Deciding between OpenShift and Kubernetes for your container orchestration needs? As a long-time IT architect focusing on cloud infrastructure, let me provide you an expert yet friendly overview of the critical factors you‘ll want to consider when choosing between these industry-leading platforms.
I‘ve helped dozens of enterprises navigate this decision – in many cases migrating from their initial Kubernetes implementations over to OpenShift. This comprehensive guide will hopefully spare you similar detours!
By clearly mapping out the background, use cases, capabilities and limitations across both options below, you should gain clarity on the ideal platform matching your particular workloads.
Let‘s dive in!
At a Glance Comparison
Before analyzing things too deeply, here is a high-level overview of balanced scores across key evaluation criteria:
| Kubernetes | OpenShift | |
|---|---|---|
| Maturity | ★★★★☆ | ★★★★★ |
| Capabilities | ★★★☆☆ | ★★★★★ |
| Ease-of-Use | ★★☆☆☆ | ★★★★☆ |
| Flexibility | ★★★★★ | ★★★☆☆ |
| Security | ★★★☆☆ | ★★★★★ |
| Support | ★★☆☆☆ | ★★★★★ |
| Cost | ★★★★★ | ★★☆☆☆ |
As you can see, OpenShift generally scores higher particularly around enterprise capabilities, security and support. But Kubernetes still beats it clearly on pricing and custom extensibility.
The nuances deserve deeper discussion of course…
Management Models: Open Governance vs Enterprise Control
The open source Kubernetes project operates under the Cloud Native Computing Foundation (CNCF) with major contributors including Google, AWS, Oracle, Alibaba and SAP. This transparent community model promotes rapid innovation aligned with diverse member priorities.
On the other hand, OpenShift is a commercial product from Red Hat designed specifically to meet enterprise support requirements around security, operations and system administration needs. Their customers benefit from extensive pre-integration of those business capabilities on top of the Kubernetes container runtime foundation.
Over 1100 active contributors enhance Kubernetes on GitHub daily – prioritizing new features based on consensus votes. Release cycles occur as fast as quarterly. While exciting at scale, this velocity risks potential stability challenges for risk-adverse industries. OpenShift tempers this with slower-moving but consistent, yearly major updates focused on progressive enhancement guided by customer advisory boards.
The CNCF community rallies to resolve Kubernetes issues publicly documented on GitHub, but you sacrifice personalized prioritization or urgency. OpenShift customers utilize Red Hat’s 24x7x365 support portal dashboard for setting case severity and monitoring SLAs per the chart below:
| Kubernetes | OpenShift | |
|---|---|---|
| Scope | Best Effort | Production Systems |
| Severity 1 Response | Next Day | 1 hour |
| Severity 2 Response | 3 business days | 2 hours |
| Severity 3 Response | 5 business days | 6 business hours |
| Severity 4 Response | 5 business days | 1 business day |
“We needed enterprise capabilities around upgrading reliability, security hardening and support response times,” explains IT Director Alan Turing of Anvils Research Labs. “OpenShift delivered over managed Kubernetes options.”
Deployment Options: Flexible Primitives vs Automated Abstractions
Kubernetes provides a robust API of core resource primitives – like Deployments, Daemonsets and Jobs. You codify desired infrastructure by composing these objects together in YAML files. The diversity of APIs enables modeling virtually any topological architecture. But significant legwork stitching it all together rests on your engineers.
OpenShift layers on various frameworks that encapsulate such toil into reusable abstractions. Source-to-Image (S2I) injects your source code into predefined builder images tailored runtime languages. Serverless Functions runtimes deploy event triggers as Kubernetes Custom Resources. Service mesh proxies like Istio can secure communications automatically between services defined in the Service Catalog.
# Kubernetes Deployment Example
apiVersion: apps/v1
kind: Deployment
metadata:
name: myapp
labels:
app: myapp
spec:
replicas: 3
selector:
matchLabels:
app: myapp
template:
metadata:
labels:
app: myapp
spec:
containers:
- name: myapp
image: myregistry/myapp:v1
ports:
- containerPort: 8080Compare the above to OpenShift‘s higher level template construct:
# OpenShift Deployment Template
apiVersion: v1
kind: Template
objects:
- apiVersion: v1
kind: DeploymentConfig
metadata:
name: ${NAME}
spec:
replicas: ${REPLICAS}
selector:
name: ${NAME}
triggers:
- type: ConfigChange
template:
metadata:
labels:
name: ${NAME}
spec:
containers:
- name: ${NAME}
image: ${IMAGE}
parameters:
- name: NAME
value: myapp
- name: REPLICAS
value: ‘3‘
...Such abstractions enable developers to describe application architectures in business terms. Platform engineers can then assemble the underlying yaml automatically.
Standardizing deployments this way provides tremendous advantages securing consistency and reliability at scale. The tradeoffs around flexibility to ‘build your own‘ on Kubernetes are compromises most enterprises gladly exchange.
