Building a Production-Ready REST Client Layer on Top of Refit in .NET MAUI

🚀 Building a Production-Ready REST Client Layer on Top of Refit in .NET MAUI

Building a Production-Ready REST Client Layer for Modern Distributed Applications

Modern applications are more connected than ever. Whether you're building a mobile application with .NET MAUI, a cloud-native backend, a desktop application, or an enterprise SDK, chances are your application spends a significant portion of its lifetime communicating with REST APIs.

• Authentication...
• Product catalogs...
• Orders...
• Payments...
• Analytics...
• Notifications...
• Cloud synchronization...

Almost every feature eventually becomes an HTTP request. Over the years, the .NET ecosystem has dramatically simplified the way developers consume REST services.

We went from manually creating sockets, to WebRequest, then HttpWebRequest, followed by HttpClient, and eventually to elegant interface-based libraries like Refit that almost completely eliminated networking boilerplate. Today, consuming an API can be as simple as writing this:

public interface IPostsApi
{
    [Get("/posts")]
    Task<List<Post>> GetPostsAsync();

    [Post("/posts")]
    Task<Post> CreatePostAsync([Body] CreatePostRequest request);
}

• No manual serialization.
• No request creation.
• No response parsing.
• No dozens of repetitive lines of networking code.

This programming model has become incredibly popular because it allows developers to focus on what they want to call instead of how the HTTP request is built.

For many applications, that's exactly enough.

But as applications grow... things begin to change.

🌎 When Applications Become Enterprise Applications

Small applications generally communicate with one or two REST APIs.

• Requests are simple.
• Networks are stable.
• Authentication is straightforward.
• Failures are rare.
• Enterprise applications are completely different.

A single mobile application may communicate with:

• 👤 Authentication Services
• 📦 Product APIs
• 💳 Payment Providers
• ☁️ Cloud Storage
• 📊 Analytics Platforms
• 🔔 Push Notification Services
• 📍 Location Services
• 📷 Computer Vision APIs
• 🤖 AI Services
• 🏢 Internal Microservices Every one of those services introduces additional complexity.

Soon, making an HTTP request is no longer enough.

Applications begin asking questions like:

• 🔁 What happens if the request fails?
• 🔐 What if the JWT token expires?
• 📶 What if the user loses internet connectivity?
• ⏱️ How long should we wait before timing out?
• 📊 How do we measure latency?
• 🔍 How do we trace requests across microservices?
• 💾 Can we cache this response?
• 🚧 What if the service is temporarily unavailable?
• 📤 How do we report upload progress?
• 📥 How do we report download progress?
• 🧩 How do we support XML, Protobuf and JSON simultaneously?

At that point, networking stops being "just HTTP." It becomes an architectural concern.

📖 Why Refit Changed Everything

Before Refit, consuming REST APIs usually looked something like this:

var request = new HttpRequestMessage(
    HttpMethod.Get,
    "https://api.mycompany.com/users");

var response = await _httpClient.SendAsync(request);

if (!response.IsSuccessStatusCode)
{
    throw new Exception();
}

var json = await response.Content.ReadAsStringAsync();

var users = JsonSerializer.Deserialize<List<User>>(json);

There is nothing inherently wrong with this code.

The problem appears when you have... 20, 50, 20 endpoints.

Every endpoint repeats the same concepts:

• URLs
• Serialization
• Headers
• Authentication
• Error handling

This is exactly what Refit solved. Instead of manually constructing requests, developers simply define contracts.

public interface IUserApi
{
    [Get("/users")]
    Task<List<User>> GetUsersAsync();

    [Post("/users")]
    Task<User> CreateUserAsync([Body] User user);
}

That's beautiful, readable, strongly typed, testable. It feels almost magical. And honestly...

It still remains one of the best approaches for consuming REST APIs in .NET.

🏢 Where Enterprise Projects Begin to Hurt

The problem isn't Refit.

The problem is that enterprise applications require much more than request generation. Imagine a typical request in a production application.

Request
↓
Authentication
↓
Logging
↓
Correlation ID
↓
Retry Policy
↓
Circuit Breaker
↓
Timeout
↓
Telemetry
↓
Metrics
↓
Caching
↓
Compression
↓
Serialization
↓
HttpClient
↓
API

😵 The DelegatingHandler Problem

If you've worked on enterprise applications before, you've probably seen something like this:

AuthenticationHandler
↓
RetryHandler
↓
LoggingHandler
↓
TelemetryHandler
↓
CachingHandler
↓
CompressionHandler
↓
CorrelationHandler
↓
CustomHeadersHandler
↓
HttpClient

Now imagine... 1.- A new project starts. 2.- The first thing everyone does is... 3.- Copy all those handlers. 4.- Again. 5.- And again.

Eventually you have dozens of nearly identical implementations scattered across multiple repositories. This isn't because Refit is missing something.

It's because enterprise applications inevitably accumulate cross-cutting concerns. Those concerns deserve their own architecture.

💡 The Idea Behind Shaunebu.Common.RESTClient

While working on several enterprise projects, I repeatedly encountered the same situation.

Every solution eventually added:

• Authentication
• Logging
• Retry Policies
• Telemetry
• Timeouts
• Custom Serialization
• Progress Reporting
• Fallback Logic
• Caching
• Diagnostics

Project after project... The same infrastructure was being rewritten. That experience ultimately led to the creation of Shaunebu.Common.RESTClient.

The goal wasn't to replace Refit.

It was to preserve everything developers already love about interface-driven REST development while extending it with the capabilities typically required by production systems. Instead of writing infrastructure repeatedly...

Build it once.

Reuse it everywhere.

🏗️ Architecture

The resulting architecture looks like this.

Application
        │
        ▼
Shaunebu.Common.RESTClient
        │
Interceptor Pipeline
        │
Serialization Engine
        │
Resilience Engine
        │
Content Negotiation
        │
Caching
        │
OpenTelemetry
        │
HttpClient
        │
REST API

Notice something interesting. The ViewModel... The Service... The Business Layer...

None of them know anything about retries, telemetry, caching, Polly and serializers.

Networking concerns remain centralized inside the REST layer.

This dramatically simplifies application code.

🎯 Design Goals

When designing RESTClient, several principles guided every architectural decision.

🎨 Declarative by Default

Developers should describe behavior rather than implement infrastructure. Instead of writing retry logic manually:

[Retry(3,2)]

Instead of configuring timeout handlers:

[Timeout(10)]

The intention is immediately visible. No boilerplate. No hidden behavior.

🧩 Extensible Everywhere

Enterprise applications are never identical. Some require XML.

Others require Protobu, some need custom authentication, others require proprietary serialization. Rather than forcing one implementation, every major component is replaceable.

Developers can extend:

• 🧩 Serializers
• 🔐 Authentication
• 🧱 Interceptors
• 🌐 URL formatting
• 💾 Cache providers
• 📊 Metrics
• 📈 Telemetry
• 💥 Fallback providers

without modifying the core framework.

🚀 Production Ready

Many networking libraries stop after generating HTTP requests. RESTClient was designed with production workloads in mind from day one.

That means supporting:

• 🔁 Automatic retries
• 🚧 Circuit breakers
• ⏱️ Timeouts
• 📊 Observability
• 🪵 Structured logging
• 📈 Metrics
• 📶 Offline scenarios
• 💾 Response caching
• 📤 Upload progress
• 📥 Download progress

👨‍💻 Interface-First Development

One of the best aspects of Refit is that developers describe APIs as interfaces. RESTClient keeps that same philosophy.

public interface IProductsApi
{
    [Get("/products")]
    Task<List<Product>> GetProductsAsync();

    [Get("/products/{id}")]
    Task<Product> GetProductAsync(int id);

    [Post("/products")]
    Task<Product> AddProductAsync([Body] Product product);
}

⚡ Dependency Injection Made Simple

Registering a client is intentionally straightforward.

services.AddRESTClient<IProductsApi>(
    "https://api.contoso.com");

Behind this single line, RESTClient automatically configures: ✅ Proxy generation ✅ Serialization ✅ HttpClient ✅ Interceptor pipeline ✅ Polly policies ✅ Content negotiation ✅ Metrics ✅ Diagnostics ✅ Dependency Injection

No repetitive configuration is required. That means developers spend less time wiring infrastructure and more time building features.

🌍 Centralized Configuration

One of the biggest maintenance problems in large applications is configuration sprawl.

• Timeouts in one file.
• Retry counts in another.
• Cache settings elsewhere.

RESTClient centralizes everything through a single options object, allowing teams to define global networking behavior in one place while still supporting per-client or per-method customization when necessary.

services.AddRESTClient(options =>
{
    options.Timeout.RequestTimeoutSeconds = 30;
    options.Resilience.RetryCount = 3;
    options.Resilience.CircuitBreakerThreshold = 5;
});

This keeps networking behavior predictable across the entire application.

🔁 Automatic Retry Policies

Transient failures are incredibly common. Imagine this sequence:

Request
↓
Network Timeout
↓
Retry
↓
Success

From the user's perspective... Nothing happened. The operation simply succeeded. Without retries, that exact same scenario would have resulted in an unnecessary error dialog. RESTClient allows retries to be declared directly on each endpoint.

[Retry(3,2)]
Task<Order> GetOrderAsync(int id);

Meaning:

• Retry up to 3 times
• Wait 2 seconds between attempts
• No additional configuration.
• No middleware.
• No custom handlers.

📈 Exponential Backoff

Retrying immediately isn't always the best strategy. Imagine a server that is temporarily overloaded.

Retrying hundreds of requests simultaneously only makes things worse. A better approach is Exponential Backoff.

Attempt 1
↓
2 seconds
↓
Attempt 2
↓
4 seconds
↓
Attempt 3
↓
8 seconds
↓
Attempt 4

This dramatically reduces pressure on the remote service while increasing the probability of recovery. RESTClient builds on Polly's resilience engine, allowing sophisticated retry strategies without polluting application code.

⏱️ Timeout Policies

Not every request deserves the same timeout, downloading a profile picture might reasonably wait 30 seconds, fetching a product catalog may only deserve 5 seconds, authenticating a user should probably fail much sooner.

RESTClient supports method-level timeout declarations.

[Timeout(5)]
Task<LoginResponse> LoginAsync(LoginRequest request);

Rather than waiting indefinitely, requests fail predictably, allowing the application to recover gracefully.

🚧 Circuit Breakers

One of the most misunderstood resilience patterns is the Circuit Breaker.

Imagine an API experiencing a major outage. Without a circuit breaker:

Request
↓
Failure
↓
Request
↓
Failure
↓
Request
↓
Failure
↓
Request
↓
Failure

The application continues hammering a service that is already unavailable.

A Circuit Breaker detects repeated failures and temporarily stops sending requests.

Closed
↓
Failures
↓
Open
↓
Requests Blocked
↓
Cooldown
↓
Half Open
↓
Healthy?
↓
Closed Again

Benefits include: ✅ Preventing cascading failures ✅ Reducing unnecessary traffic ✅ Faster application responses ✅ Giving remote systems time to recover

🪂 Typed Fallback Providers

Enterprise applications should not always crash because a request failed

RESTClient introduces Typed Fallback Providers.

Instead of throwing an exception:

API Failure
↓
Fallback Provider
↓
Offline Response
↓
Application Continues

Example:

[Fallback(typeof(UserFallbackProvider))]
Task<User> GetUserAsync(int id);

The fallback provider decides how the application should recover.

Perhaps returning:

• Offline data
• Cached information
• Empty collections
• Default configuration

instead of interrupting the user.

This approach is particularly valuable in mobile applications where intermittent connectivity is expected rather than exceptional.

🧱 Bulkhead Isolation

Imagine an application communicating with three different services.

Authentication

Products

Payments

If the Payments API suddenly becomes extremely slow... Should every other API become slow too?

Of course not. Bulkhead isolation prevents one dependency from exhausting shared resources.

Authentication
──────────────
Products
──────────────
Payments

Each service receives its own execution boundaries. A failure in one area doesn't compromise the rest of the application.

🧩 The Interceptor Pipeline

One of the biggest architectural differences between RESTClient and traditional REST libraries is the interceptor pipeline. Instead of creating dozens of DelegatingHandler implementations or scattering HTTP logic across helper classes, every request passes through a configurable processing pipeline.

Request
↓
Authentication
↓
Security
↓
Logging
↓
OpenTelemetry
↓
Metrics
↓
Caching
↓
Retry
↓
HTTP
↓
Response

Every stage has a single responsibility.

This makes the pipeline incredibly flexible while keeping the application architecture clean.

🔐 Security Interceptor

Authentication is often repeated across every project.

• Adding Bearer tokens.
• Refreshing expired credentials.
• Injecting custom headers.
• Handling API keys.

Instead of manually modifying every request, the Security Interceptor centralizes those responsibilities.

Request
↓
Access Token
↓
Authorization Header
↓
API

Business logic never worries about authentication headers.

🪵 Structured Logging

Logging should tell a story. Rather than simply printing:

GET /products

Enterprise logs often include:

• Correlation IDs
• Request duration
• Response size
• HTTP status
• Exception details
• Retry count
• User context

RESTClient includes a dedicated logging interceptor capable of producing structured logs that integrate with modern observability platforms.

📊 OpenTelemetry Integration

Distributed applications rarely consist of a single service. A mobile application may communicate with dozens of APIs.

Without tracing, understanding request flow becomes nearly impossible. RESTClient includes native support for OpenTelemetry.

Mobile App
↓
RESTClient
↓
API Gateway
↓
Orders API
↓
Inventory API
↓
Database

Every request can participate in a distributed trace.

Developers gain visibility into: 📈 Latency 📉 Bottlenecks 📊 Throughput ❌ Failures 🕒 Request timing

without writing additional instrumentation.

📦 Metrics Collection

Monitoring isn't only about logs. Metrics answer questions like:

• How many requests are executed per minute?
• Which endpoint is the slowest?
• How many retries occurred today?
• Which API fails most frequently?
• What's the average response time?

RESTClient collects these metrics automatically through its interceptor pipeline, making operational dashboards significantly easier to build.

💾 Intelligent Response Caching

Not every request should hit the network.

Many APIs return information that changes infrequently.

Examples include:

• Product catalogs
• Countries
• Categories
• Settings
• Configuration
• Feature flags

Rather than requesting the same data repeatedly:

Application
↓
Cache
↓
Hit?
↓
Yes
↓
Return Cached Data

Only cache misses reach the server.

Benefits include: ⚡ Faster startup 📶 Reduced bandwidth 🔋 Lower battery consumption ☁️ Reduced cloud costs

🌐 Multiple Serialization Formats

JSON dominates REST APIs today. But enterprise systems frequently communicate using:

• XML
• MessagePack
• Protocol Buffers
• FormUrlEncoded
• Multipart

Instead of forcing developers to manually switch serializers, RESTClient supports multiple serialization formats natively and selects the appropriate serializer based on the request configuration and content negotiation rules.

For example, a single application might:

• Consume JSON from a public REST API.
• Exchange Protobuf messages with high-performance internal services.
• Upload files using multipart requests.
• Communicate with legacy XML-based systems.

All through the same programming model.

🌍 Dynamic Content Negotiation

One of the less-discussed challenges when building SDKs for enterprise environments is that not every API speaks the same language.

While JSON has become the de facto standard for modern REST services, many organizations still expose services using:

• 📄 XML
• ⚡ Protocol Buffers
• 📦 MessagePack
• 📝 FormUrlEncoded
• 📤 Multipart
• 🧩 Proprietary media types

Supporting these APIs usually means manually changing serializers throughout the application. Soon developers begin writing code such as:

if(contentType == "application/json")
{
    ...
}
else if(contentType == "application/xml")
{
    ...
}
else if(...)
{
    ...
}

Besides being repetitive... It completely violates the Open/Closed Principle.

RESTClient approaches this problem differently.

Instead of making serialization decisions inside business logic, it delegates that responsibility to a Content Negotiator.

Application
    ↓
RESTClient
    ↓
Content Negotiator
    ↓
-> JSON
->XML
->MessagePack
->Protobuf
    ↓
REST API

The application simply requests data.

RESTClient determines which serializer should be used. This makes it possible to communicate with multiple heterogeneous APIs without changing application code.

📦 Multiple Serialization Engines

Serialization should be an implementation detail. Not an application concern.

RESTClient includes native support for multiple serialization engines including:

📄 JSON 📄 XML ⚡ MessagePack 🚀 Protocol Buffers 📝 FormUrlEncoded 📤 Multipart

This becomes especially valuable when developing enterprise SDKs where different services may expose completely different media types.

For example:

Orders API
↓
JSON
────────────────────
Inventory API
↓
Protobuf
────────────────────
Legacy ERP
↓
XML
────────────────────
Document API
↓
Multipart

Despite communicating with four completely different systems... The developer experience remains identical.

🔄 Content Negotiation

HTTP already includes a mechanism for negotiating representations. The Accept header. For example:

Accept: application/json

or

Accept: application/xml

or

Accept: application/x-protobuf

Rather than forcing developers to manually swap serializers, RESTClient automatically selects the appropriate serializer according to the requested media type.

This allows applications to evolve without introducing serializer-specific code throughout the project.

📤 Upload Progress

Mobile applications frequently upload large files. Examples include: 📸 Images 🎥 Videos 📄 Documents 📦 ZIP Archives 🏥 Medical Imaging 📁 CAD Files

Without progress reporting, users are left wondering: "Is the application frozen?"

A production-ready networking layer should provide visibility into long-running operations. RESTClient supports real-time upload progress.

Upload
↓
5%
↓
18%
↓
42%
↓
76%
↓
100%

This allows developers to build responsive experiences with progress bars, percentage indicators, or remaining time estimations. For applications dealing with large media assets, this dramatically improves user experience.

📥 Download Progress

Downloads deserve the same treatment.

Whether downloading: 📄 PDFs 🎵 Audio 🎥 Video 📦 Offline Maps 🤖 AI Models 📁 Database Snapshots

Users expect feedback.

RESTClient reports download progress continuously, making it easy to present real-time status within the UI.

Download
↓
Receiving...
↓
65%
↓
89%
↓
Completed

🧩 Extensibility by Design

One of the primary goals during the design of RESTClient was avoiding rigid architectures.

Instead of forcing developers into a predefined implementation, nearly every component can be replaced.

Developers can implement custom: 🧩 Serializers 🔐 Authentication Providers 🧱 Interceptors 📦 Cache Providers 🌐 URL Formatters 📊 Metrics Collectors 💥 Fallback Providers

This allows RESTClient to adapt to the application's architecture rather than forcing the application to adapt to the framework.

⚙️ Configuration Through Dependency Injection

Enterprise applications typically expose dozens of REST clients. Maintaining configuration across every registration quickly becomes repetitive.

Instead of configuring every client individually, RESTClient centralizes configuration.

services.AddRESTClient(options =>
{
    options.Timeout.RequestTimeoutSeconds = 30;

    options.Resilience.RetryCount = 3;

    options.Resilience.CircuitBreakerThreshold = 5;
});

The same configuration automatically applies across every registered client.

For even larger applications, registrations can be externalized into configuration files, allowing environments to define REST clients without recompiling the application.

☁️ Enterprise Architecture

As applications grow, networking gradually becomes its own subsystem. Rather than thinking in terms of individual HTTP requests, enterprise teams think in terms of communication architecture.

A typical application using RESTClient might look like this.

ViewModels
↓
Business Services
↓
Repositories
↓
REST Interfaces
↓
RESTClient
↓
Authentication
↓
Retry
↓
Telemetry
↓
Logging
↓
Caching
↓
Serialization
↓
HttpClient
↓
Cloud APIs

Notice something important. The upper layers know nothing about:

• Retry policies.
• OpenTelemetry.
• Logging.
• Serialization.
• Authentication.
• Caching.
• Progress reporting.

Every concern remains isolated within the communication layer. This separation significantly improves maintainability.

🏢 Enterprise Scenarios

RESTClient was designed with enterprise workloads in mind. Some examples include:

🏥 Healthcare

• Healthcare applications frequently communicate with multiple systems simultaneously.
• Electronic medical records.
• Imaging services.
• Insurance providers.
• Identity systems.

Each service often exposes different protocols and availability guarantees. RESTClient's resilience pipeline helps maintain reliability even when individual systems become temporarily unavailable.

🛒 Retail

Retail applications synchronize:

• Products
• Inventory
• Pricing
• Promotions
• Customer Profiles
• Orders

A robust communication layer minimizes network usage through caching while improving responsiveness through retry policies and offline fallbacks.

☁️ Cloud-Native Systems

Microservice architectures typically involve dozens of HTTP dependencies. Observability becomes essential.

Distributed tracing, structured logging, metrics, and correlation identifiers provide the visibility required to diagnose issues across service boundaries.

⚡ Performance Considerations

Performance is often associated with raw execution speed. However, in distributed systems, performance is much broader than simply measuring how quickly a request completes.

Questions such as these become equally important:

• 📶 How many unnecessary requests are being sent?
• 🔄 How many retries are executed every hour?
• 💾 Are responses being cached efficiently?
• 📊 Which endpoint is introducing latency?
• 🔍 Where are requests spending most of their time?
• 📈 Are network resources being utilized efficiently?

A well-designed REST communication layer should optimize not only execution speed, but also reliability, scalability, and resource utilization.

RESTClient addresses performance from multiple perspectives.

🚀 Reducing Boilerplate Improves Performance Too

Performance isn't only measured in milliseconds. Developer productivity is equally important. Imagine creating ten REST clients manually.

Each one requires:

• HttpClient configuration
• Serialization
• Authentication
• Retry policies
• Logging
• Error handling
• Timeouts
• Dependency Injection

Now multiply that across: 📦 25 APIs 👨‍💻 10 developers 🏢 Multiple projects

Soon, hundreds of lines of infrastructure code exist solely to support HTTP communication.

RESTClient dramatically reduces that repetitive code, allowing teams to spend more time building business functionality instead of infrastructure.

🌐 Efficient Network Usage

Mobile devices operate under conditions very different from servers. Network quality changes constantly. Bandwidth is limited. Battery consumption matters.

Every unnecessary HTTP request affects: 🔋 Battery life 📶 Cellular usage ☁️ Backend resources

Response caching, retries, and fallback providers help minimize unnecessary traffic while improving responsiveness.

📈 Observability Drives Performance

One of the biggest advantages of modern distributed systems is observability. Without visibility, optimization becomes guesswork. Imagine receiving a report that:

"The application feels slow."

Where is the bottleneck? Authentication? Serialization? DNS? Network latency? API Gateway? Database? Without telemetry, answering these questions becomes extremely difficult. RESTClient integrates OpenTelemetry directly into its communication pipeline, making performance analysis part of the framework rather than an afterthought.

REST Request
↓
Trace
↓
Span
↓
Metrics
↓
Dashboard
↓
Optimization

Instead of wondering what happened... Developers can see exactly what happened.

🏗️ Building SDKs Instead of HTTP Clients

One of the design goals behind RESTClient was changing the way developers think about REST communication. Rather than building isolated HTTP clients... Think about building reusable SDKs.

A typical enterprise SDK contains far more than endpoint definitions. It encapsulates:

• Authentication
• Validation
• Serialization
• Retry logic
• Diagnostics
• Logging
• Metrics
• Configuration
• Progress reporting
• Error handling

RESTClient provides the foundation for that SDK while keeping the public API remarkably simple.

📊 Comparing Approaches

Every approach to REST communication has its strengths.

It's important to emphasize that this comparison is not about replacing Refit.

Refit remains an outstanding library for interface-based REST communication.

RESTClient simply addresses scenarios that frequently arise once applications grow into enterprise-scale systems.

🏢 A Real Enterprise Workflow

To better understand how all these components work together, let's walk through a typical production scenario. A user signs into a mobile application.

User
↓
Login
↓
JWT Token
↓
RESTClient
↓
Request
↓
Authentication Interceptor
↓
Logging Interceptor
↓
OpenTelemetry
↓
Retry Policy
↓
HTTP Request
↓
API

Everything works as expected... Until the access token expires.

Without an enterprise communication layer, the application might immediately receive:

401 Unauthorized

The ViewModel now needs to decide:

• Should I refresh the token?
• Should I retry the request?
• Should I redirect to Login?
• Should I log the failure?
• Should I notify telemetry?

Business logic suddenly becomes responsible for networking concerns. RESTClient keeps those responsibilities inside the communication layer.

The workflow becomes:

Request
↓
401 Unauthorized
↓
Token Refresh
↓
Retry
↓
Success
↓
ViewModel Receives Response

The ViewModel never knows the request failed. From its perspective...

Everything simply worked.

That separation of responsibilities is one of the biggest architectural benefits of centralizing REST communication.

📚 Documentation and Resources

If you'd like to explore RESTClient in more depth, including installation instructions, complete samples, extensibility points, and advanced configuration scenarios, you can find everything here:

📂 GitHub Repository

Shaunebu.Common.RESTClient Documentation & Samples

The repository includes:

• Complete samples
• Configuration examples
• Advanced interceptor implementations
• Custom serializers
• Fallback providers
• Architecture documentation

📦 NuGet Package

Install directly from NuGet: Shaunebu.Common.RESTClient on NuGet

dotnet add package Shaunebu.Common.RESTClient

💡 Best Practices

When designing a communication layer for production applications, a few practices can make a significant difference over time: ✅ Keep business logic independent from networking concerns. ✅ Centralize authentication, retries, and telemetry. ✅ Prefer declarative configuration over repetitive infrastructure code. ✅ Design for failures rather than assuming success. ✅ Treat observability as a core feature, not an optional add-on. ✅ Use typed fallbacks to improve offline experiences. ✅ Keep serializers replaceable to support heterogeneous APIs. ✅ Build communication layers that can evolve without affecting application code.

🚀 Key Takeaways

By extending the familiar interface-based programming model popularized by Refit, it's possible to build a communication layer capable of addressing the realities of modern enterprise applications.

Throughout this article we've explored how REST communication can evolve beyond simple HTTP requests by incorporating resilience, observability, extensibility, and centralized configuration into a single architecture.

Some of the most important ideas include:

• 🚀 Keep the simplicity of interface-driven API definitions.
• 🛡️ Design for failure using retries, circuit breakers, and fallback providers.
• 📊 Make observability a first-class citizen through OpenTelemetry and metrics.
• 🧩 Centralize cross-cutting concerns inside an interceptor pipeline.
• 🌐 Support multiple serialization formats without complicating application code.
• 💾 Reduce network usage through intelligent caching and offline-friendly behaviors.
• 🔧 Build reusable SDKs instead of repeatedly recreating HTTP infrastructure.

🌟 Final Thoughts

Refit transformed the way many of us consume REST APIs in .NET by demonstrating how elegant interface-based programming could eliminate boilerplate and improve maintainability.

As applications evolve, however, communication becomes far more than simply issuing HTTP requests. Authentication, resilience, observability, caching, serialization, diagnostics, and configuration all become essential parts of a reliable networking stack. Shaunebu.Common.RESTClient was created from real-world enterprise experience with a simple goal: preserve the productivity of Refit while providing the infrastructure typically required by production-grade applications.

Rather than replacing existing development practices, it builds upon them—offering a flexible, extensible, and production-ready foundation for REST communication across modern .NET applications, including cross-platform solutions built with .NET MAUI.

Whether you're developing mobile applications, enterprise SDKs, cloud-native services, or distributed systems, investing in a robust communication layer pays dividends in reliability, maintainability, and developer productivity.

If you'd like to explore the project further, try the samples, or contribute to its evolution, be sure to check out the GitHub repository and the NuGet package. I hope it helps simplify your next REST integration as much as it has mine. 🚀