If you are deciding where to deploy a web app, you will almost always run into a choice between a platform like Heroku and running on Kubernetes.
This article will compare Heroku and Kubernetes. They are two popular platforms for deploying and managing applications. This article breaks down the key differences in architecture, use cases, complexity, cost, and scalability to help engineers choose the right go-to platform for their needs.
Although this article focuses on Heroku, most of the tradeoffs apply to other PaaS platforms as well.
What are Heroku and Kubernetes?
Understanding the fundamental nature of each platform prevents mismatched expectations and architectural decisions that cause problems later. This section shows how Heroku and Kubernetes differ in their core design philosophies and what those differences mean for how you structure and deploy applications.
Heroku
Heroku provides a managed platform where you push code and the service handles everything else. It automatically helps you with provisioning servers, configuring load balancers, managing SSL certificates, and routing traffic. You interact with Heroku through simple commands like git push heroku main using source control integration, and the platform automatically detects your application's language, installs dependencies, and deploys it. The abstraction hides complexity but also introduces limited customization options and restricts your control over the underlying infrastructure.
The Heroku platform operates through a buildpack system that recognizes common application frameworks and configures them using sensible defaults. When you deploy a Rails application, Heroku detects the Gemfile, installs dependencies, precompiles assets, and starts your web server without requiring explicit configuration.
Kubernetes
Kubernetes takes a different approach. It provides a framework for container orchestration across clusters of machines.
Unlike Heroku, when you deploy a Rails application on Kubernetes, you build a container image that includes your app and its dependencies, define how it should run using YAML manifests (for deployments, services, and configuration), and then deploy it to a cluster.
You define your application's desired state through YAML configuration files that specify how many instances to run, how they should communicate, and what resources they need. Kubernetes then works to maintain that state, handling container lifecycle, networking, storage, and scheduling. This requires specialized knowledge and has a steep learning curve.
Comparing Heroku and Kubernetes directly can be tough because Kubernetes is an orchestration tool, not a complete platform. When you choose Kubernetes, you're also choosing where (AWS, Digital Ocean, etc.) and how to run it (VMs, K3s, bare metal, etc.). These decisions dramatically impact your experience, costs, and operational burden.
Kubernetes vs Heroku: When to choose each
It would make sense to choose Heroku when shipping features matters more than infrastructure optimization. Where Heroku makes more sense to use is in startups validating product-market fit. You can quickly deploy an MVP in an afternoon rather than spending weeks learning container orchestration. Small engineering teams should prefer platforms that abstract operational complexity.
Heroku makes sense until your app grows and you hit specific limitations. These include Git repos greater than 1 GB, requiring custom routing logic, or running workloads with strict cloud cost optimization requirements. Many successful companies run on Heroku for years before these constraints become a problem.
Pick Kubernetes when your application demands infrastructure customization that PaaS platforms can't provide. When you are working with data processing pipelines, machine learning inference serving, or maybe microservices architectures with service meshes, this kind of system-level control is where Kubernetes shines.
Applications that need sophisticated autoscaling based on custom metrics, blue-green deployments with traffic splitting, or integration with specialized hardware like GPUs exceed what Heroku supports. You can often run large Kubernetes workloads more cheaply than the equivalent number of Heroku dynos, but those savings only materialize if you already have the platform engineering expertise and are willing to invest in cluster operations.
Kubernetes becomes necessary when cost optimization justifies the operational overhead. Running hundreds of containers on right-sized instances costs less than equivalent Heroku dynos, but only if you account for platform engineering time.
Heroku vs Kubernetes: What to consider
A three-person startup can deploy on Heroku without hiring a dedicated DevOps engineer or having minimal DevOps knowledge. App developers handle deployments as part of their regular workflow. Kubernetes typically requires dedicated platform engineering time, whether through full-time staff or significant investment in training existing engineers. Organizations that jump to Kubernetes prematurely often discover they've traded application development velocity for infrastructure control and maintenance. Here are some things you should consider when choosing between Heroku and Kubernetes.
Ease of use and developer experience
Heroku optimizes for minimal configuration. After creating an account and installing the CLI, you can deploy a Node.js application in three steps: initialize a git repository, create a Heroku app, and push your application code. Heroku's buildpack system detects your programming language, installs dependencies listed in your package.json or requirements.txt, and starts your application using sensible defaults. Database provisioning happens through a single command that automatically injects connection credentials as environment variables.
Kubernetes requires a substantial upfront investment in understanding its architecture and make no mistake, this is a steep mountain to climb. Deploying that same Node.js application suddenly involves building a Docker image, pushing it to a container registry, writing deployment and service manifests, configuring ingress rules, setting up persistent volumes, managing secrets, understanding networking policies, and so on.
Each step branches into dozens of decisions: Which base image? How many replicas? What resource limits and requests? Should you use ClusterIP, NodePort, or LoadBalancer?
What about init containers? Readiness probes? Pod disruption budgets? These complexities compound quickly. You'll spend hours debugging why your pods are in CrashLoopBackOff, wrestling with RBAC permissions, and deciphering cryptic error messages, even getting to errors need special commands.
The learning curve doesn't end at deployment. It extends to monitoring, logging, security policies, cluster upgrades, and disaster recovery. Kubernetes is powerful, but that power comes at the cost of significant complexity that can feel overwhelming, especially when you're just trying to get an application running.
Cost structure
Heroku pricing centers on dyno hours and add-ons. Basic dynos cost $7 monthly, providing 512MB RAM suitable for development. Production dynos start at twenty-five dollars monthly per dyno for 512MB RAM, with Performance dynos ranging from $250 to $500 monthly, offering up to 14GB RAM. Add-on costs accumulate separately: Heroku Postgres starts at $5 monthly for 1GB storage, and other tools charge additional fees.
Kubernetes costs depend entirely on infrastructure choices. Self-managed clusters require compute instances: at least one control plane node plus a worker node sized for workloads.
Scaling capabilities
Heroku scales horizontally by adding dyno instances and vertically by changing dyno types. The platform handles load balancing automatically across web dynos. Heroku's native autoscaling feature is available only for Performance and higher-tier dynos. However, autoscaling can be implemented on any dyno type through third-party add-ons from the Heroku marketplace.
Kubernetes provides sophisticated scaling mechanisms for containerized applications through multiple controllers. Horizontal Pod Autoscaling adjusts replica counts based on CPU, memory, or custom metrics from Prometheus or other monitoring systems. Vertical Pod Autoscaling modifies container resource requests automatically. Cluster Autoscaling adds or removes worker nodes based on pending pods.
Security and compliance
Heroku implements security at the platform level. The platform manages SSL certificates automatically through ACM, handles OS patching, provides network isolation between applications, and maintains SOC 2, ISO 27001, and HIPAA compliance certifications, but note that HIPAA compliance is only available on certain (more-expensive) tiers.
Kubernetes security requires deliberate configuration. Network security policies restrict pod-to-pod communication and define which services can connect. Pod security standards enforce container restrictions, preventing privilege escalation.
Secrets management requires external solutions like HashiCorp Vault or cloud provider services. RBAC (Role-Based Access Control) controls who can deploy or modify cluster resources, allowing users granular permissions for different team members and allowing users to access only what they need. Security scanning tools like Falco or Trivy detect runtime threats and image vulnerabilities.
Kubernetes demands security expertise, but allows implementing exactly the controls your compliance requirements.
Rollback implementation
Heroku maintains deployment history in its release system. Rolling back executes through heroku rollback v123, restoring the previous code and configuration instantly. The platform serves the prior release immediately without building or testing.
Kubernetes rollback leverages deployment history through ReplicaSets. Each deployment creates a new ReplicaSet while preserving previous versions. The command kubectl rollout undo deployment/app reverts to the prior ReplicaSet, gradually replacing running pods. Rolling updates prevent downtime by maintaining old pods until new ones pass health checks. Rollback speed depends on pod startup time and health check configuration.
Complex Kubernetes deployments often use GitOps patterns where Git commits represent cluster state.
Monitoring and observability
Heroku provides basic metrics through its dashboard: response times, throughput, memory usage, and error rates. Application logging flows to Heroku's log aggregation system, accessible through CLI or dashboard. Advanced monitoring requires add-ons like New Relic, Datadog, or Honeybadger. Metrics retention and querying capability depend on the chosen add-on tier.
Kubernetes monitoring requires assembling components into observability stacks. Prometheus collects metrics from applications and Kubernetes components. Grafana visualizes metrics through customizable dashboards. The ELK stack (Elasticsearch, Logstash, Kibana) or Loki aggregates logs. Distributed tracing tools like Jaeger track requests across microservices. These tools run inside clusters or connect to managed services.
Error tracking
Heroku's ephemeral filesystem and limited direct server access make external error tracking essential rather than optional. Without persistent storage, application logs disappear when dynos restart or scale down. You cannot SSH into production dynos to inspect log files or reproduce errors interactively. Error tracking services become your primary window into production environments' behavior.
Honeybadger provides error tracking designed specifically for Heroku's deployment. The service offers two integration paths, through Heroku's add-on marketplace or through standalone Honeybadger accounts. Each of them is suited to different organizational structures.
Kubernetes deployments distribute your application across multiple pods, potentially running on different nodes across multiple high availability zones. Error tracking must handle this distributed nature. The same bug might generate exceptions from dozens of pod replicas simultaneously. Effective monitoring aggregates these related errors while preserving enough context to understand which pods, nodes, or deployments experienced problems.
Bridging the gap between platforms
Managed Kubernetes platforms like Google (GKE), Azure (AKS), and Amazon (EKS) offer the control that Kubernetes delivers without the operational overhead required by DevOps teams. These cloud providers handle complex infrastructure management tasks such as node provisioning, scaling, and upgrades, so developers can focus on deploying and managing applications. These services provide automated cluster management, built-in monitoring, and easy integration with CI/CD pipelines, striking a balance between ease of use and advanced configurability.
In essence, managed Kubernetes sits in the sweet spot between Heroku’s “push-to-deploy” simplicity and Kubernetes’ raw container orchestration capabilities.
In the Heroku vs Kubernetes debate, both platforms continue to evolve and narrow their gaps. Heroku has added more enterprise features like Private Spaces and an enhanced compliance feature. Kubernetes has simplified deployment tools and platforms that reduce operational burden.
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