Heroku vs AWS: which cloud platform should you choose in 2025?
When comparing Heroku vs AWS, you're asking: should I trade simplicity for control, or is there a better way?
If you're reading this, you likely have one of these concerns:
- "My Heroku bill jumped from $50 to $500/month and I need to know if AWS will save money or create new problems."
- "My team has no DevOps engineers and Heroku's limitations hurt - we can't customize scaling, access infrastructure, or run GPU workloads."
- "I need to recommend a platform to leadership but don't want to cause a budget explosion or migration disaster."
- "We're leaving Heroku because of costs. Is direct AWS our only option, or is there something simpler?"
This guide breaks down what each platform offers, where hidden costs live, and when each option fits.
We'll also show you how platforms like Northflank solve the Heroku vs AWS dilemma by combining Heroku's simplicity with deployment in your own AWS, GCP, or Azure account.
At a glance:
- Heroku: Git push deployments, minimal infrastructure management (Heroku handles OS updates, security patches, server provisioning), simple pricing that can reach $300-500/month for medium apps and $1,000-3,000+/month for high-traffic applications, managed AI services only (no direct GPU access for custom training)
- AWS: Extensive control over infrastructure configuration, can be 50-70% cheaper than Heroku at scale with Reserved Instances and optimization (requires expertise), doesn't require DevOps knowledge for basic usage but production-grade implementations need DevOps skills, full GPU access for custom workloads (H100, A100, V100) but complex to set up
- Northflank (combines both approaches): Git push deployments in your own cloud (AWS/GCP/Azure), transparent pricing, simple GPU access (H100, A100, B200) for custom training and inference, supports both AI and traditional workloads on a single platform
Bottom line: Choose Heroku for speed without DevOps staff, AWS for infrastructure control with technical expertise, or Northflank for both simplicity and control.
We'll start by looking at how Heroku differs from AWS fundamentally.
Heroku is a Platform-as-a-Service (PaaS) owned by Salesforce that runs on AWS infrastructure.
It's designed for developers who want to deploy applications without thinking about servers or configuration.
You push your code via Git, and Heroku handles everything: provisioning servers (called "dynos"), managing scaling, applying security patches, and keeping your app running.
AWS (Amazon Web Services), on the other hand, is an Infrastructure-as-a-Service (IaaS) provider with over 240 cloud services.
Unlike Heroku's "we handle everything" approach, AWS gives you raw infrastructure (servers, networking, storage, databases) and you decide how to use it.
You're responsible for choosing instance types, configuring security groups, setting up load balancers, managing updates, and orchestrating how services work together.
See the comparison table below that shows the key differences between Heroku and AWS based on their approach:
| Heroku (PaaS) | AWS (IaaS) |
|---|---|
| Deploy via Git push | Choose between EC2, ECS, EKS, Elastic Beanstalk, or Lambda |
| Runs apps in "dynos" (managed containers) | Configure and manage your own virtual servers or containers |
| Fully managed infrastructure | You manage OS updates, security patches, scaling policies |
| Built-in monitoring, logging, and add-ons marketplace | Assemble your own monitoring stack (CloudWatch, third-party tools) |
| AI capabilities: Managed Inference, MCP, pgvector for RAG | Full GPU support: P-series, G-series, H100, A100 instances |
| No GPU support for training AI models | Complete GPU access but requires Kubernetes/driver expertise |
| Limited customization by design | Unlimited customization and control |
| Gets expensive at scale | Cost-effective with optimization, but complex to manage |
Heroku is simple and predictable until it isn't. AWS is complex and variable but can be optimized heavily.
| Heroku | AWS | |
|---|---|---|
| Pricing model | Per dyno (container) | Pay-as-you-go per second |
| Starting cost | $5/month (Eco dyno) | $7.50/month (t3.micro) |
| Small production app | $135-150/month | $145-160/month (or $80-100 optimized) |
| Medium app | $300-1,000/month | $150-400/month |
| Free tier | None (removed 2022) | 12 months free for new accounts |
| Cost optimization | Limited | High (Reserved Instances save up to 72%, Spot up to 90%) |
Heroku's pricing includes convenience. AWS's lower costs require expertise. Platforms like Northflank let you deploy in your own cloud (AWS, GCP, or Azure) at provider rates while handling the complexity.
Heroku charges per dyno (container). A typical small production app runs 2 Standard dynos ($50/month), Postgres Standard-0 ($50/month), Redis Mini ($15/month), and typical add-ons for monitoring and logging ($15-30/month) for around $130-160/month total.
Dyno types:
- Eco: $5/month for 1,000 dyno hours
- Basic: $7/month per dyno
- Standard: $25-50/month per dyno
- Performance: $250-500/month per dyno
Databases: Heroku Postgres starts at $5/month but production plans are $50-500+/month.
AWS uses pay-as-you-go billing with per-second charges. A small production app typically needs 2 t3.medium instances (around $60/month if running 24/7), RDS database (around $30/month), load balancer (around $20/month), ElastiCache (around $15/month), and storage/transfer (around $20/month) - roughly $145-160/month. With Reserved Instances and optimization, this can drop to $80-100/month.
Note: These are baseline estimates. Actual costs vary significantly based on data transfer volumes, storage requirements, backup retention, and usage patterns. High-traffic applications or those with heavy data transfer can see costs increase substantially.
Common instances (On-Demand, estimated monthly cost if running 24/7):
- t3.micro: around $7.50/month
- t3.medium: around $30/month
- t3.large: around $60/month
Cost-saving options:
- Reserved Instances: up to 72% savings
- Spot Instances: up to 90% savings
- Savings Plans: up to 66% savings
Additional costs: RDS databases ($15-200+/month), load balancers ($20/month), data transfer ($0.09/GB after 100GB free), storage ($0.10/GB/month), IPv4 addresses ($3.60/month per IP).
Bottom line: Looking at these numbers, you're likely thinking: "I can't afford Heroku's premium at scale, but I don't have time to become an AWS expert either."
This is where platforms like Northflank come in - you can deploy in your own cloud account to get provider pricing while the platform handles configuration and optimization.
Pricing isn't everything. How you deploy code, handle traffic spikes, and work with each platform day-to-day shapes your team's experience.
Heroku: Push your code via Git and you're live. Run git push heroku main and Heroku detects your language, installs dependencies, builds your app, and deploys it. Zero configuration for standard frameworks. You get automatic buildpack detection, Review Apps for every pull request, and built-in CI/CD with Heroku Pipelines.
AWS: Deployment requires choosing your approach. Elastic Beanstalk is closest to Heroku but still needs more configuration. ECS/Fargate requires building Docker images, pushing to ECR, and configuring task definitions. EKS means setting up Kubernetes clusters and writing YAML manifests. Most production setups involve managing your own CI/CD pipelines.
Heroku for simplicity. AWS requires significantly more setup.
Northflank gives you Git-based deployments like Heroku but deploys to Kubernetes in your own cloud account, avoiding vendor lock-in.
Heroku: Move a slider or run heroku ps:scale web=5 to add dynos instantly. Autoscaling is available on Performance dynos and above. Simple, but you scale entire dynos without granular resource control.
AWS: Define Auto Scaling Groups, configure CloudWatch metrics, and set target tracking policies. You can scale based on CPU, memory, request count, or custom application metrics across multiple dimensions. More powerful but requires configuration.
AWS for customization. Heroku for simplicity.
Northflank gives you both - simple scaling interface with the power to configure custom autoscaling policies in your own cloud.
Heroku: Clean dashboard showing app health at a glance. Intuitive CLI with commands like heroku logs --tail. Beginner-friendly documentation. Everything lives in one place.
AWS: Console with 240+ services can feel overwhelming. CLI requires learning AWS-specific syntax. Comprehensive documentation that assumes technical knowledge. You navigate between EC2, RDS, CloudWatch, IAM, and other services constantly.
Heroku for developer happiness. AWS for control.
Northflank combines Heroku's intuitive interface with AWS-level infrastructure access.
Heroku officially supports Node.js, Ruby, Python, Java, PHP, Go, Scala, and Clojure through buildpacks. Works best with standard web frameworks.
AWS supports anything that runs on Linux with full control over runtime environments.
Both cover what most teams need. AWS handles edge cases better.
Northflank supports any language or framework that runs on Linux, with the same ease as Heroku.
Heroku: No direct GPU access for custom training. Recently added AI features (Managed Inference, MCP) use external AI services - you're not running GPU workloads on Heroku infrastructure itself.
AWS: Full GPU support with H100, A100, V100, and T4 instances. Offers Capacity Blocks for ML to reserve GPU clusters and SageMaker for managed workflows. But setting up EKS/ECS with GPU support, managing drivers, and configuring orchestration requires significant expertise.
Northflank: Full GPU access (H100, H200, A100, B200) with simple deployment. You can deploy GPU workloads as easily as regular apps - no Kubernetes expertise required. Supports both AI workloads (training, inference, notebooks) and traditional apps on the same platform, in your own cloud account.
Each platform has clear trade-offs that matter depending on your team size, expertise, and priorities. Look at the table below that sums up both the advantages and disadvantages of Heroku and AWS.
| Aspect | Heroku | AWS |
|---|---|---|
| Deployment speed | Deploy in minutes with git push | hours, days or weeks to deploy what Heroku does in minutes |
| Infrastructure management | Minimal (Heroku handles OS updates, security patches, server provisioning) | You manage everything (updates, patches, monitoring, scaling policies) |
| Developer experience | Intuitive CLI, dashboard, and documentation | Steep learning curve (EC2, VPCs, IAM, security groups) |
| Add-ons/services | One-click add-ons for databases, caching, monitoring | 240+ services (compute, storage, ML, analytics, IoT) but requires configuration |
| Review/testing | Review Apps for every pull request | Build your own preview environments |
| Cost at scale | $500-3,000/month for high-traffic apps (3-5x more than AWS) | 50-70% cheaper at scale with Reserved/Spot Instances |
| Customization | Limited (can't customize infrastructure or access underlying systems) | Complete control over infrastructure and networking |
| Vendor lock-in | High (migration requires rearchitecting) | Lower (standard infrastructure patterns) |
| GPU support | None (can't train custom AI models) | Full GPU support (H100, A100, V100, T4 for AI/ML) |
| Performance | Abstraction layer adds overhead | Direct infrastructure access, better performance |
| Cold starts | Eco/Basic tiers sleep after 30 minutes of inactivity | No cold starts on standard instances |
| Expertise required | None (designed for developers without DevOps knowledge) | Requires DevOps expertise to set up and maintain |
| Pricing complexity | Simple and predictable | Confusing (surprise bills from data transfer, idle resources) |
| Global reach | Limited to Heroku's regions | 38 regions, 120+ availability zones globally |
The middle ground: Northflank combines Heroku's deployment simplicity with AWS's infrastructure control. You get Git-based deployments, GPU access, and the ability to run in your own cloud account without needing DevOps expertise.
Here's which platform fits your situation based on team size, expertise, and requirements.
| Choose Heroku if you... | Choose AWS if you... | Choose Northflank if you... |
|---|---|---|
| Are a startup/small team without DevOps expertise | Have DevOps expertise or can hire it | Want both simplicity and infrastructure control |
| Need to ship fast (live in minutes, not weeks) | Need custom networking, specialized instances, or security configs | Need GPU access without Kubernetes complexity |
| Built a standard web app, API, or background workers | Are building microservices, multi-region, or hybrid cloud systems | Want to run both AI and traditional workloads on one platform |
| Are pre-revenue and budget isn't the primary constraint yet | Spend $1,000+/month on hosting (cost optimization matters) | Are outgrowing Heroku's costs or limitations |
| Value developer happiness over cost savings | Need GPU compute for AI/ML training | Want to avoid the AWS learning curve while getting AWS-level infrastructure |
| Want minimal operational overhead | Plan to scale to enterprise level eventually | Want Git-based deployments in your own cloud account (AWS, GCP, Azure) or need enterprise compliance and support |
If you've decided to leave Heroku, you have two main paths.
Migrating directly to AWS means rebuilding your container hosting (EC2/ECS/EKS), databases (RDS), load balancing, CI/CD pipelines, monitoring, and secrets management, a process that takes 1-2 weeks for simple apps and up to 3 months for complex ones.
The alternative is using a platform like Northflank that keeps Heroku's Git-based workflow while deploying to Kubernetes in your own cloud account (AWS, GCP, or Azure), reducing migration time from weeks to days without the AWS learning curve.
Helpful migration resources:
- How to migrate from Heroku: step-by-step guide
- Heroku migration documentation
- Heroku pricing comparison and reduction guide
The Heroku vs AWS decision comes down to matching requirements with your needs, but there's a solution that resolves this entirely.
Heroku works for speed and simplicity when you lack DevOps expertise. Good for early-stage startups and prototypes where getting live fast is the priority.
AWS works when you need infrastructure control and have the expertise to manage it. Good for complex applications with dedicated DevOps resources.
Northflank removes the choice between simplicity and control by giving you both.
You get Git-based deployments like Heroku, but running in your own cloud account (AWS, GCP, or Azure).
GPU access for AI workloads without Kubernetes complexity.
A single platform for both AI and traditional applications.
No vendor lock-in - everything runs on Kubernetes infrastructure you control.
If you're caught between Heroku's limitations and AWS's complexity, or building AI applications that need GPU access, Northflank is built for this. Try the free developer sandbox to see how it works, or if you're an enterprise with specific requirements, book a demo to discuss your setup.