloud from on-prem VMware. Provides a comprehensive view of the “cloud economic model” that allows users to understand their cloud resources and optimize costs. Cons The API presents multiple, fragmented, and restricted aspects. Customer success and services often come with additional, undisclosed costs. Final Thoughts on Our FinOps Tools Competitive Analysis So, what did we learn? The cloud costs management platform battlefield has some serious competition going on! These six platforms help their customers get some visibility and understand their cloud costs in a way that wouldn't be possible without them. BUT  Anodot cost-effectively does all of this, with user-friendly APIs and support on multiple cloud computing platforms. We're the game-changer that elevates your cloud cost monitoring to a new level. So even though the battlefield is fierce, there’s only one victor, and that’s us! Learn more.

The gaming industry has always been a highly lucrative and adored field. According to online gaming industry statistics, it is projected to surpass $33.77 billion by 2026. However, a downside emerges when governments impose substantial taxes on the income generated from gaming. It's happening now. The Indian government has decided to impose a 28% tax on online gaming, which may lead to a funding shortage and a decrease in investor confidence. Undoubtedly, many gaming companies will look for new strategies to save costs. Business monitoring is a powerful strategy that reduces costs, maintains performance, and enhances user experience. Let's explore the power of business monitoring, its benefits for the gaming industry, and Anodot's prominent role in this service. Enhancing Savings Identifying problems early is crucial for businesses, especially in the gaming industry. Spotting potential bugs is essential for a great user experience and saves time on resolution. Anodot's Business Monitoring and Anomaly Detection platform offers valuable solutions for identifying and preventing costly abnormalities in gaming operations. Here's how: Early Detection: Anodot helps online, and mobile gaming companies spot and troubleshoot game-specific problems early on. By keeping an eye on real-time metrics and data, it catches any unusual behavior that could affect player experience or revenue. (Check out this story.) [CTA id="b018de92-ed32-441f-bb60-5488b5b08c64"][/CTA]   Real-time Alerts and Forecasts: Anodot's autonomous monitoring solution provides real-time alerts and forecasts for revenue-critical business incidents. This allows gaming companies to proactively address potential problems before they escalate, enhancing operational efficiency. Cost Anomaly Detection: Anodot's machine learning can also keep an eye out for any unexpected cost changes. This way, gaming companies can better manage their expenses and find ways to save some cash. Enhanced Player Experience: Timely detection of abnormalities can lead to quicker resolution, reduced downtime, and improved customer satisfaction so your players can keep gaming without interruptions! Unexpected things can happen in your gaming operations that are beyond your control. But hey, you can still handle the abnormalities by using ML analytics. Our insights can help you save money to keep making your game the best it can be! Improving User Experience Your users are the key to your gaming success. When they have a blast playing your game, they'll keep returning for more and recommend it to other gamers! Unfortunately, the same thing can happen if your users aren't happy with your game. And when dealing with those new taxes, you'll want to keep your existing players and attract new ones consistently. Here's how Anodot can help: Proactive incident management: By promptly detecting anomalies, Anodot enables gaming companies to address issues that may negatively affect the user experience, minimizing downtime and maximizing player satisfaction. Comprehensive anomaly grouping: Anodot's platform can group anomalies across different silos, allowing businesses to quickly identify and address incidents that impact user experience, ensuring a seamless gaming experience for players. Optimized decision-making: With real-time business monitoring and anomaly detection, gaming companies can make informed decisions to optimize player experience and avoid potential losses. Enhanced user retention and brand reputation: By effectively detecting and addressing anomalies that impact user experience, Anodot's solution helps gaming companies retain players, boost player satisfaction, and maintain a positive brand reputation, contributing to long-term success in the competitive gaming industry. It only takes one bug or glitch to get players turned off from your game. To maintain a healthy user experience, staying alert for possible anomalies is imperative. Of course, getting a partner can help this process stay easy and automated! Optimizing Performance Keeping a close eye on and quickly resolving anomalies is important for top-notch performance in the gaming industry. So, how exactly does anomaly detection contribute to better gaming performance? Immediate Issue Detection: Proactive monitoring detects anomalies that may affect gameplay performance. Real-time tools and analytics help gaming companies identify issues like server latency, network congestion, or hardware failures early on. This allows swift action to address problems promptly. Enhanced Performance Optimization: Identifying anomalies offers valuable insights into game performance metrics. Real-time analysis enables gaming companies to identify bottlenecks, optimize server capacity, fine-tune game mechanics, and improve load balancing. These optimizations lead to smoother gameplay, reduced lag, and improved performance. Competitive Advantage: In the gaming industry, high-performance gameplay sets a company apart. Resolution of anomalies enables gaming companies to deliver superior gameplay experiences. By consistently providing high performance, companies can gain a competitive edge, attracting and retaining more players. Final Thoughts As of 2023, there are 3.220 billion gamers worldwide. The expansive market encompasses various demographics and can be very profitable for gaming companies. However, new industry regulations may emerge, impacting operations and triggering a chain reaction in how issues are addressed and resolved. This is why business monitoring is incredibly powerful. It effortlessly anticipates errors before they escalate into problems, offering remarkable benefits such as cost savings, enhanced user experience, and improved performance in the gaming industry. With Anodot's AI-powered business monitoring and anomaly detection, you can effortlessly tackle errors before they occur. So, no matter what new taxes come your way, rest assured that you're already cutting costs with Anodot. Let's talk! 

AWS DynamoDB is a fully managed NoSQL database provided by Amazon Web Services. It is a fast and flexible database service which has been built for scale. What are the features of DynamoDB? Some features of DynamoDB are: Flexible Schema: DynamoDB is a NoSQL database. It provides a flexible schema which supports both document and key-value data models. Therefore each row can have any number of columns at any point in time. Scalability: Amazon DynamoDB is highly scalable. It has impeccable horizontal scaling capabilities that can handle more than 10 trillion requests per day. Performance: DynamoDB provides high throughput and low latency performance, which results in a millisecond response time for database operations and can manage up to 20 million requests per second. Security: DynamoDB encrypts the data at rest and supports encryption in transit. Its encryption capabilities along with the IAM capabilities of AWS provide state-of-the-art security. Availability: AWS DynamoDB provides guaranteed reliability and industry-standard availability with a Service Level Agreement of 99.999% availability. Backup and Restoration: DynamoDB provides automatic backup and restoration capabilities and supports emergency database rollback. Cost Optimization: Amazon DynamoDB is a fully managed database that scales up and down automatically depending on your requirements. Integration with AWS Ecosystem: AWS DynamoDB provides seamless integration with other AWS services that can be used for data analytics, extracting insights and monitoring the system.  DynamoDB — Best Practices to maximize scale and performance Provisioned Capacity: Increase the floor of your autoscaling provision capacity beforehand to what you expect your peak traffic would be in scenarios where you are expecting a huge surge of traffic such as during the Black Friday Sale, Prime Day or Super Bowl. You can drop it down to the normal provision capacity when the high-traffic event is over. This ensures that the burst bucket capacity and adaptive scaling kick in and everything runs smoothly even with a massive surge in traffic.  Availability: If you want 5 nines availability (99.999%) in DynamoDB then enable Global Tables in your DynamoDB service which provides you with multi-region data replication. Providing 5 nines availability in this scenario is an SLA guarantee from AWS. A single region DynamoDB setup only provides 4 nines availability. Handling Aggregation Queries: Aggregation queries are complicated to deal with in a NoSQL scenario. DynamoDB streams can be used in sync with Lambda functions to compute this data in advance and write to an item in a table. This preserves valuable resources and the user is able to retrieve data instantly. This method can be used for all types of data change events like writes, updates, deletes etc. The data change event hits a DynamoDB stream which in turn triggers a Lambda function that computes the result. Serverless Computing Lambda Execution Timing: DynamoDB works along with AWS’s native Lambda functions to provide a serverless infrastructure. However, we need to keep in mind that the iterator age in a Lambda function is relatively low and manageable. If it is increasing, it should be in bursts and not via a steady increasing activity, if the Lambda function is too heavy and the work being done inside it is very time-consuming, it will result in your Lambda function falling behind your DynamoDB streams. This will cause the database to run out of stream buffer which eventually results in data loss at the edge of the streams. Policy Management: DynamoDB works in sync with AWS Identity and Access Management IAM functionality that provides you with fine-grained control over your access management. Typically the Principleof Least Privilege should be used — which states that a user or entity should only have access to the specific data, resources and applications that are needed to complete the required task. Fine-grained data scan policies can also be set in DynamoDB that control the database querying capabilities of an individual, thus creating a scenario where a user who should not have access to some data would not be able to extract it from the database. Global Secondary Indexes: GSIs can be used for cost optimization in scenarios where an application needs to perform many queries using a variety of different attributes as query criteria. Queries can be issued against these secondary indexes instead of running a full table scan. This approach results in drastic cost reduction. Provisioned throughput considerations for GSIs: In order to avoid potential throttling, the provisioned write capacity for a GSI should be equal to or greater than the write capacity of the base table. This is due to the fact that updates in the database would need to be written in both the base table and the Global Secondary Index. Provisioned Capacity with Auto Scaling: Generally, you should use provisioned when you have the bandwidth to understand your traffic patterns and are comfortable changing the capacity via the API. Auto Scaling should only be used in the following scenarios: When the traffic is predictable and steady When you can slowly ramp up batch/bulk loading jobs When pre-defined jobs can be scheduled where capacity can be pre-owned. Using DynamoDB Accelerator (DAX): Amazon DAX is a fully managed, high-availability cache for Amazon DynamoDB which provides 10x performance improvement. DAX should be used in scenarios where you need low-latency reads. For instance, DAX can provide performance improvement from milliseconds to microseconds even in a system that is processing millions of requests per second. Increasing Throughput: Implement read and write sharding for situations where you need to increase your throughput. The process of sharding involves splitting your data into multiple partitions and distributing the workload across them. Sharding is a very common and highly effective DBMS functionality. Batching: In scenarios where it is possible to read or write multiple items at once consider using batch operations as it significantly reduces the number of requests made to the database thus optimizing the cost and performance. DynamoDB provides BatchWriteItem and BatchGetItem operations for implementing this strategy. Monitoring and Optimization: It is a good practice to monitor and analyze your DynamoDB metrics. By doing this you can understand the performance of the system better and identify performance bottlenecks and optimize them. AWS DynamoDB provides seamless integration with AWS Cloud Watch, which is a monitoring and management system for AWS resources. Using this approach you can periodically optimize your queries by leveraging efficient access patterns. Monitoring the cost of DynamoDB is very important as it can directly impact your organization’s cloud budget. This is essential in order to esure that you are staying within the budget constraints and all the cost spikes are kept in check. Anodot’s Cloud Cost Management capabilities can help you to effectively monitor the cost of your DynamoDB instances. Anodot provides you full visibility into your cloud environment which helps in visualization, optimization and monitoring your DynamoDB usage. The tools provided by Anodot help in ensuring that the DynamoDB instances are not idle and both your allocation and usage are in sync. Periodic Schema Optimization: Periodically the database schema should be reviewed and optimized. The required access patterns for an application change over time and to maintain the efficiency of the system you should optimize your schema and access patterns, this includes — restructuring database tables, modifying indexes etc. System diagram of DynamoDB being used in a serverless setup with AWS Lambda, Amplify and Cognito.