Building Scalable .NET APIs: Patterns That Actually Work in Production

When building APIs that need to handle thousands of concurrent requests, architecture decisions made early on determine your ceiling. At Bracha Group, we've refined our approach over years of production deployments.

The Problem with Traditional Layered Architecture

Most .NET tutorials teach a layered approach: Controllers, Services, Repositories. While this works for small projects, it creates coupling that slows teams down as the codebase grows. Every feature touches every layer, and changes ripple unpredictably.

Vertical Slice Architecture

We've moved to a vertical slice approach where each feature is self-contained. A "Create Order" feature has its own request model, handler, validator, and response — all in one folder. This means:

• Faster development: New features don't require changes across multiple layers
• Easier testing: Each slice can be tested in isolation
• Simpler onboarding: New developers understand features by reading one folder

CQRS Where It Matters

We don't apply CQRS everywhere — that's over-engineering. Instead, we use it where read and write patterns genuinely differ. For example, an e-commerce product catalog might have complex read queries with caching, while writes go through a strict validation pipeline.

Entity Framework Core Best Practices

After years of production use, here are our non-negotiable EF Core rules:

• Always use projections for read queries — never return full entities from GET endpoints
• Explicit loading over lazy loading — predictable query patterns matter
• Compiled queries for hot paths — the performance gain is real
• Separate read and write DbContexts when using CQRS

Middleware Pipeline Design

Your middleware order matters more than you think. We follow a strict pipeline: Exception Handling, Request Logging, Authentication, Authorization, Rate Limiting, then your endpoint. Getting this wrong leads to security gaps and confusing error responses.

Conclusion

The best API architecture is one your team can maintain and extend confidently. Start simple, measure everything, and evolve based on real production data — not theoretical purity.