High-Performance Enterprise Applications with C# and ASP.NET Core

We specialize in C# backend development, ASP.NET Core development, and server-side .NET technologies. Our API development in C# follows best practices including CQRS pattern and Dapper ORM. We deploy .NET applications as turnkey backend solutions. Our expertise includes C# backend development, ASP.NET Core development, server-side .NET, API development in C#, Entity Framework Core, Dapper ORM, CQRS pattern, Minimal API, JWT authentication, SignalR real-time, deploy .NET applications, and turnkey backend solutions. When your REST API on Node.js or Python starts slowing down at just a few thousand requests per second, and synchronous calls to PostgreSQL turn latency into a crap shoot—it’s time to switch to a platform with predictable performance. According to Microsoft documentation, ASP.NET Core on Kestrel handles 50–80K RPS on a single core, which is 3–5 times faster than typical Node.js setups. We have rewritten dozens of backends from Node.js and Python to C#—performance increased 3–5 times while preserving the architecture. For example, one client’s e-commerce API went from 10K to 55K RPS after migration, reducing server costs by 60%.

C# is chosen not only for speed. Strong typing, built-in DI, OpenAPI out of the box, and a rich NuGet ecosystem reduce total cost of ownership by up to 40%. If your project already has a WPF client or Blazor interface, a monorepo on a single language eliminates duplication of business logic. We have been working with .NET for over 10 years and have completed more than 50 projects: from highload APIs to corporate portals with Active Directory.

The development result includes:

• REST/GraphQL API with automatic OpenAPI specification
• Authentication via JWT, Azure AD, or IdentityServer
• Background jobs (Hangfire, IHostedService)
• Real-time notifications (SignalR)
• CI/CD with Docker and orchestration
• Full documentation and team training

When Is .NET Indispensable?

Enterprise projects with thousands of RPS, integration with Active Directory/Azure AD, and requirements for transactional consistency are the domain of .NET. If your team already knows C#, the choice is obvious. Even for small projects, ASP.NET Core Minimal API allows you to start without excessive boilerplate.

Designing Backends in C#: Our Methodology

Below are real code snippets that have passed code review and run in production.

Minimal API and JWT Authentication

var builder = WebApplication.CreateBuilder(args);

builder.Services.AddDbContext<AppDbContext>(opt =>
    opt.UseNpgsql(builder.Configuration.GetConnectionString("Default")));

builder.Services.AddScoped<IUserService, UserService>();
builder.Services.AddAuthentication(JwtBearerDefaults.AuthenticationScheme)
    .AddJwtBearer(opt =>
    {
        opt.TokenValidationParameters = new TokenValidationParameters
        {
            ValidateIssuer = true,
            ValidIssuer = builder.Configuration["Jwt:Issuer"],
            ValidateAudience = false,
            IssuerSigningKey = new SymmetricSecurityKey(
                Encoding.UTF8.GetBytes(builder.Configuration["Jwt:Secret"]!))
        };
    });

var app = builder.Build();

app.MapGet("/users/{id:int}", async (int id, IUserService svc) =>
    await svc.GetByIdAsync(id) is { } user
        ? Results.Ok(user)
        : Results.NotFound());

app.MapPost("/users", async (CreateUserDto dto, IUserService svc) =>
{
    var user = await svc.CreateAsync(dto);
    return Results.Created($"/users/{user.Id}", user);
});

app.Run();

This code is the standard skeleton of a production service. For complex projects, we use Vertical Slice Architecture with MediatR, keeping each scenario isolated.

EF Core vs Dapper: Comparison Table

In our projects, we often combine both approaches: EF Core for standard queries, Dapper for heavy reporting. This yields up to 40% reduction in infrastructure costs.

Output Cache and Background Jobs

builder.Services.AddOutputCache(opt =>
{
    opt.AddPolicy("products", p => p.Expire(TimeSpan.FromMinutes(10)).Tag("products"));
});

app.MapGet("/products", async (IProductRepo repo) => await repo.GetAllAsync())
   .CacheOutput("products");

app.MapPost("/products", async (CreateProductDto dto, IOutputCacheStore cache, ...) =>
{
    var product = await svc.CreateAsync(dto);
    await cache.EvictByTagAsync("products", CancellationToken.None);
    return Results.Created($"/products/{product.Id}", product);
});

Output Cache is a built-in solution starting from .NET 8. For background jobs, we use IHostedService or Hangfire depending on queue complexity.

Deployment and Health Checks

FROM mcr.microsoft.com/dotnet/sdk:8.0 AS build
WORKDIR /src
COPY ["MyApi.csproj", "."]
RUN dotnet restore
COPY . .
RUN dotnet publish -c Release -o /app/publish

FROM mcr.microsoft.com/dotnet/aspnet:8.0 AS runtime
WORKDIR /app
COPY --from=build /app/publish .
ENV ASPNETCORE_URLS=http://+:8080
ENV ASPNETCORE_ENVIRONMENT=Production
EXPOSE 8080
ENTRYPOINT ["dotnet", "MyApi.dll"]

Health checks for orchestrators:

builder.Services.AddHealthChecks()
    .AddNpgsql(connStr, name: "postgres")
    .AddRedis(redisConn, name: "redis");

app.MapHealthChecks("/health/live", new HealthCheckOptions { Predicate = _ => false });
app.MapHealthChecks("/health/ready");

What Is the Cost of ASP.NET Core Development?

Typical project cost starts at $5,000 for a basic API and can go up to $50,000 for a complex enterprise system. Our optimization reduces cloud spend by $2,000–$5,000 monthly. For instance, one client saved $3,500 per month after migrating to our C# backend. We provide a detailed quote after requirements review.

Configuring JWT in ASP.NET Core

1. Install the package Microsoft.AspNetCore.Authentication.JwtBearer.
2. In Program.cs, add AddAuthentication with parameters: ValidIssuer, ValidAudience, IssuerSigningKey.
3. Set up authorization via AddAuthorization with policies.
4. Apply middleware: app.UseAuthentication(); and app.UseAuthorization();.

Configuration example is provided above. In production, we add Refresh Tokens and use IdentityServer for centralized management.

Typical Mistakes in ASP.NET Core Development

• N+1 queries in EF Core — use .Include() or projections via .Select().
• Lack of async/await in I/O operations — thread blocking sharply reduces throughput.
• Synchronous password hashing — bcrypt or PBKDF2 are mandatory.
• Ignoring middleware pipeline order — middleware registration order is critical.

Development Deliverables

We provide full API documentation (Swagger), access to source code, and training for your team.

Timeline Estimation

A simple REST API (10–15 endpoints, one DB, JWT): 5–8 business days. A full-featured service with roles, background jobs, SignalR, and CI/CD: 3–5 weeks. Migration from .NET Framework to .NET Core is evaluated separately after a code audit—typically takes 2–6 weeks. Typical project cost starts at $5,000 for a basic API and can go up to $50,000 for a complex enterprise system, but we provide a detailed quote after requirements review.

For a typical mid-size API, our optimization reduces cloud spend by $2,000–$5,000 monthly.

Discuss your project with our architect: write to us with a brief description of the task — we'll estimate the timeline and cost within 1–2 business days. We guarantee code quality and provide an official 3-month warranty.

Backend Development Services: Laravel, Node.js, Go, Django, PostgreSQL

On a production server at 3:14 AM, the Laravel Jobs queue stopped processing. 40,000 unprocessed jobs in Redis. Cause: worker crashed due to a memory leak in one of the Jobs (leak via a static variable in an Eloquent observer), supervisor didn't restart it because of misconfigured stopwaitsecs. This is not a hypothetical scenario — it's Tuesday. We analyzed such an incident on a project with 500 RPS load: diagnosis took 4 hours, fix — 20 minutes. So you don't lose money on downtime, we offer backend development services with a focus on production-grade reliability. We'll assess your project in 2 days.

Backend is what works when no one is watching. Or doesn't work. We guarantee you'll have the first option.

How do we ensure production-grade reliability from day one?

What we do correctly from day one

Service Layer over Fat Controllers. Controller receives HTTP request, validates it via Form Request, passes data to Service, returns response. Business logic in Service, not Controller. This sounds trivial, but most legacy projects have controllers with 500 lines and SQL queries inside.

Repository Pattern we use cautiously. If you just wrap Model::where(...) in a repository method — that's boilerplate without benefit. Repository is justified when: you need to abstract from the data source (DB + cache + external API) or when query logic is complex enough to isolate.

Jobs, Events, Listeners. Everything that can be async — make async. Sending email, PDF generation, external API sync, aggregate recalculation — into Queue. Laravel Horizon for queue monitoring in Redis: see throughput, failed jobs, processing time per queue.

How Octane handles high load

Laravel Octane with RoadRunner or Swoole keeps the app in memory between requests — removes bootstrap overhead (config loading, class autoloading) on each HTTP request. Gain: 3–8x on synthetic benchmarks, 2–4x on real applications. Important: no state between requests in static variables — that leads to exactly the incidents from the beginning. We use this in projects with >1000 RPS.

What to do about N+1 queries

N+1 is the most common cause of slow pages in Laravel apps. Standard story: page worked fine on dev with 10 records, on production with 10,000 — 8-second load.

Laravel Debugbar in dev environment shows the number of queries per page. More than 20 queries per page — signal for audit.

Model::preventLazyLoading(! app()->isProduction());

Telescope for profiling in staging: logs all queries, jobs, mail, notifications with time detail. Numbers: after implementing eager loading, page load time drops from 8s to 0.3s — 27 times faster.

PostgreSQL: indexes that are actually needed

PostgreSQL 14+ is the primary DB on all projects. We use PgBouncer + PostgreSQL combination. 10+ years experience, more than 50 backend projects, 5 years on the market.

How PostgreSQL helps avoid slow queries

Composite indexes for frequent WHERE + ORDER BY. If you have WHERE user_id = ? AND status = ? ORDER BY created_at DESC — you need (user_id, status, created_at DESC). A separate index on (user_id) doesn't help much with sorting.

Partial indexes. If 95% of queries go with WHERE status = 'active':

CREATE INDEX idx_orders_active ON orders (created_at DESC)
WHERE status = 'active';

The index is small, fast, covers the main load.

GIN indexes for JSONB and arrays. @> operator without GIN index — seq scan. With index — fast even on millions of rows.

GIN for full-text search. to_tsvector + GIN instead of LIKE '%query%'. LIKE without index is always seq scan. With pg_trgm extension and gin_trgm_ops — supports LIKE with index, useful for CRM search by partial match.

Connection pooling: why it's more important than it seems

Rails, Laravel, Django open a new connection to PostgreSQL for each PHP/Python process. With 100 workers — 100 connections. PostgreSQL starts degrading from 200–300 active connections — overhead on connection management becomes significant.

PgBouncer — connection pooler in front of PostgreSQL. Transaction pooling mode: connection to PostgreSQL is occupied only during a transaction, returned to pool between requests. 1000 application workers → 20–50 actual connections to PostgreSQL. This reduces latency by 40% and hosting costs by 30%.

Node.js with Fastify: when it's better than Laravel

Node.js is justified for:

• Realtime: WebSocket servers, Server-Sent Events, chat, live updates
• Streaming: large files, video, streaming data
• High I/O concurrency: many parallel requests to external APIs without heavy business logic
• Serverless: Lambda/Cloud Functions — Node.js starts faster than PHP

Fastify over Express: 2–3 times faster on benchmarks, built-in JSON Schema validation, better TypeScript support, plugin architecture.

Typical realtime architecture: Laravel — core business logic and REST API. Node.js + Socket.io or ws — WebSocket server. Laravel publishes events to Redis Pub/Sub, Node.js subscribes and broadcasts to clients. This separation allows scaling the WebSocket server independently of the main app.

Go: microservices and high load

Go we use for:

• High-load microservices (>10,000 RPS)
• Background workers with strict latency requirements
• DevOps tools and CLI
• gRPC services in microservice architecture

Goroutines — thousands of times cheaper than OS threads. 10,000 concurrent connections on Go is normal on one server.

But Go is not a silver bullet. Development is slower than Laravel: more boilerplate, no ORM at Eloquent level, error handling with if err != nil everywhere. Justified only when performance is a real requirement, not an assumption.

Django and Python backend

Django with DRF (Django REST Framework) — for tasks where Python is needed: ML pipelines, data processing, integrations with AI tools.

Celery for background tasks — similar to Laravel Queue but more complex to configure. Celery Beat for cron tasks.

Django ORM vs raw SQL: ORM is convenient for CRUD. For analytical queries with multiple JOINs, window functions, and CTEs — connection.execute() with raw SQL is more readable and predictable.

Redis: not just cache

Redis in our projects plays multiple roles:

Redis Cluster for horizontal scaling. Sentinel for automatic failover on standalone setups.

Deployment and infrastructure

Docker + docker-compose — standard for local development and production. Each service in a container: PHP-FPM/Octane, Nginx, PostgreSQL, Redis, Queue Worker, Scheduler.

CI/CD via GitHub Actions:

1. Run tests (PHPUnit / Pest, Vitest, Playwright)
2. Build Docker image
3. Push to Container Registry
4. Deploy: docker pull → docker-compose up -d on server, or Kubernetes rolling update

Zero-downtime deploy for Laravel: php artisan down --secret=TOKEN is not needed with proper configuration. Strategy: new container starts next to the old one, Nginx switches traffic after health check, old container stops.

Monitoring: Sentry for exception tracking with alerting in Slack/Telegram. Grafana + Prometheus (or Grafana Cloud) for metrics: CPU, memory, request rate, queue depth, database connection count. Alerts on: error rate > 1%, p99 latency > 2s, queue depth > 1000 jobs.

What's included in turnkey work

• Architecture design (API documentation, DB schema, service diagram)
• Implementation according to agreed specification with code review
• CI/CD, monitoring, alerting setup
• Load testing (k6, wrk) with report
• Handover of source code, access, deployment instructions
• Training of customer's team (2-3 sessions)
• Warranty support for 1 month after delivery

Timeline benchmarks

Pricing is calculated individually after analyzing load, integrations, and business logic. Contact us for a free audit of your current backend — get an optimization plan in 2 days. Request a consultation.