Cut Your .NET 10 API Startup Time by 80%: Native AOT for High-Performance

As talking about the fintech, milliseconds matter. When market conditions change, the difference between a profitable trade and a missed opportunity often comes down to how quickly your application can process and respond to incoming data. After spending the last six months optimizing a mission-critical trading API at a major financial institution, I’ve discovered that one of the most impactful performance improvements in .NET 10 isn’t getting the attention it deserves:

Native Ahead-of-Time (AOT) compilation

The results speak for themselves: our production API startup time dropped from 70ms to just 14ms — an 80% reduction — while memory usage decreased by more than 50%. For applications that need to scale rapidly during market volatility or restart quickly after deployments, these improvements directly translate to business value.

let’s walk through exactly how to implement Native AOT for your .NET 10 fintech applications with some code examples, performance benchmarks, and the lessons we learned along the way…

What Is Native AOT

Before implementation, let’s clarify what Native AOT actually does and why it matters for fintech applications specifically.

Traditional .NET applications rely on Just-In-Time (JIT) compilation, which transforms IL (Intermediate Language) code into machine code at runtime. While the JIT compiler is impressively efficient, it introduces overhead:

• Startup delay: The first request to your API waits while the JIT compiler warms up
• Memory overhead: Both IL and compiled machine code consume memory
• Unpredictable performance: JIT compilation can cause periodic pauses

Native AOT takes a fundamentally different approach by performing compilation during the build process, producing a self-contained native executable with these characteristics:

• No JIT overhead: The application starts executing native code immediately
• Reduced memory footprint: No need to keep IL code in memory
• Deterministic performance: Fewer runtime surprises during critical operations
• Self-contained deployment: No dependency on a specific .NET runtime version

For fintech applications, these benefits directly address common challenges like rapid scaling during market events, consistent performance for time-sensitive operations, and simplified deployment to environments with strict security requirements.

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Adding Native AOT to our API

Let’s implement Native AOT in our case — REST API that provides real-time stock quotes and executes trade orders. we are using ASP.NET Core in .NET 10, focusing on the components most relevant to fintech applications.

Step 1: Configure the Project for Native AOT

Start by modifying .csproj file to enable Native AOT compilation:

  
    net10.0
    enable
    enable
    
    
    true
    
    
    Size
    
    
    true
  

<PublishAot>true</PublishAot>, which enables Native AOT compilation & additional properties to optimize the output size, which is particularly important for cloud deployments where image size affects startup time and costs.

Step 2: Addressing Common Fintech-Specific Challenges

Native AOT has some limitations that can especially impact fintech applications. we need to address them one by one:

2.1. Dynamic Code Generation and Reflection

Finance applications often use reflection for configuration, plugin architectures, or serialization. Native AOT requires knowing all types at compile time, so we need to provide hints:

Program.cs

using System.Runtime.CompilerServices;
using System.Text.Json.Serialization;

// Add this before your builder configuration
[assembly: DynamicDependency(DynamicallyAccessedMemberTypes.All, typeof(TradeOrder))]
[assembly: DynamicDependency(DynamicallyAccessedMemberTypes.All, typeof(StockQuote))]

var builder = WebApplication.CreateBuilder(args);

// Configure JSON options to work well with AOT
builder.Services.ConfigureHttpJsonOptions(options =>
{
    options.SerializerOptions.TypeInfoResolverChain.Insert(0, AppJsonSerializerContext.Default);
});

//.. rest your code..

And define your JSON context: [either separate or just add at the bottom of Program.cs]

[JsonSerializable(typeof(TradeOrder))]
[JsonSerializable(typeof(StockQuote))]
[JsonSerializable(typeof(List))]
internal partial class AppJsonSerializerContext : JsonSerializerContext
{
}

just to ensure that the compiler includes the necessary metadata for your domain models.

2.2. Database Access

In our case, we are using Entity Framework Core for data access. Here’s how we make it work with Native AOT:

using Microsoft.EntityFrameworkCore.Query;

builder.Services.AddDbContext(options =>
{
    options.UseSqlServer(builder.Configuration.GetConnectionString("TradingDb"))
           .EnableSensitiveDataLogging()
           .UseModel(TradingDbContextModel.Instance); // Pre-compiled model
});

also, need to generate the model class:

dotnet ef dbcontext optimize -o Models/CompiledModels -n YourNamespace.Models.CompiledModels

This creates a compiled model that avoids runtime model building, which is incompatible with Native AOT.

2.3. High-Performance Market Data Processing

Now for real-time market data processing, our application needs fine-grained memory management. Native AOT works well with the System.IO.Pipelines API for high-throughput scenarios:

public class MarketDataProcessor
{
    public async Task ProcessMarketFeed(PipeReader reader)
    {
        while (true)
        {
            ReadResult result = await reader.ReadAsync();
            ReadOnlySequence buffer = result.Buffer;
            
            // Process market data packets
            TryParseMarketData(buffer, out SequencePosition consumed);
            
            reader.AdvanceTo(consumed, buffer.End);
            
            if (result.IsCompleted) break;
        }
    }
    
    private bool TryParseMarketData(ReadOnlySequence buffer, out SequencePosition consumed)
    {
        // Market data parsing logic...
        consumed = buffer.Start;
        return true;
    }
}

This low-level approach avoids allocations and works perfectly with Native AOT’s optimization capabilities.

Step 3: Building and Deploying

Now its time to build our Native AOT application:

dotnet publish -c Release

For containerized deployments, use a multi-stage Dockerfile to keep the final image size small:

# Build stage
FROM mcr.microsoft.com/dotnet/sdk:10.0 AS build
WORKDIR /source

# Copy project files
COPY *.csproj .
RUN dotnet restore -r linux-x64

# Copy source code and publish
COPY . .
RUN dotnet publish -c Release -o /app -r linux-x64 --no-restore

# Final stage
FROM mcr.microsoft.com/dotnet/runtime-deps:10.0
WORKDIR /app
COPY --from=build /app .

# Set the entry point
ENTRYPOINT ["./YourTradingApi"]

# Expose the port your API uses
EXPOSE 8080

This creates a lightweight container with just your compiled application and minimal dependencies.

Performance Benchmarks: Before and After

Here are the results:

See the improvements in startup time and memory usage, but we also saw meaningful gains in API latency, particularly at the P95 level, which is critical for ensuring consistent performance during high-volume trading…

Also,

we applied Native AOT to our market data gateway, which ingests, normalizes, and distributes real-time prices from multiple exchanges. This service needs to be extremely reliable — any downtime means missed trades and potential financial losses.

Before implementing Native AOT, we faced several challenges:

• Slow scaling during market events: When volatility spiked, our autoscaling took too long to bring new instances online
• Inconsistent performance after deployments: The “cold start” problem meant the first trades after a deployment experienced higher latency
• Memory pressure during peak hours: Our monitoring showed high GC activity during market open and close

After implementing Native AOT:

• Scale-up time decreased by 76%: New instances became fully operational in under 3 seconds
• Consistent latency after deployment: No more “warming up” period
• GC pressure reduced by 42%: Less memory churn led to fewer GC pauses
• Cost savings of approximately 35%: Lower memory requirements allowed for higher-density deployments

The most significant impact was on our 99.9th percentile latency numbers — exactly the outliers that cause problems in financial systems. These improved from 28ms to just 12ms, a critical improvement for time-sensitive trading operations.

Best Practices

Based on our experience implementing Native AOT in production fintech applications, here are the key lessons and best practices:

1. Profile Before Optimizing

Native AOT isn’t a magic solution for all performance problems. Before implementation, profile your application to understand where the bottlenecks are:

// Add to Program.cs for development profiling
if (builder.Environment.IsDevelopment())
{
    builder.Services.AddDiagnosticListener();
    var listener = new DiagnosticListener("PerformanceMonitoring");
    DiagnosticListener.AllListeners.Subscribe(new DiagnosticObserver());
}

2. Identify and Address Reflection Usage

Use the ILLink analyzer to find potential compatibility issues:

dotnet publish -r linux-x64 -c Release /p:PublishAot=true /p:EmitCompilerGeneratedFiles=true

Review the warnings in the obj/Release/net10.0/linux-x64/linked directory to find reflection patterns that need to be modified.

3. Test Thoroughly in Production-Like Environments

Native AOT can behave differently depending on the runtime environment. Test on the same OS and architecture as your production system.

4. Monitor Memory and GC Differently

Native AOT apps have different memory characteristics. Update your monitoring to track:

• Native heap usage instead of just managed memory
• Thread pool starvation (a common issue in high-throughput fintech apps)
• CPU usage patterns (often more consistent but possibly higher)

5. Understand the Trade-offs

Native AOT comes with trade-offs that may impact your development workflow:

• Longer build times (our build pipeline went from 4 minutes to 11 minutes)
• Larger binary sizes (though smaller runtime footprint)
• Less runtime flexibility for dynamic loading

Conclusion: Is Native AOT Right for Your Fintech Application?

Based on our real-world implementation, Native AOT provides substantial benefits for fintech applications where:

• Startup time is critical: Market data services, trading engines, or any component that needs to scale quickly
• Predictable performance matters: Order execution, pricing engines, or risk calculations
• Memory efficiency is important: High-volume data processing services or edge deployments
• Deployment simplicity is valued: Environments with strict security requirements or limited runtime options

The 80% reduction in startup time and 44.8% improvement in API latency we achieved translated directly to business value: faster trade execution, better scaling during market volatility, and lower infrastructure costs.

As .NET 10 makes Native AOT more accessible than ever, fintech developers should seriously consider this approach for performance-critical components of their trading systems. The implementation complexity is far outweighed by the benefits, especially in environments where milliseconds translate directly to dollars.

Have you implemented Native AOT in your fintech applications? I’d love to hear about your experiences in the comments below.

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