Spring Boot Production Setup Checklist

Most of the Spring Boot applications run perfectly in local or in development, UAT and other environment But when it's being deployed in the Production is totally different; a lot of issues are coming, and due to that,  Production systems must handle failures, scale, security threats, memory pressure, and unpredictable traffic.

I am going to write a real-world Spring Boot production checklist most be performed before enterprise deployments.

If your app passes this checklist, it’s production-ready.

1. Environment Configuration Strategy

You should never use a single properties file for every environment, just like application.properties or application.yml file. Always use a properties file based on the number of environments like dev, uat/test and prod.

Best practice:-

Separate properties/yml file based on environment.

application-dev.yml
application-test.yml
application-prod.yml

Activate profile:

-Dspring.profiles.active=prod

Rules for production config/property file:-

• Never store secrets in your Git repository properties file.
• Use environment variables
• Externalize credentials, for example: store in Secret Manager in AWS
• Always Encrypt sensitive values

Example:

spring:
  datasource:
    password: ${DB_PASSWORD}

Production rule:

👉 Secrets must live outside of your codebase.

2. Logging & Monitoring Setup

If your production app crashes and you don’t have logs, debugging becomes guesswork. For multiple reasons, like in local or UAT, we debug with breakpoints.

In production? That luxury is gone. Logs became the only way to see what actually happened.

Due to the logs, only you can track

• Which request failed?
• What input causes the issue?
• Service or DB call broke.

Do you know the differences between Logging & Monitoring? Let me explain simply.

Logging tells the story of the past

Monitoring watches the present

You need both to survive in production. Logging is not optional, it's mandator.

Production logging checklist.

• Structured JSON logs:
• Always structure logs in a JSON format, not mandatory but recommended. I will give you the reason soon.

For example:- Instead of writing the log as a plain text log as a structured JSON format.

Instead of this way:-

 User login failed for userId=123 at 10:30

Always use in JSON format. Just giving one sample format you can use any format based on your choice.

{
  "timestamp": "2026-02-07T10:30:15",
  "level": "ERROR",
  "service": "auth-service",
  "userId": "123",
  "message": "User login failed"
}

• Centralized log aggregation:
• All logs from all applications, servers and services are collected and stored in one single place. Want to know more briefly.👉
• Production doesn't run on one server. A single user request may hit any instance.
• Correlation IDs per request:
• It's super important because we can tell easily which log belongs to which request.
• Common centralized logging tools
• ELK Stack (Elasticsearch, Logstash, Kibana)
• Splunk
• AWS CloudWatch
• Azure Monitor
• Datadog Logs
• Log levels properly configured:
• This will tell you the importance of log messages.
• We have multiple log level:
• Error
• Warn
• Info
• Debug

Monitoring essentials

Enable Spring Boot Actuator:

management:

  endpoints:

    web:

      exposure:

        include: health, metrics, info

Never expose all actuator endpoints in production.

Use:

• Prometheus
• Grafana dashboards

You should always know:

👉 CPU usage
👉 memory usage
👉 request latency
👉 error rate

3. Security Hardening

Development security is relaxed. Production security cannot be.

Minimum security checklist:

✔ HTTPS only
✔ JWT or OAuth authentication
✔ Input validation
✔ CORS restrictions
✔ Rate limiting
✔ Security headers
✔ Hide stack traces
✔ Disable default error pages

Example security config:

http

  .csrf().disable()

  .sessionManagement().sessionCreationPolicy(STATELESS);

Golden rule:

👉 Never expose internal errors to clients.

4. Database Configuration

Default database settings are not production safe.

Checklist:

✔ Hikari connection pool tuning
✔ Query timeout
✔ Index optimization
✔ Slow query logging
✔ Retry strategy
✔ Connection validation

Example:

spring:

  datasource:

    hikari:

      maximum-pool-size: 20

      connection-timeout: 30000

Database misconfiguration is one of the top causes of outages.

5. JVM & Memory Tuning

Java doesn’t auto-optimize for production.

You must control memory.

Recommended JVM flags:

-Xms512m

-Xmx512m

-XX:+UseG1GC

-XX:+HeapDumpOnOutOfMemoryError

Checklist:

✔ fixed heap size
✔ GC monitoring
✔ thread dump enabled
✔ heap dump on crash

Without heap dumps, post-crash debugging becomes nearly impossible.

6. Docker Production Setup

Containers must be optimized and secure.

Checklist:

✔ multi-stage Docker build
✔ minimal base image
✔ run as non-root user
✔ health check endpoint
✔ resource limits defined

Example base image:

eclipse-temurin:21-jre-alpine

Security rule:

👉 Never run containers as root.

7. API Reliability & Resilience

Production APIs must survive network failures.

Checklist:

✔ timeout configuration
✔ retry strategy
✔ circuit breaker
✔ graceful shutdown
✔ idempotent endpoints

Use libraries:

• Resilience4j
• Spring Retry

Example:

resilience4j:

  circuitbreaker:

    instances:

      backendA:

        failure-rate-threshold: 50

Resilience prevents cascading failures.

8. Observability & Distributed Tracing

Logs alone are not enough in microservices.

You need visibility across services.

Checklist:

✔ distributed tracing
✔ request correlation
✔ latency tracking

Use:

• OpenTelemetry
• Zipkin / Jaeger

This helps answer:

👉 Where is the slowdown happening?

---

9. Production Error Handling

Never leak stack traces to clients.

Design consistent API errors:

{

  "code": "PAYMENT_TIMEOUT",

  "message": "Service temporarily unavailable"

}

Checklist:

✔ global exception handler
✔ standard error format
✔ safe messages
✔ proper HTTP status codes

Users should never see internal implementation details.

---

10. Deployment Safety

Production deployments must be safe and reversible.

Checklist:

✔ health checks
✔ readiness probes
✔ rolling updates
✔ zero downtime deployment
✔ feature flags
✔ rollback strategy

Kubernetes example:

livenessProbe:
  httpGet:
    path: /actuator/health

Always test deployment failure scenarios.

---

Final Production Readiness Checklist

Before going live:

✔ logs centralized
✔ metrics visible
✔ secrets externalized
✔ JVM tuned
✔ DB pool tuned
✔ security hardened
✔ health endpoints working
✔ tracing enabled
✔ graceful shutdown tested
✔ load test passed

If even one box is unchecked, the system is not production ready.

Conclusion

Production readiness is not about:

“Does it run?”

It’s about:

“Can it survive failure?”

A strong Spring Boot production setup focuses on:

• resilience
• observability
• security
• performance
• recovery

If even one box is unchecked, the system is not production ready.