Table of Contents
Typical patterns where OpenShift fits
OpenShift shines when you need a standardized, automated way to build, deploy, run, and manage containerized applications at scale. Rather than listing every possible use, this chapter focuses on recurring patterns you’ll see in real environments.
We’ll briefly describe the scenario, why OpenShift is a good match, and key platform features that are typically involved (without going into their inner workings, which are covered later in the course).
Modernizing existing applications
Many organizations start with OpenShift as part of an application modernization or “cloud transformation” program.
Lift-and-shift of legacy apps
Some existing applications can be moved into containers with minimal change:
- Typical workloads
- Monolithic Java/.NET apps
- Internal line-of-business tools
- Smaller services with state in an external database
- Why OpenShift
- Provides a consistent runtime environment across dev, test, and prod
- Built-in image management and deployment workflows reduce “works on my machine” issues
- Resource limits and quotas help prevent a single migrated app from exhausting cluster resources
Incremental refactoring and strangler patterns
Instead of rewriting everything at once, teams often:
- Wrap a legacy application and gradually peel off functionality into new microservices.
- Place these microservices on OpenShift while the legacy core remains on VMs or bare metal.
- Why OpenShift
- Supports side-by-side deployment of old and new components
- Routing and versioning features help direct traffic to new services or roll back if needed
- Makes it easier to standardize CI/CD pipelines for new components
Cloud‑native greenfield applications
New applications are increasingly designed as microservices, event-driven systems, and APIs from the start.
- Typical workloads
- Customer-facing web and mobile backends
- Public and internal REST/GraphQL APIs
- Event-driven or message-based services
- Why OpenShift
- Provides a Kubernetes-based platform with guardrails, policy, and security defaults
- Makes it easier to define and manage application configuration, secrets, and storage
- Integrated CI/CD, image build, and deployment patterns reduce boilerplate platform work
- Supports traffic management patterns (such as blue/green and canary releases) used by agile teams
This is a core “target” use case: OpenShift as the application platform for new digital services.
Enterprise DevOps and CI/CD platforms
OpenShift is often adopted as the common runtime behind an organization’s DevOps toolchain.
- Typical scenario
- Many teams, multiple languages and frameworks
- Desire for standardized pipelines and deployment practices
- Need to enforce security and compliance policies
- Why OpenShift
- Provides a stable, self-service environment for builds, tests, and deployments
- Enables pipeline-driven workflows where every change goes through automated checks
- Integrates with version control, artifact registries, and external CI/CD tools
- Makes it easier to encode organizational policies as platform defaults rather than manual reviews
Over time, teams move from individual ad hoc setups to a shared “internal developer platform” based on OpenShift.
Multi-tenant internal platform (“Internal Developer Platform”)
Large organizations often use OpenShift as the foundation for an internal platform shared by many teams or business units.
- Typical requirements
- Strong isolation between teams
- Central security and governance
- Chargeback/showback of resource consumption
- Standardized base images and runtime stacks
- Why OpenShift
- Namespaces (projects) with role-based access control allow clear multi-tenancy
- Quotas and limits support capacity management and cost attribution
- Built-in policies and security features help enforce organizational standards
- Operators provide a controlled way to offer shared platform services (databases, message queues, logging, etc.) to all teams
In this setup, OpenShift acts as the backbone of a “platform as a product” offered internally.
Hybrid and multi‑cloud deployments
Enterprises rarely live in a single environment. They might have:
- On-premises data centers
- Private clouds
- One or more public clouds
OpenShift is frequently used to standardize application deployment across these locations.
- Typical patterns
- Common platform for on-prem and public cloud clusters
- Disaster recovery targets or active/active multi-region setups
- Gradual migration from on-prem to cloud while minimizing application changes
- Why OpenShift
- Provides a consistent application platform across infrastructure providers
- Reduces dependency on any single cloud provider’s proprietary services
- Eases relocation of workloads by keeping deployment and operations practices largely the same
This use case is about portability and operational consistency, not just raw compute.
Regulated and security‑sensitive environments
OpenShift is often selected where compliance, auditing, and strong security controls are non-negotiable.
- Typical sectors
- Finance and banking
- Healthcare and life sciences
- Government and defense
- Telecom and critical infrastructure
- Why OpenShift
- Opinionated defaults for container security and isolation
- Fine-grained access controls and policy enforcement
- Strong integration with enterprise identity management
- Support for auditing and compliance reporting
- Purpose-built distributions and add-ons for highly regulated environments
In these sectors, OpenShift is valued less for “raw Kubernetes” and more as a hardened, supported platform.
Data processing, analytics, and AI workloads
While traditional HPC is its own topic, many organizations run data-intensive and AI workloads on OpenShift as part of a broader data platform.
- Typical workloads
- ETL/ELT and data preparation pipelines
- Streaming data processing (logs, metrics, IoT)
- Model training and inference services for machine learning
- Self-service notebooks for data scientists
- Why OpenShift
- Provides a common platform for data engineers, data scientists, and application teams
- Allows scaling of compute-intensive containers up and down on demand
- Integrates GPU-accelerated workloads with regular application services
- Simplifies packaging and deployment of models as APIs or batch jobs
The key idea here is unifying data/AI workloads with the rest of the application ecosystem rather than keeping them on isolated islands.
Edge and remote site deployments
OpenShift variants can run in smaller footprints suitable for edge or remote locations.
- Typical use cases
- Retail stores, factories, and warehouses
- Telco edge for 5G and network functions
- Remote offices with intermittent connectivity
- Why OpenShift
- Provides a manageable container platform even at small scale
- Enables consistent deployment and upgrade of applications across many sites
- Supports “central management, local execution” patterns: control from a central team while apps run at the edge
- Makes it easier to ship, update, and monitor edge workloads in a standardized way
This use case is about managing many distributed locations as one logical platform.
Shared platform for third‑party and partner applications
Some organizations expose OpenShift-based environments to partners, vendors, or internal product teams as a hosted platform.
- Typical scenarios
- Ecosystem or marketplace where partners deploy their solutions
- Multi-organization collaboration environments
- Central IT hosting apps developed by various subsidiaries
- Why OpenShift
- Multi-tenancy features allow multiple organizations to share infrastructure while keeping separation
- Policy controls let the hosting organization define what is allowed (images, registries, resources)
- Integrated monitoring and logging help the platform owner supervise usage while offering autonomy to tenants
Here, OpenShift becomes the “platform of platforms” for a broader ecosystem.
Burst and batch workloads
Many workloads don’t run continuously but in bursts: periodic jobs, report generation, or irregular heavy processing.
- Typical workloads
- Nightly reporting and billing jobs
- Data imports and exports
- Scheduled maintenance or housekeeping tasks
- Why OpenShift
- Containers can start quickly, run the job, and then free resources
- Scheduling and automation features support recurring and event-driven jobs
- Teams can express resource requirements and deadlines, letting the platform handle placement and scaling
This use case is about efficient resource utilization for non-steady workloads.
Summary: Recognizing good OpenShift candidates
Across all these scenarios, OpenShift is particularly strong when you need:
- A standardized, self-service application platform for many teams
- Consistent operations across environments (on-prem, cloud, edge)
- Built-in security, governance, and compliance capabilities
- Integration of Dev, Ops, and Security practices on one platform
- The ability to support both legacy-adjacent and cloud-native workloads
When evaluating whether OpenShift is a good fit, you’re usually asking:
- Do we need a shared enterprise platform rather than isolated clusters?
- Do we need strong governance, security, and lifecycle management?
- Do we want consistent deployment and operations across multiple environments?
If the answer to these is “yes,” you’re looking at a typical OpenShift use case.