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This blog breaks down Amazon RDS cost optimization into actionable strategies: estimating costs, identifying inefficiencies, using monitoring tools, and avoiding common mistakes. You’ll learn how automation, especially with Sedai, can help reduce RDS spend by eliminating waste and right-sizing resources in real time.

This easy-to-follow guide walks you through AWS Cost Explorer, showing how to get a clear picture of your cloud expenses and spot where you can save. You'll learn how to track spending trends, set budgets, and use AWS tools to keep costs under control. Also, get practical tips for monitoring and optimizing your AWS bills so you're never caught off guard. It’s perfect for cloud architects, finance teams, and IT leaders who want to manage AWS costs smartly while growing their cloud infrastructure.

AWS EBS can be very helpful for your cloud, but it needs to be managed properly. Otherwise, it can cause volumes to bloat your bill, increase GP3 credits that vanish mid-query, and cause IO2 volumes to cost more than your dev team's coffee habit. This guide will help choosing the right volume type (without the trial-and-error), automating snapshot lifecycles before they bankrupt you, and fixing performance issues before users notice.

Sedai works alongside Datadog, using AI to automate cost savings without any manual setup. By learning your unique environment, Sedai takes smart actions autonomously, keeping your observability lean and efficient.

Most teams shouldn't waste time managing EMR clusters. Sedai automates rightsizing, scaling, and killing idle jobs, allowing engineers to focus on what matters, reduce EMR costs, and streamline operations.

For cloud migrations, Engineers need a fast, reliable, and automated solution. AWS Database Migration Service ensures seamless, secure transfers with minimal downtime while maintaining data integrity

Cloud storage should be simple and affordable. Azure Blob Storage gives engineers secure, reliable storage that scales with every project. Sedai’s AI automation keeps costs low and performance high. Together, they help you to take control of your data efficiently and confidently.

Engineers shouldn’t have to spend hours optimizing the performance of each software application, and then constantly monitoring these applications to adjust for changes and fix issues. These engineers need to integrate performance optimization across the DevOps lifecycle, and then use AI tools to adapt to changes and fix issues, so they can focus on what matters.

Explore the top cloud storage options for 2025 and see how Sedai’s AI boosts performance and cuts costs automatically.

Engineers can stop manually rotating credentials with AWS Secrets Manager, which securely stores and auto-rotates secrets. Sedai, the cloud's AI brain, optimizes settings, scales resources, and reduces costs automatically for faster apps and lower cloud expenses.

Managing cloud data shouldn’t be hard or expensive. Snowflake’s smart architecture and pay-as-you-go model help engineers scale easily while keeping costs under control. Paired with Sedai’s AI automation, they gain real-time cost insights, optimized resources, and smoother teamwork. Simple, efficient, and fast.

Engineers shouldn’t have to invest countless hours configuring and managing infrastructure when starting with cloud databases. With Amazon RDS, they can integrate database management seamlessly into their workflow. RDS handles automation and reduces the need for manual adjustments, so engineers can stay.

Meet Sedai's new Chief Revenue Officer, Vaneet Bhaskar.

Discover essential steps to optimize Azure VM performance, from right-sizing and storage choices to networking enhancements. Learn how Sedai’s autonomous cloud optimization platform streamlines these tasks, delivering continuous, proactive optimization to ensure efficient, reliable VM performance without manual intervention.

Explore the critical aspects of high availability (HA) in cloud environments and discover strategies to minimize downtime and enhance performance. Learn how to implement redundancy, proactive monitoring, and failover mechanisms, while leveraging AI-driven solutions like Sedai to optimize resource allocation and ensure uninterrupted service.

Managing AWS EMR costs effectively is crucial for optimizing cloud expenses while maintaining performance. This article explores key strategies to reduce EMR cluster costs, including right-sizing instances, leveraging spot instances, enabling auto-scaling, and optimizing data storage. By implementing these best practices, businesses can significantly lower their cloud computing expenses while ensuring efficient big data processing.

Choosing the right cloud optimization tool is key to managing costs and performance. This guide covers cost vs. architectural optimization, TCO, automation, and security. It also highlights Sedai’s AI-powered automation for minimizing cloud waste and improving efficiency across AWS, Azure, and Google Cloud.

This guide compares AWS, Azure, and GCP savings options to help businesses optimize cloud costs. Discover the benefits of AWS's flexible plans, GCP's no-upfront savings, and Azure’s structured reservations, and learn how AI-driven tools like Sedai’s autonomous optimization ensure continuous, cost-effective cloud management.

Choosing the right virtual machine (VM) provider is crucial for businesses looking to optimize performance, scalability, and cost-effectiveness in the cloud. This article compares AWS EC2, Azure Virtual Machines, and Google Compute Engine (GCE), breaking down their features, pricing models, and use cases. We explore key aspects like instance types, performance, networking, security, and cost structures to help you determine which cloud provider best suits your needs.

Discover strategies to manage cloud computing costs across AWS, Azure, and Google Cloud in 2025. This article offers an in-depth comparison of pricing models, hidden fees, and key cost components such as computing, storage, and networking. Learn how to calculate total cloud expenses and make informed decisions based on your infrastructure needs and cloud provider. Additionally, uncover best practices for optimizing cloud resources through techniques like right-sizing, autoscaling, and leveraging discounts to achieve cost-efficiency across your cloud environments.

AWS Spot Instances can help businesses cut EC2 costs by up to 90%, but their unpredictability makes them challenging to manage. With Sedai’s AI-powered automation, you can seamlessly optimize Spot Instance usage, prevent service disruptions, and maximize cloud savings—without manual effort. Sedai dynamically shifts workloads between Spot, On-Demand, and Reserved Instances, ensuring high availability and cost efficiency.

Google Cloud Dataflow and AWS Kinesis Data Analytics are two leading solutions for real-time data processing, each catering to different needs. Dataflow offers a unified model for batch and stream processing using Apache Beam, making it ideal for complex workflows. Kinesis Data Analytics, on the other hand, provides low-latency stream processing with deep AWS integration. This blog compares their architecture, scalability, cost, security, and use cases to help you choose the best fit for your data pipeline. Plus, discover how AI-driven optimization with Sedai can enhance performance and cost efficiency.

Discover key strategies to optimize Amazon EKS costs, from resource right-sizing and autoscaling to efficient instance selection. Understand how Sedai’s autonomous platform drives cost-effective management by continuously monitoring and adjusting workloads in real time, reducing the need for manual intervention. Learn how proactive optimization through tools like Sedai can help you control expenses across EKS components—like EC2 instances, storage, data transfer, and load balancing—ensuring a scalable, high-performing Kubernetes environment on AWS that aligns with your budget.

Sedai has been recognized as both a Leader and Outperformer in the 2025 GigaOm Radar for Kubernetes Resource Management. Discover how our AI-powered platform delivers 15-50% cloud cost reduction while improving application performance by up to 30% for enterprises like Palo Alto Networks, Experian, and HP.