With the rise of cloud computing, containerization, and microservices architecture, developers are adopting new approaches to building and deploying applications that are more scalable and resilient. Microservices architecture, in particular, has gained significant popularity due to its ability to break down monolithic applications into smaller, independent services.

Kubernetes is an excellent solution for building a flexible and scalable infrastructure to run dynamic workloads. However, as our cluster expands, we might face the inevitable situation of scaling and managing multiple clusters concurrently. This notion can introduce a lot of complexity for our day-to-day workload maintenance and adds difficulty to keep all our policies and services up to date in all environments.

At Codefresh, we are always happy to see companies and organizations as they adopt Argo CD and get all the benefits of GitOps. But as they grow we see a common pattern: It is at this point that organizations come to Codefresh and ask how we can help them scale out the Argo CD (and sometimes Argo Rollouts) initiative in the organization. After talking with them about the blockers, we almost always find the same root cause.

These are 10 strategies for reducing Kubernetes costs. We’ve split them into pre-deployment, post-deployment and ongoing cost optimisation techniques to help people at the beginning and middle of their cloud journeys, as well as those who have fully adopted cloud and are just looking for a few extra pointers.

In a previous blog post, we explained how containers’ CPU and memory requests can affect how they are scheduled. We also introduced some of the effects CPU and memory limits can have on applications, assuming that CPU limits were enforced by the Completely Fair Scheduler (CFS) quota. In this post, we are going to dive a bit deeper into CPU and share some general recommendations for specifying CPU requests and limits.

Tradeoff: a balance achieved between two desirable but incompatible features; a compromise. Schooling often promotes the idea that there is a right and wrong answer to questions… It does little to prepare us for how many times that there are multiple right answers and no definitive best path forward. In a time where we have unlimited information at our fingertips, you can throw a stone and hit a thousand people with an opinion.

Containers are a big deal today. Because containers are software units that contain all the code, runtime, and dependencies required to run a distributed application, they help engineers test and run apps on any device and platform without compatibility issues. Organizations can use containers to reduce engineering costs, speed up deployments, develop and test AI models, and automate more processes. You probably want those benefits as well.

This blog post by Grafana Labs Senior Software Engineer Milan Plžík was originally published on the Kubernetes.io blog on Nov. 16, 2023. There’s been quite a lot of posts suggesting that not using Kubernetes resource limits might be a fairly useful thing (for example, For the Love of God, Stop Using CPU Limits on Kubernetes or Kubernetes: Make your services faster by removing CPU limits ).

This is the final article of a three-part series. To start at the beginning, read Part 1: Benefiting from multi-cluster setups requires familiarity with common variations and Part 2: Exploring the facets of a multi-cluster observability strategy. As companies scale software production, they lean on Kubernetes as a crucial container orchestration platform for managing, deploying and ensuring software availability.

As cloud computing continues to grow in popularity, so does the need for accurate cloud cost forecasting. With cloud costs being variable and unpredictable, it can be difficult to know how much you’re going to spend each month, a problem that can lead to budget overruns and financial problems. This makes cloud cost forecasting a necessary step in owning and managing your cloud efficiently. In simple terms, cloud cost forecasting is the process of predicting future cloud costs.