Microsoft Azure offers over 150 cloud products and services. In addition to Infrastructure-as-a-Service (IaaS), Software-as-a-Service (SaaS), and Platform-as-a-Service (PaaS), Azure Cloud also supports multiple use cases. Yet, Azure can be complex, expensive, and a lot to figure out — let alone optimize for your specific cloud computing needs. Maybe you’ve looked into the cloud service provider.

The promise of cloud computing has always been about flexibility and cost-effectiveness. Yet many organizations find themselves trapped in a cycle of unpredictable costs and underutilized/overutilized resources. The culprit? A lack of understanding about the power of scaling within platforms like Amazon’s Elastic Kubernetes Service (EKS).

Cost optimization in cloud environments is no longer a luxury — it’s a necessity. As businesses increasingly migrate to AWS, they are met with the difficult task of ensuring robust performance and scalability, all while keeping an eagle eye on costs. How does one achieve this delicate balance? How can organizations ensure that their AWS architectures are both high-performing and cost-efficient?

As an engineer or CTO, you already know that monitoring your tech stack is essential for maintaining optimal performance, security, and health. The thing is, building and hosting an observability platform for your full stack’s logs, metrics, and more is quite time-consuming and can be costly. One of the top contenders if you are seeking a SaaS-based monitoring and observability tool is Datadog. Perhaps you already use Datadog but would like to switch to another observability platform.

Kubernetes has revolutionized how we manage and scale containerized applications, the flip side of this robustness is often a rising cloud bill. As you navigate the complexities of cluster growth across teams and applications, cost management can become a genuine headache. Enter Komodor’s newly released Cost Optimization Suite. In this blog post, we’ll unpack how this feature-rich addition to the Komodor platform will empower you to optimize costs without sacrificing performance.

Amazon Elastic Kubernetes Service (EKS) has revolutionized the way organizations deploy, manage, and scale containerized applications using Kubernetes. However, optimizing costs on EKS infrastructure remains a challenge for many. Enter Karpenter, a Kubernetes-native node autoscaler designed to improve resource efficiency.

Essentially, cloud infrastructure consists of a set of virtual tools and resources that help deliver cloud-based services and products. For companies, cloud infrastructure liberates them from building their own physical data centers. Instead, they rent computing capacity on a need-by-need basis. But let’s not get ahead of ourselves. In this post, we’ll define cloud infrastructure, examine cloud computing types, delivery models, and more.

Cloud computing offers many advantages over on-premises environments, including scalability, flexibility, and cost-efficiency. Yet, simply using a “lift and shift” strategy — where you move your application as-is from an on-premise environment to the cloud with minimal, if any, modification — can lead to several issues (such as inefficient design, bloated costs, etc.).

Azure VM Reservations are the best solution for optimizing cloud expenses. Users can obtain discounted prices for virtual machines in Microsoft Azure by committing to a one- or three-year term. This strategic approach ensures predictable costs, enhances budget management, and is ideal for workloads with consistent resource requirements. Leveraging Azure VM Reservations empowers businesses to achieve significant cost savings while maintaining flexibility and scalability in the cloud infrastructure.

Cost optimization is critical to managing any cloud-based service, including Azure Cosmos DB. Azure Cosmos DB is a globally distributed, multi-model database service that allows you to scale your storage and throughput across regions. Although it offers a highly scalable and flexible platform for creating applications, Azure Cosmos DB cost optimization is important to ensure efficient resource utilization, avoid unforeseen expenses, and estimate costs more accurately.