Heroku
vs AWS
For teams outgrowing Heroku's managed platform or hitting cost ceilings at scale, AWS offers granular control at the expense of operational complexity. This comparison covers the dimensions that drive the migration decision.
Side-by-Side Comparison
Git push to deploy. Heroku manages build, release, and runtime automatically. Zero infrastructure configuration required — developers focus entirely on application code. Deployment pipelines with review apps and staging environments are built in.
Multiple deployment options — Elastic Beanstalk for managed deployments, ECS/EKS for containers, Lambda for serverless. Each requires infrastructure configuration, IAM roles, and networking setup. CI/CD pipelines must be built or configured using CodePipeline, GitHub Actions, or similar tools.
Dyno-based pricing starts free but scales expensively. A Performance-L dyno costs $500/month for 14GB RAM. Costs become prohibitive for multi-process architectures. Sleep behavior on free/eco tiers adds latency for low-traffic apps.
Pay-per-use pricing across a vast compute menu — EC2 instances, Fargate containers, Lambda invocations. Reserved instances and savings plans reduce costs 40-60% for predictable workloads. Cost optimization requires active management but the ceiling is far higher before costs become unreasonable.
Horizontal scaling by adding dynos via slider or CLI. Autoscaling available on Performance dynos. Vertical scaling limited to predefined dyno sizes. Simple but constrained — you cannot fine-tune CPU, memory, or network independently.
Auto Scaling Groups, ECS service auto-scaling, Lambda automatic concurrency scaling. Scale vertically to virtually any instance size. Scale horizontally with fine-grained policies based on CPU, memory, request count, or custom CloudWatch metrics. Far more flexible but requires configuration expertise.
Heroku Postgres is fully managed — automated backups, rollback, followers for read replicas, and dataclips for ad hoc queries. Limited to PostgreSQL. Connection pooling and credential rotation are handled by the platform.
RDS supports PostgreSQL, MySQL, MariaDB, Oracle, and SQL Server. Aurora adds cloud-native scaling and replication. DynamoDB for NoSQL at any scale. Full control over parameter groups, maintenance windows, and replication topology. More powerful but requires database administration knowledge.
Heroku Add-ons marketplace provides one-click provisioning for Redis, Kafka, monitoring, logging, email, and search. Around 200 add-ons available. Simple provisioning but often more expensive than direct alternatives. Third-party providers manage the infrastructure.
Over 200 AWS services covering compute, storage, databases, ML, analytics, IoT, and media. Native integration between services via IAM and VPC. No marketplace abstraction — you configure and manage services directly. The breadth is unmatched but the learning curve is steep.
Limited visibility into underlying infrastructure. No SSH access to dynos. Log drains for external log management. Metrics limited to what the Heroku dashboard provides. You trust the platform to handle runtime concerns correctly.
Full control over every infrastructure layer — VPCs, security groups, OS-level access, custom AMIs, and network architecture. CloudWatch, X-Ray, and CloudTrail provide deep observability. You own every operational decision, which means you own every operational problem.
When migrating from Heroku to AWS is worth the complexity
Migrate to AWS when Heroku's cost model no longer makes sense at your scale — typically when monthly Heroku spend exceeds $5,000-10,000 and the equivalent AWS architecture would cost 40-60% less. The inflection point arrives when you need compute configurations Heroku cannot provide, multi-region deployment for latency or compliance, or services beyond what Heroku Add-ons offer.
Stay on Heroku if your team is small, infrastructure is not a core competency, and the deployment simplicity lets your engineers focus on product rather than operations. The Heroku premium is effectively an operations team you do not have to hire. For many startups and small teams, this tradeoff remains favorable.
The migration path is not all-or-nothing. Many teams start by moving background workers or data pipelines to AWS while keeping the web tier on Heroku, gradually building AWS expertise before a full migration. Container-based deployments on ECS or EKS provide the smoothest transition since Heroku apps already run in containers.
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