When organisations begin reviewing their Heroku footprint, they often consider Amazon Web Services as the first destination. It provides long-term infrastructure control, flexible scaling, and a mature ecosystem. However, moving from Heroku to AWS goes beyond a simple migration exercise. It represents a fundamental shift in the operating model.
Heroku is a highly abstracted platform. As a result, it hides networking, scaling complexity, and infrastructure management behind a clean developer experience. In contrast, AWS gives you direct control over those layers. Therefore, instead of abstracting the underlying systems, AWS expects you to configure and manage them yourself. That control is powerful, but it comes with responsibility. The migration therefore becomes less about replacing services and more about designing infrastructure intentionally.
The comparison below is based on the standard service matches explained in the Heroku to AWS migration guide, but the real value comes from understanding what changes in the system design.
Application Runtime: Replacing Dynos
On Heroku, applications run on dynos. You scale them horizontally, Heroku handles orchestration, and infrastructure remains largely invisible. When moving to AWS, you have several options depending on the level of control your team wants.
| Heroku Service / Feature | AWS Equivalent | Notes / Migration Considerations |
| Dynos | Amazon ECS with Fargate | Closest experience to dynos. Serverless container runtime. |
| AWS Elastic Beanstalk | Simplified PaaS-style deployment. | |
| Amazon EC2 | Full infrastructure control with higher management overhead. | |
| Amazon EKS | Kubernetes-based container orchestration. |
For most Heroku workloads, Amazon ECS with Fargate provides the smoothest transition. It allows container-based deployment without managing servers directly. Elastic Beanstalk can work well for teams seeking simplicity, while EC2 or EKS suit organisations that require deeper operational control. The key shift here is that you now design your deployment model rather than relying on Heroku’s abstraction.
Database Migration: Heroku Postgres to AWS
Heroku Postgres maps cleanly to AWS managed database services, but the migration still requires planning, especially around downtime and schema dependencies.
| Heroku Service | AWS Equivalent | Migration Notes |
| Heroku Postgres | Amazon RDS for PostgreSQL | Direct managed PostgreSQL replacement. |
| Amazon Aurora PostgreSQL | Higher-performance alternative with optional serverless scaling. |
Smaller databases can often be migrated using pg_dump and restore processes. For production systems requiring minimal downtime, AWS Database Migration Service (DMS) enables replication-based migration. While the database move itself is usually straightforward, it is important to assess how tightly application logic depends on the existing schema. Infrastructure migration does not automatically reduce application coupling.
Redis, Messaging and Streaming Services
Many Heroku add-ons have direct AWS equivalents, which simplifies technical mapping.
| Heroku Add-on | AWS Equivalent | Notes |
| Heroku Redis | Amazon ElastiCache for Redis | Managed Redis with clustering and scaling options. |
| Heroku Kafka | Amazon MSK | Managed Kafka service. |
| Heroku RabbitMQ | Amazon MQ | Managed RabbitMQ. |
| Amazon SQS + SNS | Alternative for simplified messaging needs. |
Redis migrations typically involve minimal friction. Messaging and streaming services can also create opportunities to simplify architecture, especially when teams modernize legacy patterns during the transition.
Environment Variables and Secrets Management
Heroku’s Config Vars need to be replaced with structured configuration management.
| Heroku Feature | AWS Equivalent |
| Config Vars | AWS Systems Manager Parameter Store |
| AWS Secrets Manager |
Parameter Store works well for standard configuration values, while Secrets Manager supports encryption and rotation for credentials. This is often a good moment to formalise secret governance instead of continuing informal environment-based configuration patterns.
CI/CD and Build Process
Heroku buildpacks and slug compilation do not translate directly to AWS. Teams usually transition to container-based builds.
| Heroku Feature | AWS Equivalent |
| Buildpacks | Dockerfiles |
| Heroku Pipelines | AWS CodePipeline + CodeBuild |
| GitHub Actions with AWS integration |
Containerising applications improves portability and aligns with modern deployment models. The build pipeline becomes more explicit, and deployment stages require clearer orchestration design.
AWS Networking Design in a Heroku to AWS Migration
Networking is where the migration becomes architectural rather than mechanical. Heroku abstracts networking almost entirely. AWS requires explicit design of networking boundaries.
| Heroku Concept | AWS Equivalent | Design Consideration |
| Default Networking | Amazon VPC | Define subnets, CIDR blocks, routing tables. |
| Private Spaces | VPC with private subnets | Stronger, customisable isolation. |
| Router | Application Load Balancer | HTTP/HTTPS routing and SSL termination. |
| VPN Connection | AWS Site-to-Site VPN | Hybrid connectivity options. |
| IP Allowlisting | NAT Gateway with Elastic IP | Static outbound IP control. |
Designing your Virtual Private Cloud properly is critical. Public subnets host load balancers, while private subnets isolate application and database resources. Security Groups and Network ACLs must be defined intentionally. This added design responsibility increases operational maturity but also provides stronger compliance, isolation, and hybrid integration capabilities.
Logging, Monitoring and Observability
Heroku provides simplified logging. AWS offers deeper observability tooling, but configuration becomes your responsibility.
| Heroku Feature | AWS Equivalent |
| Logging | Amazon CloudWatch Logs |
| Metrics | Amazon CloudWatch |
| Tracing | AWS X-Ray |
AWS allows more granular insight into performance and infrastructure behaviour. However, observability must be architected, not assumed.
Scheduled Tasks and Background Jobs
Heroku Scheduler equivalents are typically implemented using AWS-native scheduling services.
| Heroku Feature | AWS Equivalent |
| Heroku Scheduler | Amazon EventBridge + AWS Lambda |
| ECS Scheduled Tasks |
Many organisations modernise background jobs during migration by adopting serverless functions for cost efficiency and scalability.
Cost Considerations
Heroku bundles abstraction into platform pricing. AWS pricing is granular and usage-based. Compute, database, networking, and load balancers are billed separately. When architected carefully, many organisations achieve meaningful cost optimisation at scale. However, cost discipline requires governance and monitoring. AWS does not enforce simplicity; it enables flexibility.
Heroku to AWS Migration Strategy: Avoiding Lift-and-Shift
A thoughtful migration usually follows a phased approach rather than a single cutover. Teams containerise applications first. They then define networking and IAM structures. Next, they replicate and transition databases. After that, they deploy applications in parallel using blue-green or canary strategies before redirecting traffic. This approach reduces downtime risk and creates space to modernize where appropriate.
The biggest mistake teams make is assuming migration alone modernises architecture. Rehosting changes infrastructure ownership. It does not automatically change integration patterns or technical debt.
Integration and Data Synchronisation in a Heroku to AWS Migration
If you are already a MuleSoft customer, the Heroku to AWS migration presents a strategic opportunity. Many Heroku environments were not simply hosting applications; they were running integration logic, synchronising Salesforce data, orchestrating PostgreSQL workflows, or acting as middleware layers.
When moving to AWS, you can choose to recreate that integration logic inside application services again, or you can centralise it within MuleSoft. For organisations that already license MuleSoft, this can be an economically efficient approach because it leverages an existing strategic integration platform instead of expanding custom middleware on new infrastructure.
In that model, AWS becomes the hosting foundation, while MuleSoft becomes the governed integration backbone. This separation reduces duplicated retry logic inside application code, strengthens monitoring and governance, and improves API reuse. Migration then becomes more than a hosting decision; it becomes an opportunity to mature integration architecture without increasing platform cost.
Infrastructure migration moves your workloads. Integration strategy determines how well your systems scale and evolve.
If you are beginning to review your Heroku strategy, a structured assessment can help you move forward with confidence. At NJC Labs, we offer a focused Heroku Modernization Assessment designed specifically for organizations navigating this transition, providing clear visibility into your current architecture, integration dependencies, and practical next steps aligned to your long-term cloud and integration strategy.