# Heroku vs AWS in 2026: Which Cloud Platform Is Better?

> Compare Heroku and AWS for backend deployment, pricing, scaling, pipelines, and rollbacks. See which platform fits your application and team needs in 2026.
- **Author**: parth-kanpariya
- **Published**: 2025-07-20
- **Modified**: 2026-07-27
- **Category**: Alternatives
- **URL**: https://kuberns.com/blogs/heroku-vs-aws/

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Heroku is the better choice when you want a managed Platform as a Service and a straightforward application deployment workflow. AWS is better when you need broader infrastructure choices, deeper control, or access to a large ecosystem of cloud services.

The comparison is not completely one-to-one. Heroku is a managed application platform, while AWS is a cloud ecosystem. AWS Elastic Beanstalk and App Runner are closer to Heroku's deployment model. ECS with Fargate supports containerized services, while EC2 gives you virtual machines that your team configures and manages.

For backend deployment, the right choice depends on how much infrastructure responsibility your team wants, which services the application needs, and whether developer time or infrastructure flexibility matters more.

## TL;DR: Heroku vs AWS

* **Choose Heroku** when you want managed dynos, established deployment conventions, integrated data services, and minimal server administration.
* **Choose AWS** when you need service-level flexibility, custom networking, infrastructure control, or close integration with other AWS products.
* AWS Elastic Beanstalk and App Runner are closer to Heroku than EC2 is.
* Heroku pricing is easier to understand at the plan level. AWS pricing depends on the services, region, resource sizes, traffic, storage, and architecture you select.
* Heroku Pipelines provides an application-focused release workflow. AWS CodeDeploy provides configurable deployment strategies across eligible AWS compute services.
* Neither platform is always cheaper. Compare the complete application and the engineering work required to operate it.

For a deeper explanation of dynos and Heroku's platform model, see [what Heroku is and how it works](https://kuberns.com/blogs/what-is-heroku/).

## Heroku vs AWS Comparison Table

| Category | Heroku | AWS |
| --- | --- | --- |
| Platform model | Managed PaaS | Cloud ecosystem with managed and infrastructure services |
| Closest deployment services | Heroku dynos and Pipelines | Elastic Beanstalk, App Runner, ECS/Fargate |
| Server-level option | Not exposed in the standard PaaS workflow | EC2 |
| Best for | Teams prioritizing developer experience and platform conventions | Teams requiring cloud-service breadth and infrastructure control |
| Deployment | Git, GitHub, container registry, CLI, and Pipelines | Service-specific workflows using tools such as CodePipeline and CodeDeploy |
| Scaling | Change dyno type or quantity; eligible tiers support autoscaling | Service-specific scaling through App Runner, Auto Scaling, ECS, Lambda, or other services |
| Databases | Heroku Postgres and marketplace services | RDS, Aurora, DynamoDB, ElastiCache, and other services |
| Storage | Ephemeral dyno filesystem; external persistent storage | EBS, EFS, S3, and service-specific storage |
| Pricing | Published dyno and managed-service plans, generally prorated | Usage-based and architecture-specific |
| Operational responsibility | Heroku manages the platform and underlying runtime | Varies from managed App Runner to user-managed EC2 |

The most important difference is responsibility. Heroku offers a consistent application platform. AWS lets teams choose how much of the platform they want AWS to manage and how much they want to design themselves.

## Complete Heroku vs AWS Comparison to Help You Make a Better Decision

AWS is not one application deployment product. This comparison uses Elastic Beanstalk and App Runner for managed deployment, ECS with Fargate for containers, and EC2 for server-level control.

### Backend deployment experience

Heroku provides one opinionated workflow around applications, releases, dynos, configuration variables, add-ons, and Pipelines. AWS provides multiple valid workflows, so teams must first select the services that match the application.

That difference affects setup time. Heroku reduces the number of infrastructure decisions before the first deployment. AWS can offer more architectural flexibility, but the team must choose and configure the required compute, networking, identity, storage, database, monitoring, and deployment services.

#### Heroku deployment workflow

Heroku can detect supported buildpacks, build the application, create a release, and run its processes in dynos. Teams can also deploy container images. A Procfile is useful when an application needs explicit process types, but it is not required for every supported application.

A typical workflow includes:

1. Connect a repository or push code through Git.
2. Configure required environment variables.
3. Select dyno types and process quantities.
4. Attach data services or add-ons.
5. Deploy and monitor the release.

Heroku manages the underlying operating system, routing, container scheduling, and platform maintenance. Developers still make application-level decisions about process types, scaling, add-ons, and resource sizes.

![Heroku application deployment workflow](https://kuberns-blogs-media.s3.ap-south-1.amazonaws.com/heroku-complexity.png)

#### AWS deployment workflow

AWS deployment depends on the selected service:

* **Elastic Beanstalk** accepts application code, creates an environment, and provisions AWS resources such as EC2 instances. AWS does not charge separately for Elastic Beanstalk, but it charges for the underlying resources.
* **App Runner** builds from source code or deploys container images and manages the running web service.
* **ECS with Fargate** runs containers without requiring teams to manage EC2 hosts, but teams configure clusters, task definitions, services, networking, and related permissions.
* **EC2** gives teams virtual machines and the most direct server control. The team manages the operating system, runtime, security, process manager, deployment automation, and patching.

![AWS CodeDeploy deployment workflow](https://kuberns-blogs-media.s3.ap-south-1.amazonaws.com/aws-codedeploy.png)

AWS can therefore be relatively managed or highly configurable. The flexibility is valuable, but comparing Heroku with "AWS" without naming the deployment service produces an incomplete answer.

#### Heroku Pipelines vs AWS CodeDeploy: deployment time and rollbacks

Heroku Pipelines organizes applications into stages such as review, staging, and production. Teams can promote a tested build between pipeline stages. Heroku also records releases, and a rollback creates a new release using the slug and configuration from an earlier release.

AWS CodeDeploy automates deployments to supported compute platforms, including EC2, Lambda, and Amazon ECS. Its behavior depends on the target and deployment configuration. Teams can configure options such as in-place or blue/green strategies where supported, deployment groups, health checks, CloudWatch alarms, and automatic rollback conditions.

| Workflow | Heroku Pipelines | AWS CodeDeploy |
| --- | --- | --- |
| Initial setup | Application-focused pipeline configuration | Requires application, target, deployment group, permissions, and strategy |
| Release promotion | Promote builds between pipeline stages | Deploy revisions through configured AWS targets |
| Rollback | Create a new release from an earlier Heroku release | Automatic or manual rollback based on deployment configuration |
| Blue/green deployment | Platform workflow depends on application setup | Supported for eligible ECS, EC2, and Lambda deployments |
| Operational control | More opinionated | More configurable |

Neither platform has one universal deployment time. Build duration, image size, health checks, instance replacement, traffic shifting, migrations, and rollback strategy all affect how long a release takes.

Heroku requires fewer deployment decisions for the workflows compared here. CodeDeploy offers more control over rollout behavior, but that control requires more configuration. AWS states that CodeDeploy does not add a separate charge for deployments to EC2, although the underlying AWS resources still incur charges.

### Heroku vs AWS pricing

Heroku presents named monthly plans for dynos and managed data services. Most dynos and add-ons are prorated based on active time, with the listed monthly price generally acting as a maximum for continuous use.

AWS prices each selected service separately. The bill can include compute, memory, load balancing, storage, databases, data transfer, monitoring, IP addresses, build time, and other services. Prices also vary by region.

This means there is no accurate universal answer to "How much does AWS cost?" without defining the architecture.

#### Entry-level backend

| Platform | Example | Published price |
| --- | --- | ---: |
| Heroku | 1 Basic dyno with 0.5 GB memory | Up to $7/month |
| AWS App Runner | AWS low-traffic example | Approximately $4.80/month |
| AWS Elastic Beanstalk | Beanstalk service fee | $0, plus underlying AWS resources |

AWS's published App Runner example assumes a 1 vCPU, 2 GB application that handles requests for two hours each day and keeps provisioned memory available. It is not a performance-equivalent comparison with a Heroku Basic dyno, but it demonstrates how App Runner pricing changes with active compute time.

#### Production web application

| Platform | Example | Published price |
| --- | --- | ---: |
| Heroku | 1 Standard-1X dyno with 0.5 GB memory | Up to $25/month |
| AWS App Runner | AWS lightweight API example | Approximately $25.50/month |
| AWS Elastic Beanstalk | Managed environment | Underlying EC2, load balancer, storage, transfer, and related resources |

AWS's App Runner example assumes a 1 vCPU, 2 GB service receiving sporadic requests for eight hours per day. Again, this is an official billing example rather than a claim that the resources perform identically.

A production Heroku application may also need a separate worker dyno, database, key-value store, and add-ons. An AWS application may need additional services such as a load balancer, RDS, CloudWatch, NAT Gateway, or data transfer. Compare the complete architecture rather than one compute line.

For current Heroku plan details, see the [complete Heroku pricing breakdown](https://kuberns.com/blogs/heroku-pricing-explained/).

#### Application with a managed database

A small Heroku application using one Basic dyno and Essential-0 Postgres has a maximum published price of $12 per month before other services:

| Heroku resource | Maximum monthly price |
| --- | ---: |
| Basic dyno | $7 |
| Essential-0 Postgres | $5 |
| **Total** | **$12** |

On AWS, the equivalent architecture depends on whether the application uses App Runner, Elastic Beanstalk, ECS, or EC2 and which RDS or Aurora configuration it requires. Database engine, instance type, storage, backups, availability, data transfer, and region all affect the result.

AWS can be cost-effective when a team right-sizes and optimizes those services. It can also become expensive when resources such as load balancers, NAT Gateways, databases, idle instances, logs, or data transfer are overlooked. Use the AWS Pricing Calculator for the actual architecture instead of comparing only EC2 with a Heroku dyno.

## Which Platform Should You Choose?

### Choose Heroku when

Heroku is a better fit when:

* Your team wants a consistent managed PaaS.
* Fast application-level deployment matters more than server-level control.
* The application fits Heroku's runtime and add-on model.
* You want dynos, releases, Pipelines, and managed data services in one platform.
* Your team does not want to assemble multiple cloud services.

Heroku can still be useful for prototypes, internal applications, and production workloads that fit its conventions. The decision should be based on the workload and platform roadmap rather than a blanket claim that Heroku cannot scale.

If Heroku's pricing, platform limits, or deployment model no longer fits your application, compare the [top Heroku alternatives in 2026](https://kuberns.com/blogs/heroku-alternatives/) before choosing a replacement.

### Choose AWS when

AWS is a better fit when:

* The application needs specific AWS services or custom networking.
* Your team needs control over compute, storage, identity, security, or deployment strategy.
* You have the cloud and operational expertise to design and maintain the architecture.
* The workload benefits from services such as ECS, Lambda, RDS, DynamoDB, S3, or CloudFront.
* You need more deployment models than a traditional PaaS provides.

The operational burden depends on the service. App Runner is more managed than EC2, while ECS, EKS, and multi-service architectures require more design and configuration.

If your team wants cloud deployment without the breadth or operational complexity of the AWS ecosystem, review these [AWS alternatives for application hosting](https://kuberns.com/blogs/best-aws-alternatives-for-cheaper-cloud-hosting/).

### Moving from Heroku to AWS

Moving from Heroku to AWS is not simply replacing a dyno with an EC2 instance. Teams must map:

* Web and worker dynos to the chosen AWS compute services
* Heroku Postgres to RDS, Aurora, or another database
* Heroku Key-Value Store to ElastiCache or another service
* Config vars and secrets to the selected AWS configuration service
* Add-ons to AWS or third-party equivalents
* Releases and Pipelines to the chosen AWS delivery workflow
* Logs, metrics, alerts, networking, domains, and certificates

AWS provides more architectural choice, but migration planning should include operational ownership and not only resource prices.

## Kuberns: the Best Alternative to Heroku and AWS

[Kuberns](https://kuberns.com/) is an Agentic AI platform for deployment designed for teams that want to deploy applications without assembling AWS services or managing a traditional dyno workflow.

After you connect a GitHub repository, Kuberns analyzes the application and prepares its deployment configuration. CI/CD, monitoring, and alerts are included, and there is no per-user pricing.

Plans start at $10, a trial option is available, and bundle packs provide additional savings.

![Kuberns Agentic AI platform for deployment](https://kuberns-blogs-media.s3.ap-south-1.amazonaws.com/kuberns-home-page-new.png)

### Where Kuberns fits

Kuberns is most relevant when the team wants less deployment and infrastructure configuration. It is not positioned as a replacement for every AWS service. Teams that require direct operating-system access, specialized AWS architectures, or granular infrastructure control may still prefer AWS.

| Consideration | Heroku | AWS | Kuberns |
| --- | --- | --- | --- |
| Model | Managed PaaS | Cloud ecosystem | Agentic AI platform for deployment |
| Deployment approach | Platform conventions and dynos | Select and configure AWS services | Connect a repository and automate deployment configuration |
| Server administration | Managed by Heroku | Depends on service | Deployment infrastructure automated |
| Starting price | Basic at $7 | Architecture-specific | Starts at $10 |
| CI/CD | Heroku Pipelines and related resources | Configure selected AWS delivery services | Included |
| Monitoring and alerts | Platform features and add-ons | Configure CloudWatch and related services | Included |
| Per-user pricing | Account or enterprise terms can vary | Infrastructure is not generally billed per console user | No per-user pricing |
| Savings option | Right-size dynos and add-ons | Right-size and optimize selected services | Bundle packs provide additional savings |
| Trial or free option | No permanent free runtime tier | AWS offers service-specific free usage and credits subject to current terms | Trial Option available |

For teams considering a move, the [Heroku migration guide](https://kuberns.com/blogs/migrate-from-heroku/) explains the application-level preparation involved.

## Conclusion: Heroku, AWS, or Kuberns?

Heroku and AWS provide mature cloud capabilities, but teams can still face dyno planning, service selection, infrastructure configuration, and architecture-dependent billing as applications grow.

For a more unified deployment workflow, evaluate [Kuberns](https://kuberns.com/pricing). Kuberns is an Agentic AI platform for deployment that automates deployment configuration and includes CI/CD, monitoring, and alerts. Pricing starts at $10, there is no per-user pricing, and bundle packs provide additional savings.

<a href="https://dashboard.kuberns.com" target="_blank" rel="noopener noreferrer">
  <img src="https://kuberns-blogs-media.s3.ap-south-1.amazonaws.com/CTA_banner.png" alt="Deploy your application with Kuberns" />
</a>

## Frequently Asked Questions

### What is the main difference between Heroku and AWS?

Heroku is a managed Platform as a Service built around application deployment and dynos. AWS is a broad cloud ecosystem that includes managed deployment services, containers, serverless computing, virtual machines, databases, networking, and many other infrastructure products.

### Should I compare Heroku with EC2, Elastic Beanstalk, or App Runner?

Elastic Beanstalk and App Runner are closer to Heroku because they provide managed application deployment workflows. Compare Heroku with EC2 only when your team is considering direct virtual-machine control and is prepared to manage the operating system, runtime, security, and deployment process.

### Which platform is easier for a small backend team?

Heroku is generally easier when a small team wants an opinionated deployment workflow with less infrastructure configuration. AWS can be a better fit when the team already has AWS expertise or needs services and controls that Heroku does not provide.

### How does application scaling differ between Heroku and AWS?

Heroku scales applications by changing dyno types or quantities, with autoscaling available on eligible tiers. AWS scaling depends on the selected service and may use App Runner scaling, EC2 Auto Scaling, ECS service scaling, Lambda concurrency, load balancers, and related configuration.

### What must change when migrating an application from Heroku to AWS?

The team must map dynos, buildpacks, Procfile process types, configuration variables, data services, domains, certificates, logs, metrics, and release workflows to the selected AWS architecture. The amount of change depends on whether the target is App Runner, Elastic Beanstalk, ECS, EC2, or another AWS service.

### When should a team consider Kuberns instead of Heroku or AWS?

Consider [Kuberns](https://kuberns.com/) when the goal is to reduce deployment and infrastructure configuration without assembling multiple AWS services. Kuberns is an Agentic AI platform for deployment with CI/CD, monitoring, and alerts included, usage-based pricing starting at $10, no per-user pricing, and bundle packs for additional savings.

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