6 Kubernetes Pain Points and How to Solve Them - Part 1
March 05, 2018

Kamesh Pemmaraju
ZeroStack

Companies want to implement modern applications that can be used anytime, anywhere by always-connected users who demand instant access and improved services. Developing and deploying such applications requires development teams to move fast and deploy software efficiently, while IT teams have to keep pace and also learn to operate at large scale.

While the concept has been around for a couple of decades, containers staged a comeback in the last 3-4 years because they are ideally suited for the new world of massively scalable cloud-native applications. Containers are extremely lightweight, start much faster (than VMs), and use a fraction of the memory compared to booting an entire operating system. More importantly, they enable applications to be abstracted from the environment in which they actually run. Containerization provides a clean separation of concerns, as developers focus on their application logic and dependencies while IT operations teams can focus on deployment and management without bothering with application details.

Deploying and managing containers is still a significant challenge, however. In the past couple of years, Kubernetes burst onto the scene and became the de facto leader as the open-source container orchestrator for deploying and managing containers at scale. The hype has reached such a peak now that there are as many as 30 Kubernetes distribution vendors and over 20 Container-as-a-Service companies out there. All the major public clouds (AWS, Azure, and Google Cloud) provide Container-as-a-Service based on Kubernetes.

With more than 30 Kubernetes solutions in the marketplace, it's tempting to think Kubernetes and the vendor ecosystem has solved the problem of operationalizing containers at scale. Far from it. There are six major pain points that companies experience when they try to deploy and run Kubernetes in their complex environments, and there are also some best practices companies can use to address those pain points.

Pain Point 1 - Enterprises have diverse infrastructures

Bringing up a single Kubernetes cluster on a homogenous infrastructure is relatively easy with the current solutions in the market. But the reality is that organizations have diverse infrastructures using different server, storage, and networking vendors. In this situation, automating infrastructure deployment, setting up, configuring, and upgrading Kubernetes to work consistently is not easy.

One way to address this challenge is to deploy a unifying platform that abstracts the diversity of underlying infrastructure (physical server, storage, and networking) and offers standard open API access to infrastructure resources. This greatly simplifies the IT burden when it comes to provisioning Kubernetes.

Pain Point 2 - One Kubernetes cluster doesn't address all needs

Organizations have diverse applications teams, application portfolios, and sometimes conflicting user requirements. One Kubernetes cluster is not going to meet all of those needs. Companies will need to deploy multiple, independent Kubernetes clusters with possibly different underlying CPU, memory, and storage footprints. If deploying one cluster on diverse hardware is hard enough, doing so with multiple clusters is going to be a nightmare!

To address this pain point, the IT team should be able to set up logical business units that can be assigned to different application teams. This way, each application team gets full self-service capability within quota limits imposed by the IT team, and each team can automatically deploy its own Kubernetes cluster with a few clicks, independently of other teams.

Read 6 Kubernetes Pain Points and How to Solve Them - Part 2

Kamesh Pemmaraju is VP of Product at ZeroStack
Share this

Industry News

June 16, 2025

Operant AI announced the launch of MCP Gateway, an expansion of its flagship AI Gatekeeper™ platform, that delivers comprehensive security for Model Context Protocol (MCP) applications.

June 12, 2025

Oracle has expanded its collaboration with NVIDIA to help customers streamline the development and deployment of production-ready AI, develop and run next-generation reasoning models and AI agents, and access the computing resources needed to further accelerate AI innovation.

June 12, 2025

Datadog launched its Internal Developer Portal (IDP) built on live observability data.

June 12, 2025

Azul and Chainguard announced a strategic partnership that will unite Azul’s commercial support and curated OpenJDK distributions with Chainguard’s Linux distro, software factory and container images.

June 11, 2025

SmartBear launched Reflect Mobile featuring HaloAI, expanding its no-code, GenAI-powered test automation platform to include native mobile apps.

June 11, 2025

ArmorCode announced the launch of AI Code Insights.

June 11, 2025

Codiac announced the release of Codiac 2.5, a major update to its unified automation platform for container orchestration and Kubernetes management.

June 10, 2025

Harness Internal Developer Portal (IDP) is releasing major upgrades and new features built to address challenges developers face daily, ultimately giving them more time back for innovation.

June 10, 2025

Azul announced an enhancement to Azul Intelligence Cloud, a breakthrough capability in Azul Vulnerability Detection that brings precision to detection of Java application security vulnerabilities.

June 10, 2025

ZEST Security announced its strategic integration with Upwind, giving DevOps and Security teams real-time, runtime powered cloud visibility combined with intelligent, Agentic AI-driven remediation.

June 09, 2025

Google announced an upgraded preview of Gemini 2.5 Pro, its most intelligent model yet.

June 09, 2025

iTmethods and Coder have partnered to bring enterprises a new way to deploy secure, high-performance and AI-ready Cloud Development Environments (CDEs).

June 09, 2025

Gearset announced the expansion of its new Observability functionality to include Flow and Apex error monitoring.

June 05, 2025

Postman announced new capabilities that make it dramatically easier to design, test, deploy, and monitor AI agents and the APIs they rely on.