Traditional programming is built around well-defined inputs and deterministic logic. Developers write functions that take structured inputs, apply a predefined set of operations, and return a structured output. This model works well when the input space is predictable, but it struggles with problems where inputs are ambiguous, unstructured, or constantly evolving.

Things are changing. Technology differences between serverless and container-based systems are rapidly blurring. In 2020, Amazon Web Services (AWS) enabled AWS Lambda to package and deploy functions as container images instead of bundling all of a function’s code and dependencies in a.zip file. Today, more organizations are increasingly deploying Lambda functions as Docker container images. These companies want to reap the benefits of serverless computing, containers, and container orchestration.

The customer is the lifeblood of any company, and a good customer has the potential to make a huge impact on the direction and success of your business. Here at Cycle.io, we are no exception. We believe that when you come to Cycle, you are not only getting great tech but also receiving an excellent customer experience. The customers we work with every day are paramount in driving innovation and keeping us honest.

Have you ever needed to serve multiple customers with a single instance of your application but ran into challenges like isolating each customer's data and configuration? If you have, then you've encountered a multi-tenant architecture. In this blog post, we'll introduce the core concepts behind multi-tenancy and explore how it works. We'll then dive into how Kubernetes, the popular container orchestration platform, provides primitives that enable building multi-tenant architectures.

Monitoring and observability have become the backbone of reliable software systems. OpenTelemetry, a CNCF project, has gained immense traction as the go-to framework for collecting and exporting telemetry data. But what makes it even more powerful is its Collector—a vendor-agnostic tool that simplifies data processing. Combine that with Docker, and you’ve got a robust, portable, and scalable observability solution.

In modern application development, changes happen constantly: Deployments are pushed, feature flags are toggled, and Kubernetes events reshape infrastructure, to name just a few. While these practices drive innovation and scalability, they also introduce complexity—especially during incidents. Fragmented tools and workflows across teams and organizations make it difficult to pinpoint the root causes of issues, leading to longer resolution times.

The Kubernetes Monitoring Helm chart 2.0 is here, and it comes with some exciting changes to improve your experience collecting observability data. The Kubernetes Monitoring Helm chart makes it easy to start gathering telemetry data from your Kubernetes clusters. With one deployment, you can capture all of the metrics, logs, traces, and profiles from your cluster and the applications running on it!