How to Choose Between Kubernetes Operator and HELM Automation

The Operator Pattern and Architecture

The Operator pattern, introduced by CoreOS, relies on two key Kubernetes concepts:

• Custom Resource Definitions (CRDs) – These define new types of Kubernetes resources (e.g., PostgresCluster or KafkaTopic).
• Controllers – These watch for changes to those custom resources and take action to reconcile the state.

A typical Operator includes:

• A CRD that defines the application’s configuration
• A controller (usually written in Go) that contains the automation logic
• A Deployment that runs the controller in the cluster
• RBAC roles to authorize the controller’s actions

This architecture enables operators to continuously manage application state, utilizing a control loop to detect changes and apply necessary actions.

Custom Controllers and CRDs

With CRDs, teams can define new Kubernetes objects that represent the desired state of their applications. For example, instead of managing a database with raw manifests, you might create a PostgresCluster resource and let the Operator handle provisioning, scaling, and recovery.

The custom controller is the brain behind the Operator. It watches the cluster for changes to the CRD, checks the actual state of the system, and takes action to bring it in line with the desired configuration.

This separation between what you declare and how it’s managed makes Operators powerful and reusable across environments.

Reconciliation Loops and State Management

Operators don’t just respond to one-time events — they run a continuous reconciliation loop. Even if events are missed, the controller rechecks the state periodically and corrects any drift. This enables:

• Self-healing (e.g., restarting failed pods)
• Declarative upgrades
• Dynamic scaling
• Automated failover and backup orchestration

This always-on feedback loop is what makes Operators suitable for stateful, business-critical workloads where manual intervention isn’t scalable.

Operator Ecosystem and Examples

The Kubernetes ecosystem includes many widely adopted Operators, developed using frameworks like the Operator SDK, Kubebuilder, and Metacontroller. Some notable examples include:

• Prometheus Operator: Manages Prometheus, Alertmanager, and related components.
• MongoDB Community Operator: Automates MongoDB deployments with replica sets, sharding, and backup integration.
• Elastic Cloud on Kubernetes: Provides lifecycle automation for Elasticsearch, Kibana, and Beats.
• Istio Operator: Simplifies Istio installation and configuration using custom resources.
• Strimzi Kafka Operator: Manages Apache Kafka clusters on Kubernetes.

Operators vary in complexity. Some perform basic installations, while others orchestrate multi-step workflows across multiple resource types. Their adoption is growing in domains like data infrastructure, observability, and service meshes, where declarative automation reduces operational burden.

Package Management and Chart System

A Helm chart is a versioned package of Kubernetes resources. It typically includes:

• Chart.yaml – Metadata about the chart
• values.yaml – Default config values
• templates/ – YAML template directory for Kubernetes resources like Deployments and Services

Charts are installed using simple commands like helm install or helm upgrade, which render templates into manifests and send them to the Kubernetes API.

Helm charts can be stored in public or private repositories, allowing teams to reuse prebuilt configurations for common apps like Redis, Prometheus, and NGINX.

Template Engine and Configuration

Helm uses a powerful templating engine based on Go templates. This enables:

• Variables from values.yaml or CLI overrides
• Conditional logic and loops
• Reusable snippets via _helpers.tpl

This setup lets you create a single chart for all environments, just plug in different values. For example, you might deploy one replica of Redis in dev and five in prod, with the same chart. This flexibility helps reduce duplication and config drift.

Helm also integrates cleanly with GitOps and CI/CD pipelines, making it ideal for declarative infrastructure and automated delivery.

Release Management and Lifecycle

Each time you install a chart, Helm creates a release, which tracks the chart version, the values used, and the revision history.

This release model enables features like:

• Rollbacks: Use helm rollback to revert to a previous revision
• Upgrades: Seamlessly apply updates to applications without recreating resources
• Deletion: Cleanly remove all associated resources when uninstalling

These capabilities enable Helm to manage the full application lifecycle with version control and auditability, reducing deployment risk and simplifying operations.

Ecosystem and Integration Patterns

The Helm ecosystem is large and mature. Key features include:

• Thousands of charts in public repositories
• Plugins for diffing, secrets, testing, and more
• Native integration with GitHub Actions, Argo CD, Flux, and other CI/CD tools

Helm is often the foundation of GitOps pipelines and developer-facing platforms. It enables teams to deploy apps quickly, with clear version control and configuration consistency.

Architecture and Design Philosophy

Helm is a Kubernetes native package manager. It simplifies deployment by bundling manifests into a reusable chart. Charts are processed client-side by the Helm CLI, rendered into raw YAML, and sent to the Kubernetes API server. Helm is stateless. It does not manage applications after deployment unless manually upgraded or rolled back.

Operators are custom controllers that run inside the cluster. They extend Kubernetes by using CRDs to define custom resources and control loops to manage them. Operators interact continuously with the control plane to maintain application state.

Helm is ideal for templating and packaging. Operators are better for long-running lifecycle automation.

Automation Scope and Intelligence

Helm handles Day-1 operations: install, upgrade, and rollback. It doesn’t monitor the running system or react to failures.

Operators support both Day-1 and Day-2 operations: automated failover, scaling, backups, version checks, and more. They run control loops that detect drift and take action, enabling self-healing and proactive management.

Operators are especially valuable for complex or stateful applications that require more than configuration templating.

Complexity and Development Effort

Helm is simple to learn and use. Creating a chart requires basic YAML templating. Most users can get started quickly by downloading community charts or creating their own with helm create.

Operators are more complex. Building one typically requires:

• Proficiency in Go
• Knowledge of Kubernetes internals (CRDs, events, reconciliation patterns)
• Use of frameworks like Operator SDK or Kubebuilder

That complexity comes with power but also a higher barrier to entry and more maintenance overhead.

Maintenance and Operational Requirements

Helm is mostly hands-off after deployment. Since it runs on the client side, it doesn’t consume cluster resources. Maintenance typically involves updating chart versions, validating values files, and occasionally troubleshooting rendering issues or dependency conflicts. It’s lightweight and low-overhead.

Operators, by contrast, are active components inside the cluster. They require ongoing maintenance and observability. Teams must monitor the Operator’s health, manage upgrades to both the controller and its CRDs, and ensure proper RBAC and resource usage.

Here’s how they compare:

If your team has limited resources or isn’t ready to manage in-cluster automation components, Helm is a safer starting point. Operators offer more power but require a larger operational investment.

Complex Stateful Applications

Kubernetes is built for stateless apps, but stateful workloads need more operational control. These applications require coordinated actions: scaling with storage, backup management, failover handling, and ordered deployments.

In 2024, 74% of organizations reported running stateful workloads in containers, up from 63% in 2023. Helm alone can’t handle their runtime demands, but Operators can.

Examples:

• The PostgreSQL Operator manages backups, replica promotion, and recovery.
• The Strimzi Kafka Operator handles broker coordination, rolling updates, and metrics-based scaling.
• The MongoDB Community Operator automates replica sets and sharding.

Operators shine in these scenarios because they maintain runtime state and enforce ordering, constraints, and health checks, making them ideal for managing scalable database monitoring workflows that go beyond Day-1 deployment.

Day-2 Operations and Lifecycle Management

Helm is great for initial deployment (Day-1), but it stops there. Operators cover Day-2: the ongoing tasks that keep your app running reliably over time.

Common Day-2 capabilities include:

• Rolling upgrades with safety checks
• Scheduled backups and restores
• Auto-scaling based on usage or metrics
• Self-healing when pods fail or resources drift
• Config updates without service disruption

Example: The Prometheus Operator not only installs Prometheus but also dynamically manages scrape configs and watches for changes to custom resources like ServiceMonitors.

This always-on control is critical for high-SLA systems and production databases, where real-time monitoring practices help ensure uptime and early detection of failure states.

Domain-Specific Automation Requirements

Some workloads need specialized operational logic. Think of environments like finance, healthcare, or enterprise SaaS, where uptime, data safety, and compliance are non-negotiable.

Examples of domain logic that Operators can handle:

• Delayed init after storage provision
• Ordered pod startup and teardown
• Policy enforcement on CR inputs
• Region-aware scheduling or failover logic

Example: The Zalando Postgres Operator supports cloud-native PostgreSQL with built-in TLS, IAM, and tenant scaling logic. These features are tough to template but easy to encode in a controller.

With an Operator, you can encode business logic directly into the platform, reducing reliance on custom scripts or manual ops.

Compliance and Governance Automation

Regulated industries often require auditable, automated enforcement of operational controls. Operators can enforce these policies natively by reacting to CR changes, rejecting invalid configurations, or automatically applying required security settings.

Examples of compliance-related automation with Operators include:

• Ensuring encryption-at-rest is always enabled for database volumes
• Automatically labeling or annotating workloads with compliance metadata
• Managing certificate rotation via integration with tools like cert-manager
• Blocking unsafe schema migrations through custom webhooks and validation logic

In this context, Operators help bridge the gap between platform teams and governance stakeholders by ensuring continuous compliance without introducing manual checks into CI/CD workflows.

Stateless Applications and Microservices

Helm is a strong fit for stateless workloads like frontend services, APIs, cron jobs, and microservices that don’t rely on persistent storage or complex runtime coordination.

These applications are often small, repeatable, and horizontally scalable, which is perfect for Helm’s templating model. Common Helm-friendly use cases include:

• Deploying NGINX or Redis for caching
• Launching frontend apps with Ingress and environment variables
• Managing microservice deployments with shared chart templates

For these services, Helm provides fast, repeatable deployments without needing custom controllers.

Multi-Environment Configuration Management

Managing deployments across development, staging, and production environments can introduce configuration drift. Helm’s templating engine and values files (values.yaml) provide a structured way to manage multi-environment deployments by separating configuration from application logic.

You can define a single chart and provide different values for environment-specific settings, such as:

• Image tags
• Resource limits
• Environment variables
• Secrets and ConfigMaps
• Ingress paths and domains

By using profiles or directory-based overrides, teams can maintain one chart and safely deploy it across environments without duplicating manifests. Tools like Helmfile or Kustomize plugins further extend Helm’s capabilities in managing environment-specific deployments at scale.

CI/CD Integration and Deployment Automation

Helm integrates easily with most CI/CD tools, making it ideal for automated deployment pipelines. Benefits include:

• Versioned releases with rollback support
• Parameterized deployments (e.g., using Git commit hashes as image tags)
• Promotion between dev/stage/prod
• Secrets integration via Helm Secrets or external tools like Sealed Secrets

Tools like GitHub Actions, GitLab CI, Argo CD, and Flux all support Helm natively. You can plug Helm into any part of your pipeline to handle Kubernetes deployments cleanly and consistently.

Ecosystem Leverage and Package Distribution

One of Helm’s biggest advantages is its ecosystem. Thousands of charts exist for common workloads hosted on platforms like Artifact Hub and Bitnami. Using these charts, teams can:

• Deploy complex apps in minutes
• Fork and customize community-maintained templates
• Standardize deployments across projects

Example: If you need Grafana for monitoring, you can install a working setup in seconds with helm install grafana bitnami/grafana.

Helm also supports internal chart repositories and OCI registries, making it easy to create and distribute approved packages within your organization.

Complementary Usage Patterns

Helm and Operators aren’t mutually exclusive. In fact, many teams use both, each handling different layers of the stack. A common pattern looks like this:

• Helm deploys stateless apps (e.g., APIs, web frontends, jobs).
• Operators manage infrastructure and services with lifecycle needs (e.g., databases, service meshes, certificate managers).

This split keeps things clear: Helm handles the fast-moving app layer, while Operators manage the complex, stateful dependencies.

Operator-Managed Helm Releases

Some Operators use Helm under the hood. In these cases, the Operator handles automation and logic, while Helm handles templating and deployment.

Examples:

• The Prometheus Operator can use Helm to install components like Alertmanager.
• Flux supports HelmRelease CRs, allowing teams to manage Helm charts declaratively via GitOps.

This hybrid setup allows teams to use Helm where it makes sense, while still getting Operator-level automation and control.

Platform Engineering Strategies

Internal developer platforms (IDPs) often combine both tools to create scalable, self-service environments. The typical setup is:

• Platform teams use Operators to run shared infrastructure: databases, monitoring, ingress, and secrets.
• Developers use Helm charts to deploy apps using predefined templates and environment-specific configs.

This model balances control and flexibility. Operators enforce policies and resilience. Helm gives developers fast, repeatable delivery.

Integration Architecture Patterns

When combining Helm and Operators, clear integration boundaries are critical. Here are common hybrid architecture patterns:

By defining which tool owns what, teams can avoid tool conflict, improve automation visibility, and reduce maintenance overhead.

Application Requirements Assessment

Start by looking at what your application actually needs.

• Stateless applications: Use Helm. It handles templated deployment, version control, and environment configs without needing complex automation.
• Stateful or distributed systems: Use an Operator. These workloads need lifecycle control: backups, replica sync, safe scaling, and failover.
• Custom workflows or policies: Go with an Operator. It gives you a programmable interface to encode domain-specific behavior.

Quick Tip: Use Helm for speed and standardization. Use Operators when runtime conditions or state management are part of the job.

Organizational Maturity and Skills

Tool choice should match your team’s skills and bandwidth.

• If you’re new to Kubernetes, start with Helm. It’s YAML-based, easy to adopt, and supported by thousands of community charts.
• When you have an experienced DevOps or platform team, Operators are a better fit. You’ll need Kubernetes internals knowledge, Go development, and familiarity with controller logic.
• If you have a limited engineering time, Helm is quicker to implement and easier to maintain. Operators bring more power but require a long-term investment.

Cost-Benefit Analysis Framework

Evaluate the tradeoffs between development effort, maintenance, and automation depth, especially if you’re optimizing your telemetry pipeline architecture for performance and cost.

Sample Scenarios: When deploying a replicated NGINX web server, Helm is efficient and has low overhead. However, if you are managing a PostgreSQL cluster with automated failover and backups, an Operator delivers far more value.

Long-term Strategy Considerations

Your tool choice should align with long-term goals around scaling, standardization, and platform evolution. Think about: 

• Evolution Path: Most teams start with Helm. As automation needs grow, they introduce Operators for complex or persistent workloads, especially in internal developer platforms.
• Interoperability: The two tools can coexist.  Helm works well for exposing developer-facing app templates. Operators help you manage shared infrastructure and enforce policies.
• Scalability of Automation: Operators scale better with increasing operational complexity. They support self-healing, runtime validation, and multi-step workflows.

Strategy Tip: Start with Helm to get moving. Introduce Operators as your workloads and team demand more robust automation.

Development Effort and Timeline

Helm is quick to get started with. Creating a basic chart can take just hours, especially if you’re reusing existing ones from Artifact Hub or Bitnami. No coding is required beyond YAML templating.

Operators take more time depending on complexity. You’ll need to:

• Define CRDs
• Write a controller (typically in Go)
• Implement reconciliation logic
• Set up proper RBAC

This makes Operators more suitable for workloads that truly need lifecycle intelligence—not quick, repeatable deployments.

Team Skills and Training Requirements

To build and maintain Operators, your team should be comfortable with:

• Kubernetes internals (CRDs, events, controllers)
• Reconciliation patterns
• Go to another supported language
• Testing and debugging custom controllers

Helm, by contrast, requires:

• Familiarity with Kubernetes objects and YAML
• Basic templating and CLI usage
• Some scripting, depending on CI/CD integration

Teams without deep Kubernetes expertise will find Helm much easier to adopt and scale.

Testing and Validation Strategies

Helm testing is relatively simple:

• helm lint for syntax
• helm template for rendering validation
• CI-based tests for different value files

You’re mainly testing the output YAML and the correctness of templated configurations. Meanwhile, Operators need more advanced testing:

• Unit tests for controller logic
• Integration tests that simulate state drift, pod failures, and CR changes
• End-to-end environments to validate full automation behavior

The Operator’s logic must hold up under edge cases and runtime change, not just installation.

Monitoring and Observability

Monitoring Helm-based deployments typically involves tracking the health of Kubernetes resources such as Pods, Services, and Deployments. 

Integrations with Prometheus, Grafana, or tools like Edge Delta can provide visibility into application and infrastructure metrics. Observability at the chart level is limited unless integrated with other tools like Argo CD or Flux for lifecycle tracking.

Operators require more extensive observability. In addition to standard resource metrics, teams must monitor the operator’s logs, controller metrics (such as reconciliation duration and errors), and CRD status fields. This makes smart log pattern detection more important in proactive observability.

Prometheus can scrape custom metrics exposed by the operator. Edge Delta offers unified views of logs, metrics, and events for both the operator and the resources it manages. 

Comprehensive observability is critical for debugging reconciliation issues and ensuring reliable automation, especially when building out advanced monitor workflows for in-cluster systems.

Implementation Roadmaps

A typical Helm implementation roadmap starts with chart selection or authoring, followed by templating, values customization, and environment-specific configuration. Next comes CI/CD integration, chart promotion across dev/staging/prod environments, and optional GitOps tooling. This roadmap supports rapid delivery for stateless services and microservices.

For Operators, the roadmap begins with defining the application’s operational requirements, designing the CRD schema, and implementing controller logic. Subsequent steps include testing, CI/CD integration for operator deployment, observability instrumentation, and release versioning. 

Organizations often start with Helm and transition to Operators as complexity increases or lifecycle automation becomes critical.

Database Automation Case Study

Crunchy Postgres Operator is a leading example of operator-driven database automation. Managing PostgreSQL in Kubernetes using Helm templates alone often falls short when it comes to high availability, backup orchestration, and automated failover.

Crunchy Data built a Kubernetes Operator that:

• Manages PostgreSQL lifecycle (provisioning, scaling, and self-healing)
• Automates backups with pgBackRest
• Supports disaster recovery with replica promotion
• Enforces security and TLS policies

In contrast, Helm charts for PostgreSQL require significant manual templating and lack lifecycle logic. This makes Operators the preferred choice for managing stateful workloads that require domain-specific intelligence and operational guarantees.

Microservices Deployment Case Study

The CNCF Helm documentation highlights how Helm simplifies multi-service applications. In one example, a team packages a frontend and backend as separate Helm charts. Each chart handles its deployments, services, and configs, making it easy to version and manage independently.

Helm enables consistent, repeatable deployments across environments with minimal effort. Teams can override environment-specific settings (like image tags or resource limits) through values files or pipeline parameters.

This model is especially useful for microservices architectures, where templating and modularity are more important than deep runtime automation.

Complex Application Operator Implementation

Some workloads go far beyond templating. They require intelligent orchestration.

Red Hat built a Backstage Operator to manage enterprise deployments of Backstage, an open-source developer portal. Backstage’s extensibility and plugin architecture make it tricky to manage with Helm alone.

The Operator handles things like plugin lifecycle, PostgreSQL provisioning, TLS enforcement, and resource coordination. It allows platform teams to offer Backstage as a service, with less risk of config drift or broken upgrades.

This case shows that when apps need coordination across multiple components and high-level lifecycle awareness, Operators offer more robust automation than Helm.

Hybrid Platform Engineering Approach

Red Hat’s OpenShift platform encourages using Helm and Operators together.

• Developers use Helm to deploy stateless apps with reusable charts.
• Platform teams use Operators to manage shared services like Kafka, databases, and monitoring.

GitOps tools like Argo CD manage both seamlessly. This dual-tool strategy ensures developers get speed and flexibility, while the platform enforces consistency, reliability, and policy.

It’s a model many large teams follow: Helm for app delivery, Operators for infrastructure. Each tool is used where it makes the most sense.

Should I use an Operator or Helm for my application?

Use Helm if your application is relatively simple, stateless, or only needs basic install and upgrade functionality. Choose an Operator when your application is complex, stateful, or requires custom lifecycle automation such as backups, failover, or scaling.

What’s the main difference between Kubernetes Operators and Helm?

Helm packages Kubernetes manifests into reusable templates called charts. Operators, on the other hand, package automation logic into controllers that manage custom resources. Operators use CRDs to define application-specific behavior and continuously reconcile state in the cluster.

Can I use both Operators and Helm together?

Yes. Helm and Operators are not mutually exclusive. You can use Helm to install Operators or use both tools for different layers of the stack. Helm for deploying stateless apps and Operators for managing stateful services.

Which is easier to learn and implement: Operators or Helm?

Helm is easier to learn. It relies on YAML templates and can be installed and used with simple commands. Operators require deeper Kubernetes knowledge, CRD design, and often Go programming, making them more complex to build and maintain.

When should I choose an Operator over Helm?

Choose an Operator when your application requires automated Day-2 operations like backup, failover, scaling, or compliance enforcement. Use Helm when those lifecycle operations can be handled manually or are not needed.

References:

• The New Stack
• CNCF
• Kubernetes Operator
• Helm
• Red Hat
• Technology.org
• Cloud Native Now
• Datadog
• Kong
• Kubernetes Operator Pattern
• Groundcover
• Circleci
• Scaleway
• Medium
• Virtuozzo
• IBM
• Red Hat
• Velotio
• Kong
• Wallarm
• Hazelcast
• Datadog
• Groundcover
• Operator Hub
• Github
• CNCF
• Red Hat
• Firefly
• SDK
• Datadog
• LinkedIn
• Pascal Van Dam