Technology comparisons
Spring Boot vs .NET
Spring Boot vs .NET requires decisions about runtime model, type system, libraries, team experience, workload, deployment and long-term maintenance. This guide explains the architecture, delivery and production practices needed to achieve a stack comparison scored against your product and hiring constraints.
Compare against the same workload
Define request patterns, data volume, latency target, team experience, deployment environment and required libraries before comparing technologies. Synthetic benchmarks without the product workload rarely predict delivery cost or reliability.
Score implementation speed, maintainability, security, observability, hiring and migration—not only throughput. Prototype the riskiest integration and choose the option the team can operate for several years.
Use Spring Boot modules around business capabilities
Organize code by domains such as identity, billing or fulfillment rather than placing every controller, service and repository in global folders. Keep transaction boundaries and dependencies explicit so modules can change without reaching through one another.
Start with a modular monolith unless independent deployment solves a measured team or scaling problem. Spring Boot already provides production conventions; adding distributed services too early multiplies configuration and failure modes.
Secure Spring Boot with explicit authorization
Configure Spring Security with a deny-by-default filter chain and test public, authenticated and privileged routes. Validate token issuer, audience and expiry, then enforce resource ownership or method authorization instead of trusting roles sent by a client.
Define CORS precisely, decide whether CSRF applies to the authentication model, keep secrets outside source control and avoid exposing sensitive Actuator endpoints. Return safe errors and log security events without recording credentials or tokens.
Measure Spring Boot in production
Spring Boot Actuator and Micrometer can expose request latency, error rates, JVM behavior and custom business metrics. Add trace or request identifiers so a user-facing failure can be followed through controllers, database calls and external dependencies.
Set service-level targets before tuning. Profile CPU and allocations, inspect slow queries and load test with production-like data; cache or concurrency changes should respond to a measured bottleneck.
Build a repeatable Spring Boot deployment
Create an immutable artifact or multi-stage container image, run as a non-root user and inject environment configuration at runtime. Separate liveness from readiness so traffic does not reach the service before dependencies and migrations are ready.
Automate deployment promotion, database migration and rollback. Use a secret manager, least-privilege service identity, centralized logs, metrics, backups and tested restoration in every production environment.