Spring Boot Deployment: Heroku vs AWS vs Azure - Which is Best?

Feb 28, 2026

Purpose

When I built my first Spring Boot application, I spent weeks researching deployment options. Heroku looked simple but expensive. AWS offered everything but felt overwhelming. Azure seemed enterprise-focused. I needed clarity.

This post compares the five major cloud platforms for Spring Boot deployment: Heroku, AWS, Azure, Google Cloud Platform, and DigitalOcean. I’ve deployed production Spring Boot applications on all of them, and I’ll share what I learned about pricing, ease of use, and scalability.

The key insight: no single “best” platform exists. The right choice depends on your application stage, team expertise, and budget constraints.

Quick Comparison

Let me start with a high-level overview:

Platform	Best For	Starting Price	Ease of Use	Scalability	Learning Curve
Heroku	MVPs, prototypes, small apps	$7/mo	⭐⭐⭐⭐⭐	Limited	⭐ Easiest
AWS	Enterprise, high-scale apps	~$15/mo	⭐⭐	Unlimited	⭐⭐⭐⭐⭐ Hardest
Azure	Enterprise, Microsoft stack	~$10/mo	⭐⭐⭐	High	⭐⭐⭐⭐
GCP	Kubernetes, data-heavy apps	~$6/mo	⭐⭐⭐	High	⭐⭐⭐⭐
DigitalOcean	Budget option, simplicity	$4/mo	⭐⭐⭐⭐	Moderate	⭐⭐

Heroku: Simple But Expensive

Heroku defined the developer experience for cloud deployment. When I push code to GitHub, Heroku automatically builds and deploys it. No infrastructure management. No Docker configuration. Just git push heroku main and my Spring Boot app is live.

What Makes Heroku Simple

The deployment process couldn’t be easier:

# Login and create app
heroku login
heroku create my-spring-app

# Deploy with git push
git push heroku main

# Scale horizontally
heroku ps:scale web=2

# Open in browser
heroku open

Heroku detects Spring Boot applications automatically. The only requirement is a Procfile in your project root:

web: java -Dserver.port=$PORT -jar target/app.jar

The Heroku Ecosystem

Heroku’s add-ons marketplace is impressive. Need PostgreSQL? One click. Redis for caching? One click. Monitoring with New Relic? One click. I’ve built entire production stacks without leaving the Heroku dashboard.

Spring Boot integrates with Heroku via the CloudPlatform enum:

spring:
  main:
    cloud-platform: HEROKU

This enables automatic configuration of cloud-specific features like cloud foundry environment variables.

The Cost Problem

Here’s where Heroku gets expensive:

Eco: $5/mo (sleeps after 30 minutes of inactivity)
Basic: $7/mo (512MB RAM, shared CPU)
Standard-1X: $50/mo (512MB RAM, dedicated CPU)
Standard-2X: $100/mo (1GB RAM, dedicated CPU)
Performance: $250+/mo (2.5GB+ RAM, dedicated CPU)

A production Spring Boot app with database typically costs $100-200/month on Heroku. The same workload on AWS or GCP costs $20-40/month.

When I Choose Heroku

I recommend Heroku for:

MVPs and prototypes: Deploy in under 15 minutes
Teams without DevOps expertise: Zero infrastructure management
Proofs of concept: Test ideas quickly before investing in infrastructure
Low-traffic applications: < 10,000 requests per day

I avoid Heroku for:

High-scale applications: Costs explode beyond 2-3 dynos
Performance-sensitive workloads: Limited CPU and RAM options
Complex microservices: Better options exist for orchestration

AWS: Powerful But Complex

AWS offers everything. I’ve deployed Spring Boot apps to Elastic Beanstalk, EC2, ECS, EKS, and Lambda. Each serves different use cases. This flexibility is AWS’s strength and weakness.

The AWS Services Maze

For Spring Boot deployment, AWS provides several paths:

Elastic Beanstalk (PaaS, similar to Heroku):

option_settings:
  aws:elasticbeanstalk:container:tomcat:jvmoptions:
    JVMOptions: "-Xms512m -Xmx512m -XX:MaxPermSize=128m"
  aws:elasticbeanstalk:application:environment:
    SERVER_PORT: "5000"
    SPRING_PROFILES_ACTIVE: "production"

ECS/EKS (Container orchestration):

# Dockerfile
FROM eclipse-temurin:17-jdk-alpine
COPY target/app.jar app.jar
ENTRYPOINT ["java", "-jar", "/app.jar"]

EC2 (Raw virtual machines): Full control but requires manual setup.

Lambda (Serverless): Great for event-driven Spring Boot functions.

AWS Pricing Reality

AWS pricing is complex but transparent:

EC2 t3.micro: $7.50/month (~$8.50 with tax)
Elastic Beanstalk: ~$15/month minimum (t3.micro + load balancer)
ECS: $10/month + compute costs
RDS PostgreSQL: $15-200+/month (depends on instance class)
Free Tier: 750 hours/month for 12 months

A typical production Spring Boot app on AWS:

EC2 t3.small: $15/month
RDS t3.micro: $15/month
Load balancer: $18/month
Total: ~$50/month

Compare to Heroku’s equivalent ($100-200/month), and AWS saves 50-75% at scale.

The Learning Curve Challenge

AWS complexity is real. I spent weeks learning:

VPC configuration and networking
Security groups and IAM roles
Elastic Load Balancer setup
RDS backup and failover
CloudWatch monitoring and alerts
Auto-scaling policies

Without DevOps expertise, AWS becomes a time sink. I’ve seen teams spend months building infrastructure instead of shipping features.

When I Choose AWS

I recommend AWS for:

Large-scale applications: Unlimited horizontal scaling
Enterprise workloads: Comprehensive service catalog
Cost optimization: Cheapest at high scale
Complex architectures: Microservices, event-driven systems

I avoid AWS for:

Simple applications: Overkill for basic CRUD apps
Small teams without DevOps: Steep learning curve
Rapid prototyping: Setup time measured in hours, not minutes

Azure: Enterprise Choice

Azure shines in Microsoft-centric organizations. I’ve used it for Spring Boot apps in enterprises with existing Azure AD, Office 365, and Windows Server investments.

Azure App Service

Azure App Service is the closest equivalent to Heroku:

# Create resource group
az group create --name myResourceGroup --location eastus

# Create app service plan
az appservice plan create \
  --name my-app-service-plan \
  --resource-group myResourceGroup \
  --sku B1 \
  --location eastus

# Create web app
az webapp create \
  --name my-spring-app \
  --resource-group myResourceGroup \
  --plan my-app-service-plan

# Deploy via Maven
mvn package azure-webapp:deploy

Spring Boot detects Azure App Service automatically:

spring:
  main:
    cloud-platform: AZURE_APP_SERVICE

Azure Pricing

Azure pricing is competitive with AWS:

App Service Free F1: Free (limited, 60 CPU minutes/day)
App Service Basic B1: ~$10/month
App Service Standard S1: ~$70/month
Azure SQL: $5-200+/month
AKS cluster: ~$30/month per cluster

A typical production Spring Boot stack:

App Service S1: $70/month
Azure SQL Basic: $10/month
Total: ~$80/month

The Azure Advantage

Azure excels in three areas:

Microsoft Integration: Seamless with Azure AD, DevOps, and Office 365
Hybrid Cloud: Best-in-class for on-premises + cloud setups
Enterprise Support: Direct access to Microsoft engineers

When I Choose Azure

I recommend Azure for:

Microsoft stack enterprises: Existing Azure investments
Hybrid cloud scenarios: On-premises + cloud integration
Enterprise compliance: HIPAA, SOC2, government workloads
.NET teams transitioning to Java: Familiar tooling and portal

GCP: Kubernetes Leader

Google Cloud Platform (GCP) impressed me with Cloud Run and GKE. If Kubernetes is your target, GKE sets the standard.

Cloud Run: Serverless Containers

Cloud Run is the simplest way to deploy Spring Boot:

# Deploy from source
gcloud run deploy spring-boot-app \
  --source . \
  --platform managed \
  --region us-central1 \
  --allow-unauthenticated \
  --memory 512Mi \
  --cpu 1

# Get URL
gcloud run services describe spring-boot-app \
  --region us-central1 \
  --format 'value(status.url)'

Cloud Run offers:

Free tier: 2 million requests per month
Pay-per-use: $0.40 per 1 million requests after free tier
Auto-scaling: Zero to N instances based on traffic
Cold starts: ~5 seconds for Spring Boot apps

Google Kubernetes Engine

For production Spring Boot microservices, GKE is excellent:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: spring-boot-app
spec:
  replicas: 3
  selector:
    matchLabels:
      app: spring-boot
  template:
    metadata:
      labels:
        app: spring-boot
    spec:
      containers:
      - name: app
        image: gcr.io/my-project/spring-boot-app:1.0.0
        ports:
        - containerPort: 8080
        env:
        - name: SPRING_PROFILES_ACTIVE
          value: "production"
        resources:
          requests:
            memory: "512Mi"
            cpu: "500m"
          limits:
            memory: "1Gi"
            cpu: "1000m"

GKE pricing:

Cluster management: ~$10/month per cluster
Compute costs: Pay for node instances (e.g., e2-medium: $24/month)
Cloud Run: Free tier + usage-based pricing

When I Choose GCP

I recommend GCP for:

Kubernetes deployments: GKE is the gold standard
Serverless containers: Cloud Run’s simplicity and pricing
Data-heavy applications: BigQuery, Dataflow integration
Cost-conscious scaling: Excellent auto-scaling and free tiers

DigitalOcean: Budget Option

DigitalOcean appeals to developers wanting simplicity without Heroku’s price tag. I’ve used it for side projects and small SaaS applications.

App Platform

DigitalOcean App Platform provides a Heroku-like experience:

# Install doctl
# Create app from GitHub repo
doctl apps create --spec .do/app.yaml

# app.yaml
name: spring-boot-app
services:
- name: web
  github:
    repo: yourusername/spring-boot-app
    branch: main
  run_command: java -jar target/app.jar
  environment_slug: java:17.0
  instance_count: 1
  instance_size_slug: basic-xxs
  http_port: 8080

Droplets: Simple VPS

For full control, I use Droplets (virtual machines):

Basic Droplet: $4-6/month (512MB-1GB RAM)
Standard Droplet: $12-48/month (1-8GB RAM)
Managed Database: $15/month starting

When I Choose DigitalOcean

I recommend DigitalOcean for:

Small apps and side projects: Budget-friendly hosting
Developers wanting simplicity: Flat pricing, no surprises
Startups: less than $50/month budgets
MVPs needing more than Heroku: Better performance, lower cost

Decision Framework

After deploying Spring Boot apps across all five platforms, here’s my decision matrix:

Use Heroku When:

✅ You need to deploy in < 15 minutes
✅ You have no DevOps expertise
✅ Budget > $100/month is acceptable
✅ You’re building an MVP or prototype
✅ You value simplicity over cost

Use AWS When:

✅ You need unlimited scalability
✅ You have DevOps resources
✅ You’re an enterprise or high-growth startup
✅ Cost optimization at scale is critical
✅ You need advanced services (Lambda, SQS, etc.)

Use Azure When:

✅ Your company uses Microsoft stack
✅ You need enterprise support contracts
✅ You’re doing hybrid cloud
✅ You have .NET background transitioning to Java
✅ You need Azure-specific integrations (AD, etc.)

Use GCP When:

✅ You’re deploying to Kubernetes
✅ You want modern container workflows
✅ You need data/analytics services
✅ You want competitive pricing
✅ You like Cloud Run’s serverless model

Use DigitalOcean When:

✅ You’re a small team or startup
✅ Budget is less than $50/month
✅ You want Heroku-like simplicity but cheaper
✅ You don’t need advanced enterprise features
✅ You want predictable pricing

Feature Comparison

Feature	Heroku	AWS	Azure	GCP	DigitalOcean
Setup Time	5-10 min	1-2 hours	30-60 min	30-60 min	10-20 min
Git Deploy	✅ Native	Via Elastic Beanstalk	Via App Service	Via Cloud Run	✅ Native
Auto-scaling	✅ Manual/Manual	✅ Auto	✅ Auto	✅ Auto	Limited
Custom Domains	✅ Free	✅ Free	✅ Free	✅ Free	✅ Free
SSL Certs	✅ Auto (Let’s Encrypt)	✅ ACM Manager	✅ App Service Certs	✅ Managed Certs	✅ Let’s Encrypt
Managed DB	✅ Postgres, Redis, Mongo	✅ RDS, ElastiCache	✅ Azure SQL, Cosmos	✅ Cloud SQL	✅ Postgres, Redis
CI/CD	✅ Pipelines	✅ CodePipeline	✅ Azure DevOps	✅ Cloud Build	✅ App Platform
Monitoring	Basic logs	✅ CloudWatch	✅ Monitor	✅ Cloud Monitoring	Basic metrics
Free Tier	❌ No (Eco sleeps)	✅ 12 months	✅ 12 months	✅ Always free	❌ Trial only
Kubernetes	❌ No	✅ ECS/EKS	✅ AKS	✅ GKE (best)	✅ DOKS
Serverless	❌ No	✅ Lambda	✅ Functions	✅ Cloud Run (best)	❌ No

Cost Comparison by Scale

Real-world pricing for identical Spring Boot applications:

MVP/Low Traffic (< 1,000 requests/day)

Heroku Basic: $7/month
AWS t3.micro: $8.50/month
Azure B1: $10/month
GCP Cloud Run: $0-5/month (free tier covers most)
DigitalOcean: $4-5/month
Winner: DigitalOcean ($4/month)

Growing App (10,000 requests/day)

Heroku Standard 2X: $100/month (2 dynos)
AWS t3.small + RDS: $35/month
Azure S1: $70/month
GCP Cloud Run: $20-30/month
DigitalOcean App Pro: $12/month
Winner: DigitalOcean ($12/month) or GCP ($20-30/month)

Production (100,000+ requests/day)

Heroku Performance M: $500/month (2 dynos)
AWS t3.medium + RDS: $80/month
Azure S2: $140/month
GCP Cloud Run: $60-80/month
DigitalOcean: $40-80/month
Winner: AWS or GCP ($60-80/month)

High Scale (1M+ requests/day)

Heroku: $2,000-5,000/month (very expensive)
AWS: $300-800/month (with auto-scaling)
Azure: $400-1,000/month
GCP: $250-600/month
DigitalOcean: $500-1,500/month (limited scaling)
Winner: AWS or GCP (best price/performance)

Production Checklist

Before deploying Spring Boot to any cloud platform:

Security:

SSL/HTTPS enabled
Database credentials in environment variables
API keys in secret management (not code)
Security groups/firewalls configured
Dependency scanning enabled

Performance:

Database connection pooling configured (HikariCP)
Caching layer (Redis) for hot data
CDN for static assets
Database indexes optimized
JVM heap size tuned (-Xms512m -Xmx512m)

Monitoring:

Spring Actuator endpoints exposed (/actuator/health, /actuator/metrics)
Application performance monitoring (APM)
Error tracking (Sentry, Rollbar)
Log aggregation (cloud provider logs)
Uptime monitoring

Reliability:

Automated backups configured
Health check endpoints configured
Graceful shutdown enabled (server.shutdown=graceful)
Auto-scaling rules configured
Disaster recovery plan documented

Summary

The “best” cloud platform for Spring Boot deployment depends on your application stage:

Early Stage (Idea → MVP): Start with Heroku or DigitalOcean for fastest time-to-market. Focus on shipping features, not infrastructure.

Growth Stage (MVP → Product-Market Fit): Migrate to GCP Cloud Run or AWS Elastic Beanstalk for better cost-to-performance ratio as traffic grows.

Scale Stage (Product-Market Fit → Enterprise): Move to AWS EKS or GCP GKE for Kubernetes. Maximum control, scalability, and cost optimization.

Enterprise: Choose AWS or Azure based on existing ecosystem. Enterprise support, compliance, and hybrid capabilities matter more than pricing.

The key takeaway: Start simple, migrate as you scale. Don’t over-engineer infrastructure before you have traffic. I’ve seen teams spend months building Kubernetes clusters for apps with 100 users. Deploy to Heroku or DigitalOcean first. Scale when you need to.

Which platform are you using for your Spring Boot app? Share your experience in the comments below.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Spring Boot Cloud Platforms

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!