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TechConnect, a renowned Managed Service Provider (MSP) and AWS Advanced Consulting Partner, recently faced a common yet challenging issue in cloud cost management – the need for better visibility into specific cloud costs. This became especially apparent after they transitioned to Amazon Connect, where the lack of detailed cost insights hindered effective cloud expense management. The Challenge One of TechConnect’s key struggles was the inability to break down Amazon Connect costs into detailed components, making it difficult for their clients to understand and control their spending.  They struggled to differentiate specific costs like inbound and outbound minute charges, DID costs, and customer profile expenses. The Solution TechConnect turned to Anodot for a solution. By adopting Anodot’s platform, they were able to provide their client with a detailed breakdown of Connect and Contact Centre Telecommunications costs. Anodot's customized dashboard offered a clearer understanding of these expenses and revealed critical cost components that were previously unclear.   The Impact  The adoption of Anodot’s solution was a game changer. It not only provided deeper insights into various cost elements but also helped uncover unexpected expenses. TechConnect's Operational Platforms Manager, Conor Mulvenna, highlighted the invaluable insights provided by Anodot's dashboard, enabling swift and effective responses to discrepancies. This case study demonstrates how Anodot's innovative approach to cloud cost management can transform the way companies like TechConnect manage their cloud expenses. With Anodot, organizations can gain deeper visibility into their cloud spending, optimize their resources, and make more informed financial decisions. Still need more proof about Anodot's cloud cost optimization excellence? Read the full case study here.  

Trax is the driving force of the store of the future. The world’s top consumer goods companies and retailers use the Trax cloud platform to gain the power to see what happens at shelf and the agility to delight shoppers in new ways. Armed with Trax data and insights, retailers gain granular, SKU-level visibility to changing store conditions. Trax is a global company with hubs in the United States, Singapore, China, France and Israel, serving customers in more than 90 countries worldwide. The Challenge   Trax operates a complex, multi-cloud platform that currently runs on both AWS and Google Cloud. The company has several accounts on each cloud. The environments themselves are complicated, with Kubernetes clusters and numerous microservices. There are a lot of moving parts that are not easily tracked. The cloud providers’ native tools fall short of Trax Retail’s needs to closely manage cloud costs at the workload level. The Solution   Anodot for Cloud Costs Management provides the ability to measure the different workloads that happen inside a single server in the cloud. This allows Trax to measure a critical KPI, the “cost per image processing,” which shows how effectively the Trax system is operating. Anodot Cloud delivers real-time alerts for unexpected changes in cloud usage and recommendations for cost savings. Key Use Case Drove Requirements for a Cloud Cost Monitoring Tool   A key function of the Trax platform is to take photos of store shelves, upload the images to the cloud, process them through various engines, and deliver information back to the customer. This gives rise to one of Trax Retail’s most important KPIs: the cost to process each of those images. This cost is calculated based on the time it takes to run various applications in the cloud, i.e., cloud usage. With large volumes of images processed every day, this system represents a large source of Trax’s cloud costs. Mark Serdze is Director of Cloud Infrastructure at Trax Retail. Serdze says that when they began looking for a cloud cost management tool, they were very focused on this particular use case. “This KPI gives us a good idea of how effectively our system operates,” he says. “When we moved into Kubernetes, because of the way this platform operates as a cluster with scaling up and down, we lost the ability to break down the billing through the native cloud tools. We used to invest a lot in tracking instances but we never had an ability to measure the different workloads that happen inside a single server in the cloud.” Serdze says this was the main motivation behind looking at Anodot Cloud. “Getting to the level of detail that we need is just part of what this tool offers. In the end, Anodot Cloud won our business,” says Serdze. “In addition to meeting our requirements really well, Anodot Cloud is a SaaS offering, so we didn’t need to install and maintain any kind of infrastructure within our cloud environment. Also, Anodot can keep up with our crazy scale usage patterns. We crashed the other tool with our large volume of images.” Read Full Case Study Here Cloud Cost Monitoring is Critical to the Business   Serdze calls cloud cost monitoring “super critical” to Trax Retail’s business. “One aspect of our business is to replace manual in-store labor at various retailers with our automated solution. Anodot Cloud is an AI-based automated solution. We constantly need to make sure that whatever algorithms we develop can be operated in the most efficient way, cost wise, so it is crucial for us to track them,” says Serdze.  Anodot Cloud allows Trax to monitor cloud costs in a very granular way. “Anodot can detect in real time the anomalies in different services and new deployments that, for example, affect costs of specific microservices,” according to Serdze. “In contrast, the native tools from the cloud providers don’t give as much visibility. We can monitor our services but only retroactively and not with the level of granularity that we want.” The Benefits Are Myriad   There are several teams within Trax Retail that are the main users of Anodot Cloud. The infrastructure team does the day-to-day tracking of cloud costs and resources. The data services team implements the API connections. “In general, Anodot Cloud allows us to be more responsive to trends because the data is more updated and live than what we were previously used to,” says Serdze. “In the past we would pull this data and aggregate it once per month. Now we are doing it continuously. Mostly it reduces the toll on our DevOps team and allows us to focus on optimizing our production environment rather than creating cost tracking systems.” Serdze says there is one key thing they can do with Anodot Cloud that they could not do before. “The most important thing is that we can connect between the usage patterns of our microservices inside the Kubernetes clusters and their actual costs, which is something that’s very hard to do in the native cloud tools.”

Razorpay, India's largest payment solution provider, enables frictionless transactions, revolutionizing money management for online businesses. Founded in 2014, Razorpay offers a fast, affordable, and secure way for merchants, schools, e-commerce, and other companies to accept and disburse payments online. The Challenge Solid anomaly detection is crucial for Razorpay, particularly when serving businesses in payment management. Sudden drops in success rate drops, ticket resolution delays, or fraudulent transactions can impact customer finances and decrease client satisfaction with Razorpay. Other issues Razorpay was facing: - Slow issue detection - Lack of real-time/near real-time alerts - Delayed critical alerts resulting in financial losses - Manual effort for anomaly and fraud detection - Challenges in tracking alerts across dimensions - Lengthy post-anomaly detection root cause analysis (RCA) The Solution Anodot was the partner Razorpay needed to address key issues like ticket resolution time and fraud detection. With a user-friendly UI for non-tech business users and ML forecasting capabilities, Razorpay can enhance the customer experience with automated monitoring and real-time anomaly detection. Main KPIs tracked in Anodot: - Payments SR in different business verticals - Customer success ticket creation and average time to close - Fraudulent transactions in different payment channels - Average payment checkout time - Refunds claimed as fraud Read Full Case Study Here  Anodot: Real-time alert and forecast platform using ML and AI for business monitoring   Real-time communication With Anodot, analytics and engineering teams can receive alerts across multiple channels, including Slack for seamless communication and collaboration for efficient monitoring and problem-solving.   Enhanced customer support  Anodot is open to building customer-requested features and provides a seamless onboarding experience to familiarize users with the tool quickly. Answering all questions and providing optimized, structured solutions.   Removal of manual anomaly detection Anodot's real-time alerts help reduce the financial impact on the company. Ops and analytics can spend less time fixing anomalies and more time on innovation and operational efficiency.   "Anodot is a valuable asset for sending timely alerts and notifications to the right recipients while facilitating quick and easy feedback."  Nishant Thakar BI and Data Strategy, Razorpay

Discover how Aqua Security transformed their cloud cost management with Anodot, achieving real-time visibility and rapid ROI while eliminating manual reporting and optimizing spend across their complex multi-cloud environment.