Kubernetes, the container orchestration system for automating application deployment, began life as an internal project at Google before being open-sourced in 2014. In the years since, it has grown into one of the largest and most active open source projects in the world, becoming the de facto standard for managing containerised workloads across cloud, on-premises, and edge environments.
Its dominance is not accidental. Kubernetes offers a combination of scalability, portability, and extensibility that few platforms can match. Its ability to deploy and manage containerised applications at scale has unlocked innovation in AI, IoT, cloud computing, and edge infrastructure. This article examines how Kubernetes achieved that position and where it is headed.
The Power of an Open Source Community
One of the most significant factors behind Kubernetes’ rise is its open source development community. Unlike proprietary platforms, Kubernetes evolves through continuous collaboration among engineers, vendors, and independent contributors worldwide. This model accelerates the adoption of new technologies and ensures that improvements are driven by real-world operational needs rather than a single vendor’s roadmap.
The community’s size and diversity mean that problems are identified and resolved quickly. Engineers from competing organisations routinely collaborate on shared infrastructure challenges — an unusual dynamic that benefits everyone building on the platform.
Security is a concrete example of the community model working well. The Kubernetes Security Audit Working Group was established to conduct systematic reviews of the platform’s security posture and publish findings openly. This kind of structured, community-led security effort is rare in enterprise software and reflects the maturity of the Kubernetes ecosystem.
Kubernetes and AI: A Natural Partnership
Artificial intelligence workloads present unique infrastructure challenges. AI and machine learning pipelines typically involve large datasets, multiple processing stages, heterogeneous hardware (including GPUs), and models that must move cleanly from development through testing into production. Managing this complexity manually does not scale.
Containers address many of these challenges directly. They provide a portable, reproducible environment for AI workloads, making it straightforward to move a training job between a developer’s workstation, a test cluster, and a production GPU farm without re-engineering the application. Large monolithic AI applications can be decomposed into targeted microservices, each independently scalable and maintainable.
Kubernetes is well suited to orchestrating these containerised AI workloads at scale. Key capabilities include:
- Horizontal scaling: Kubernetes can scale training jobs across many commodity servers, distributing compute load efficiently even for deep learning models with large parameter counts.
- Resource management: GPU and CPU resource requests can be declared explicitly, ensuring workloads are scheduled on appropriate hardware.
- Pipeline portability: The same Kubernetes manifests can describe workloads running in a data centre, a public cloud, or an edge node, giving teams deployment flexibility.
- Fault tolerance: Kubernetes automatically reschedules failed pods, which is critical for long-running training jobs.
Kubeflow: ML Pipelines Built for Kubernetes
Kubeflow is an open source machine learning platform built specifically for Kubernetes, launched in 2017. Its goal is to make deploying ML workflows on Kubernetes straightforward, portable, and scalable. Kubeflow addresses common pain points in ML engineering: manual pipeline construction, fragmented tooling from multiple vendors, and the difficulty of promoting models from development to production without significant re-architecture.
By standardising ML workflows as Kubernetes-native resources, Kubeflow allows teams to iterate faster and maintain cleaner separation between experimentation and production serving — a challenge that has historically consumed significant engineering effort in AI projects.
Containers, 5G, and IoT
Containers are increasingly relevant to telecommunications and IoT infrastructure, two domains that are undergoing significant architectural change. Both 5G network functions and IoT edge deployments share characteristics that make containers an attractive deployment model:
- Fast deployment cycles: Network functions that once ran on dedicated hardware are being virtualised and containerised, enabling faster rollout of updates and new services.
- Elastic scaling: Traffic demand on both 5G networks and IoT platforms varies dramatically. Containers allow capacity to scale up and down in response to load without over-provisioning static infrastructure.
- Portability: A containerised network function or IoT gateway application can run consistently across different hardware vendors and operating environments, reducing lock-in.
For Kubernetes to fully serve 5G and IoT use cases, the platform has had to evolve. Telecom-grade workloads impose strict latency, reliability, and scheduling requirements that general-purpose Kubernetes configurations do not meet out of the box. The community has responded with projects and enhancements targeting these needs — including improved support for real-time scheduling, SR-IOV networking, and lightweight distributions suitable for edge nodes with constrained resources.
Why Kubernetes Has Sustained Its Lead
Many container orchestration platforms competed for adoption in the mid-2010s. Kubernetes won because of a combination of technical and organisational factors:
- Extensibility: The Kubernetes API is designed to be extended. Custom Resource Definitions (CRDs) and the Operator pattern allow the platform to manage almost any kind of stateful or stateless workload, not just generic containers.
- Vendor neutrality: Donated to the Cloud Native Computing Foundation (CNCF) in 2016, Kubernetes is governed by a broad coalition of contributors rather than a single company. This governance model has encouraged wide adoption across competing cloud providers.
- Ecosystem breadth: A rich ecosystem of complementary projects — for networking (CNI plugins), storage (CSI drivers), service mesh, observability, and security — means teams can assemble production-grade platforms from well-maintained building blocks.
- Operational consistency: Because every major public cloud provider offers managed Kubernetes, teams can develop skills and tooling that transfer across environments, reducing the cost of multi-cloud or hybrid strategies.
Looking Ahead
Kubernetes has moved from a novel orchestration tool to foundational infrastructure for modern software delivery. Its influence now extends well beyond web applications into AI, telecommunications, industrial IoT, and scientific computing. The qualities that enabled this expansion — a flexible API, a large and collaborative community, and a strong commitment to vendor neutrality — show no sign of diminishing.
For DevOps and platform engineering teams, understanding Kubernetes is no longer optional. It is the substrate on which an increasing proportion of production workloads run, and its ecosystem continues to evolve in response to the next generation of computing challenges.
This article draws on a piece originally written by Carmine Rimi of Canonical, published in 2019.