Heroku vs Kubernetes

In today’s world of microservices, containerized architectures, and serverless platforms, the average chief experience officer (CXO) struggles to make this key decision: Should we prioritize developer productivity and rapid delivery or build a highly customized infrastructure for ultimate control?

Built for rapid delivery, Heroku is a platform as a service (PaaS) solution offering a near-zero configuration platform for developing, deploying, maintaining, and scaling applications without configuring servers, containers, or any of the much-dreaded “IT/Ops tasks.”

Built for configurability and scaling, Kubernetes is an open-source container orchestration platform that allows organizations to build almost anything as long as it runs on a container. The sheer number of configurations possible provides the ultimate control to scale, fine-tune infrastructure, and develop applications.

Understanding each platform’s strengths and weaknesses allows CXOs to make informed decisions that balance developer experience with infrastructure performance. Organizations often need a balance between PaaS approaches—which alleviate concerns about operational overhead, infrastructure maintenance, scalability, and high availability—and platforms that offer full customization and control over the infrastructure. In this article, we also explore a tool that combines the best of both worlds, providing the benefits of PaaS with the flexibility of customizable platforms.

Summary of key Heroku vs. Kubernetes points of comparison

Cost considerations

Cost is a significant factor when choosing a platform for deploying your application. Here’s how Heroku and Kubernetes compare in terms of pricing.

Pricing models

Heroku employs a pay-as-you-go model. You pay for your application’s dyno hours (compute resources), storage space, add-ons (additional services like databases), and data transfer. This is ideal for unpredictable workloads or applications in the early stages.

Being open-source, Kubernetes has no direct cost, but the actual expense lies in the underlying infrastructure. To run your Kubernetes cluster, you must pay for cloud provider resources (servers, storage). This can involve upfront costs for setting up the cluster and ongoing charges for the resources it consumes.

Scaling needs

Users can manually scale applications on Heroku or configure them to scale automatically based on traffic. While convenient, this approach can incur additional unexpected costs if it is done without a proper understanding of the use case and requirements.

Kubernetes offers fine-grained control over scaling. Users can define how applications scale based on specific metrics (such as CPU or memory usage) using scaling strategies such as horizontal pod autoscaling (HPA) or vertical pod autoscaling (VPA). While this flexibility is advantageous, improperly configured scaling can lead to wasted resources and higher costs.

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Hidden costs

As a managed platform, Heroku handles most operational tasks, reducing the need for a large DevOps team, but there’s a tradeoff: Developers lose some control over the underlying infrastructure and customization options.

In contrast, Kubernetes requires significant expertise to manage and maintain. While open-source tools and managed Kubernetes services exist, they often come with additional costs, such as:

• Operational costs: You may need to invest in training your team or hiring skilled professionals to manage the cluster effectively.
• Kubernetes provider cost: If you use a managed Kubernetes service such as EKS/AKS/GKE/OpenShift, you have to pay the provider, which takes care of the operational overhead, scalability, high availability, etc.

Resource allocation

Kubernetes and Heroku represent two approaches to managing resources for containerized applications: granular or limited control.

Granular control in Kubernetes

Kubernetes provides fine-grained control over resource allocation for containers via many built-in features, such as requests and limits, quality of service (QoS) classes, resource quotas and limit ranges, node selectors, and pod affinity/anti-affinity.

The following manifest shows how to set pod resources and use pod affinity features to control the scheduling of workloads based on specific node selectors:

apiVersion: v1
kind: Pod
metadata:
  labels:
    run: redis
  name: redis
spec:
  containers:
  - image: redis
    name: redis
    resources:
      requests:
        memory: "256Mi"
        cpu: "250m"
      limits:
        memory: "512Mi"
        cpu: "500m"
    affinity:
      nodeAffinity:
        requiredDuringSchedulingIgnoredDuringExecution:
          nodeSelectorTerms:
          - matchExpressions:
            - key: disktype

Limited control in Heroku

Heroku offers a more abstract approach, focusing on ease of use over fine-grained control. You typically define application requirements (such as language and framework), and Heroku manages the underlying infrastructure and resource allocation. It provides different tiers (hobby, professional, etc.) with predefined resource allocations for CPU, memory, etc. While you can’t directly manipulate these resources, you can scale your application horizontally by adding more dynos (application instances) within the chosen tier.

Tradeoffs

Here are the essential resource allocation tradeoffs:

• Customization vs. ease of use: Kubernetes offers power and flexibility at the expense of complexity. Managing resource allocation requires knowledge and effort. Heroku simplifies deployment and management but limits your ability to customize resource usage.
• Performance optimization: Kubernetes allows you to fine-tune resource allocation to optimize performance for specific workloads. Heroku’s predefined tiers might only be ideal for some applications.
• Resource efficiency: Kubernetes allows specific allocations for each container, which can lead to more efficient resource utilization. Heroku might allocate more resources on average to ensure smooth operation.

Background tasks

Heroku provides a built-in scheduler that’s simple to set up but offers limited flexibility. Developers can schedule tasks to run daily, hourly, or every 10 minutes, which is sufficient for basic needs but may make it necessary to have more complex scheduling requirements.

Kubernetes, in contrast, provides more sophisticated scheduling options. It offers CronJobs—which allow for complex schedules based on cron expressions—and dedicated scheduling controllers for more granular control over job execution and retries. This flexibility enables Kubernetes to handle intricate scheduling needs effectively.

Some of the advantages of using Kubernetes in this context include:

• Greater flexibility in defining schedules for background tasks
• The ability to scale jobs independently based on workload or to run them on specific nodes
• Integration capabilities with external scheduling tools, like Apache Airflow, for advanced workflow management

These features make Kubernetes particularly well-suited for applications with complex background processing needs or those requiring fine-tuned control over task scheduling and execution.

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Networking and security

Heroku and Kubernetes differ significantly in their approaches to networking and security, each offering unique advantages and limitations.

Both platforms benefit from utilizing virtual private clouds (VPCs) to isolate applications from the public internet, enhancing overall security. They also provide access control mechanisms: Heroku uses identity and access management (IAM), which is a centralized system for managing identity and access, while Kubernetes employs role-based access control (RBAC), which is a more granular, permission-based system. These features help restrict unauthorized access to databases and backend services.

Heroku’s Private Spaces offer a degree of network isolation but with a notable limitation: Applications within the same space can still communicate with each other. This lack of complete network layer isolation distinguishes Heroku from Kubernetes. Moreover, Heroku databases cannot be firewalled, which may raise security concerns for organizations sensitive about internet-accessible databases.

Kubernetes, on the other hand, provides more comprehensive control over network traffic. It utilizes network policies, ingress controllers, and services to manage network interactions with granularity. This allows for more precise security configurations and better isolation of components within the cluster.

External dependencies

Heroku’s managed environment abstracts away infrastructure management from developers, allowing a simpler deployment process. While this reduces operational overhead, it limits control over the underlying system. Potential issues include constraints on customization, troubleshooting difficulties, and the risk of Heroku-induced delays impacting release schedules.

In contrast, Kubernetes provides granular control over infrastructure, allowing for tailored configurations and the integration of various tools. However, this flexibility comes at the cost of increased complexity. Managing these dependencies requires specialized knowledge and introduces additional potential failure points that must be carefully monitored.

Both technologies have their merits and drawbacks. Heroku’s managed environment can be ideal for teams seeking simplicity and reduced operational overhead, especially for smaller applications or those with straightforward requirements. Kubernetes, while more complex, offers the flexibility and control necessary for larger applications or for teams with specific infrastructure needs and the expertise to manage them effectively.

Scalability

Both Heroku and Kubernetes offer horizontal scaling capabilities to handle increased traffic, but their approaches and strengths differ significantly.

Heroku employs a straightforward and user-friendly dyno-based scaling model. Users can configure dynos to scale automatically based on metrics like CPU or memory usage. Professional dynos provide additional flexibility, with options to set minimum and maximum dyno counts. This model is particularly beneficial for smaller applications or those with unpredictable scaling needs, offering a cost-effective, pay-as-you-go approach that eliminates upfront infrastructure costs and reduces the burden on DevOps teams.

Kubernetes, on the other hand, provides more fine-grained control over resource allocation and scaling. It allows users to define specific resource requests and limits for each container within a pod, ensuring efficient resource utilization. The Kubernetes Horizontal Pod Autoscaler (HPA) enables automated scaling based on various metrics, offering greater flexibility in managing application performance. However, implementing these scaling strategies often requires third-party add-ons like the Metrics Server for HPA and careful analysis of scaling needs.

While Heroku’s approach is more straightforward, it can be less efficient and potentially costlier for resource-intensive applications. Kubernetes shines in scenarios involving larger, more complex applications with specific scaling requirements, providing greater control and flexibility. However, this comes with potential higher upfront costs and the need for in-house expertise to manage the cluster effectively.

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Developer productivity

Under the hood, both Heroku and Kubernetes build containers for your code. The difference mainly lies in configurability.

Build management

Heroku simplifies deployments by automatically handling language detection, buildpack selection, and image building. However, its unique abstraction layer limits configuration options and migration to other platforms. Heroku has limited support for building Docker Images, and its abstraction layer dictates how applications are built and hosted, making migration to different platforms impractical.

In Kubernetes, developers have complete control over the image’s contents and can include specific libraries, tools, or configurations not readily available in prebuilt images. Kubernetes also supports buildpacks because it has now become a CNCF project.

CI/CD considerations

Heroku has a built-in CI/CD pipeline triggered by Git pushes, and it integrates with popular Git providers like GitHub. The pipeline steps are predefined by Heroku, which may not cater to specific needs for complex deployments and could lead to vendor lock-in. On the other hand, Kubernetes requires separate CI/CD tools like Jenkins or GitLab CI/CD, giving developers complete control over the pipeline and allowing for flexibility and customization. Managed CI/CD solutions like AWS CodePipeline and Azure DevOps Pipelines integrate better and with more flexibility than Heroku.

Preview environments

Heroku Review Apps allow one-click creation of temporary deployments linked to pull requests. They have limited customization options, and environments might not fully mirror production configurations. While useful for quick code reviews, they might not be suitable for complex testing scenarios.

Kubernetes Preview Environments are highly configurable using tools like Helm or Kustomize. They can be configured to resemble production environments closely, but they come with increased complexity, requiring integration and management of additional tools. Some major projects in this area include vClusters, which allows teams to create virtual Kubernetes clusters inside a real Kubernetes cluster.

Choosing the right solution

Let’s summarize the key points to consider when deciding between Heroku and Kubernetes.

TCO and pricing

Heroku offers a more straightforward pricing structure but can be expensive for complex applications. Kubernetes requires initial investment in infrastructure and ongoing management but provides more granular control and potentially lower costs for resource-intensive applications.

Security, compliance, and vendor lock-in

Heroku provides built-in security features and SOC 2 compliance, but it presents vendor lock-in concerns as a proprietary platform. Kubernetes requires a more hands-on approach to security but offers greater flexibility for customization. It’s open-source and platform-agnostic, avoiding vendor lock-in. Compliance certifications for Kubernetes depend on your specific implementation.

Skills, resources, and exit strategy

Heroku is easier to use, with a more straightforward learning curve, but migrating out can be challenging due to custom configurations and buildpacks. Kubernetes requires a deeper understanding of container orchestration and infrastructure management, but applications are containerized and portable across different environments, making migration easier.

Heroku excels in rapid development and the prototyping of small applications. Its managed environment simplifies deployment with preconfigured options, benefiting teams with limited Kubernetes expertise. Kubernetes, conversely, suits experienced teams managing resource-intensive projects with complex scaling needs. It offers granular controls over resource allocation and container lifecycles, integrating with various tools for autoscaling, monitoring, and large deployments. This flexibility requires deeper container orchestration knowledge. For complex, large-scale, and future-proof applications, Kubernetes is the preferred choice.

An alternative route: the best of both worlds

Coherence is an internal developer platform (IDP) that integrates with your AWS or GCP infrastructure. It enhances platform-as-a-service (PaaS) capabilities while maintaining user control. It offers Kubernetes-like flexibility, Heroku-like ease of use, and migration tools. It supports cloud-native technologies like Terraform and Docker and integrates with external CI/CD tools such as GitHub Actions, reducing vendor lock-in.

Coherence is a customizable developer platform powered by CNC, an open-source platform engineering framework that sits on top of your favorite infrastructure-as-code tools. It offers:

• Infrastructure as code execution with RBAC, auditing, and cloud provider integration
• Automated CI/CD integrated with source control, such as with GitHub, supporting customizable build and deployment stages like database seeding, migrations, end-to-end tests, and parallelized unit tests
• A developer portal with self-service developer functionality, including secrets management, deployment interfaces, and environment management
• Automated preview environments for each PR, supporting frontends and backends, custom domains, and Github/Slack integration
• Customer self-hosting capabilities for application distribution to customer cloud accounts without the need for Kubernetes
• Analytics for usage, adoption, and build pipeline performance metrics

Read more about Coherence preview environments and test environment management.

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Decision-making steps

Engineering teams seeking a cloud-agnostic solution should follow this plan to make an informed decision.

1. Define core product requirements: Clearly articulate your product’s MVP features, estimating the computational, storage, and network resources needed. Consider future scalability requirements and how your chosen platform should accommodate potential growth. This foundational step ensures that your platform choice aligns with immediate and long-term product goals.
2. Conduct rapid platform assessment: Evaluate your potential solution based on key criteria: ease of use, scalability, cost-effectiveness, developer experience, integration capabilities, and security features. Leverage your team’s existing knowledge and conduct proof-of-concept tests on both platforms. This hands-on approach provides valuable insights into each platform’s performance, development speed, and potential challenges.
3. Build a minimal viable architecture: Design a flexible system architecture that can adapt to your chosen platform. Identify which components of your application will most affect the platform choice. Prioritize the development of core features, leaving optional elements for future iterations.
4. Make an informed decision: Create a comprehensive comparison matrix based on your criteria. Balance the need for rapid market entry with the platform’s capacity to support future growth. Involve your development team in decision-making, considering their preferences and concerns. Carefully weigh each platform’s pros and cons to choose the one that best serves your product and team.
5. Implement and Iterate: After selecting your platform, establish clear milestones for the initial development phase. Continuously monitor key performance metrics to assess the platform’s suitability for your needs. Remain flexible and prepared to pivot if the chosen platform becomes a bottleneck, potentially adopting a hybrid approach if necessary.

Final thoughts

This article compares Kubernetes and Heroku as platform choices. The primary difference between them lies in infrastructure control: Kubernetes offers granular control for organizations with variable scaling needs, while Heroku is better suited for early-stage startups with uncertain requirements.

Other factors considered include security, vendor lock-in, team skills, exit strategy, and Coherence as an alternative platform that aims to balance infrastructure ownership with PaaS-like simplicity.

The optimal platform choice depends on specific needs, development processes, and business objectives.