Multi-Platform API Alignment with Stable Updates

In a corporate environment, maintaining API consistency across multiple platforms—such as separate web frontends and backends, or mobile apps and backend services—is a persistent challenge. This issue is especially prevalent in multi-platform software development, where misalignments can lead to bugs, delays, and duplicated effort.

In this document, I will share my observations on this problem and present a practical solution that improves cross-platform API alignment and reduces update uncertainty.

Challenges in API Alignment

In multi-platform development, different teams often use different languages and frameworks, which can lead to inconsistencies in API design and implementation. For example, a web frontend built with React and TypeScript may define and consume APIs differently than a backend service implemented in Go or Java.

These discrepancies can manifest in data format mismatches, inconsistent error handling, and diverging authentication mechanisms. Moreover, when an API is updated, synchronizing the changes across all platforms becomes challenging—often resulting in bugs, regressions, or broken integrations.

In our case, we have a web frontend developed in TypeScript and a backend service written in Rust. Whenever I update the API on the backend, I need to manually adjust the corresponding frontend code to match the new contract. This process is not only tedious but also error-prone, especially for complex and deeply nested data structures. Even now, certain large type definitions remain inconsistent across platforms, making them hard to read, modify, and verify.

Driven by this experience, I started searching for a more reliable solution to improve API alignment and reduce the uncertainty in our update workflow.

Manual Alignment: Discussions and Native Code Definitions

In my initial approach to building a development workflow in my startup company, the only method I could rely on was to discuss API changes with the frontend team and manually adjust the code on both sides. While straightforward, this approach is fraught with challenges. It depends heavily on constant communication and is prone to misunderstandings or misinterpretations of the API contract. In practice, the frontend team may not have access to or familiarity with backend code—and vice versa.

We can't expect all developers to be full-stack engineers. Even if they are, they may not have the time or capacity to read and understand the codebases on both ends. As a student-led startup with limited funding and time, we simply can’t afford this level of overhead—either financially or operationally.

Moreover, this method doesn't scale. As the number of APIs grows, manual alignment becomes increasingly difficult and error-prone. It can easily result in one team being unaware of changes made by another, leading to broken functionality and unexpected behavior.

Group discussions are not a reliable solution. Constant communication is a luxury, especially in a student-led startup where everyone is balancing academic responsibilities with development work. We don’t have the bandwidth to dedicate a person solely to API synchronization, and even if we did, that time would be better spent on actual development.

Without a structured way to enforce API alignment, inconsistencies will inevitably creep in, ultimately slowing development and reducing reliability.

Visual Tools: Defining APIs with ApiFox and Similar Tools

In my search for a more effective solution, I explored several visual API design tools such as ApiFox. These platforms allow teams to visually design and document APIs in a centralized interface, with built-in support for HTTP methods, request/response schemas, authentication headers, and example payloads.

With ApiFox, we were able to define API contracts in a way that was both human-readable and easily shareable. It offered a schema editor with modular model definitions and an integrated documentation generator. This significantly reduced communication friction—rather than discussing every detail repeatedly, we could rely on a shared, visual source of truth. When used properly, these tools help improve clarity and reduce misunderstandings.

However, challenges still remain.

Although ApiFox allows for visual editing, we still need to manually copy schema definitions into our codebases. This process is time-consuming and error-prone, especially when changes are frequent or complex. There is no guarantee that the code written by developers strictly follows the definitions in the visual tool, and inconsistencies can easily slip through.

Another issue we encountered was limited observability in API versioning. While ApiFox provides version history in the UI, it lacks integration with our development workflow and version control system. We can see that a change occurred, but not how or why, nor what parts of the system were affected. This makes it difficult to assess the impact of changes and ensure that all dependent systems are updated accordingly.

In our case, we often ended up with APIs that technically “fit together,” but were subtly out of sync—much like two pieces of wood rejoined after being sawed apart: they still align on the surface, but an internal gap remains. For example, after adding a new field to a response struct in the backend, serde on the frontend was still able to parse the data without raising any exceptions. As a result, the inconsistency went unnoticed.

This kind of superficial consistency is dangerous. It creates a false sense of correctness while concealing real structural problems. Over time, these hidden mismatches accumulate and become harder to detect, making debugging more difficult and increasing the risk of regressions during updates.

Clearly, a better, more integrated approach was needed—one that could combine API definition, code generation, and version traceability in a single, consistent workflow.

Standardization with Swagger

To mitigate the risks of manual inconsistencies and to introduce a more formal contract between systems, we next explored using OpenAPI, formerly known as Swagger, as a machine-readable specification format for defining APIs.

The OpenAPI Specification (OAS) allows developers to describe endpoints, request and response structures, authentication mechanisms, and more, in a structured YAML or JSON format. This specification serves as a contract that both frontend and backend can adhere to, and it can be used to automatically generate documentation, client SDKs, server stubs, and test cases.

By adopting OpenAPI, we gained a more standardized and predictable API development process. We could write or generate the OpenAPI spec from our backend service (in our case, Rust), and use it to:

• Generate server types and client methods via openapi-generator, ensuring that both sides are always in sync with the latest API definitions.
• Validate requests and responses against the schema during development and testing, catching discrepancies early in the process.
• Serve interactive API documentation via Swagger UI, enabling easier collaboration and onboarding, and providing a clear reference for developers working on both ends.
• Trace API changes over VCS, allowing us to see how the API has evolved over time, understanding the impact of changes on different parts of the system and always get notified when a change occurs.

This greatly improved alignment and reduced misunderstandings. For instance, when an endpoint’s request structure changed, we could regenerate the TypeScript definitions and immediately see the compilation errors on the frontend, enforcing strict contract consistency.

However, while powerful, Swagger and OpenAPI were not without their shortcomings.

The YAML format, while expressive, can be verbose and difficult to manage, especially in large-scale systems with numerous endpoints and deeply nested types. Actually, it's not their lack of expressiveness, because OpenAPI is initiaially designed to be a micro-services specification language. But after several iterations, our OpenAPI files ballooned to thousands of lines, becoming increasingly unwieldy and hard to maintain.

We began to feel that while OpenAPI helped enforce consistency, it wasn’t elegant or developer-friendly enough for our growing needs. That’s when I started exploring more declarative, code-centric alternatives, and discovered TypeSpec.

Declarative Design: TypeSpec for OpenAPI Definition

After experiencing the verbosity and maintenance challenges of raw OpenAPI files, I began exploring more developer-friendly and declarative approaches to API specification. That’s when I discovered TypeSpec—a modern, extensible language developed by Microsoft for designing APIs. It enables generating multiple types of API definitions directly from concise, strongly-typed source code.

TypeSpec is designed to be an intuitive and powerful alternative to traditional OpenAPI workflows. Given its design similarities to TypeScript, also developed by Microsoft, it feels familiar to developers with a TypeScript background, making the learning curve relatively shallow.

Unlike manually editing YAML files or using visual tools disconnected from implementation code, TypeSpec allows you to define APIs programmatically using a clean, type-safe syntax. It bridges the gap between developer ergonomics and machine-readable specifications, offering the best of both worlds.

For example, a simple definition in TypeSpec might look like this:

model User {
  id: string;
  name: string;
}

@route("/users")
interface UserService {
  @get
  list(): User[];
}

From this definition, TypeSpec can generate an OpenAPI specification and, with appropriate plugins, produce client SDKs or server stubs in various languages such as TypeScript, C#, or Rust.

This means you can seperate the API definition from the implementation, and use TypeSpec to generate OpenAPI specs, codes, and even server stubs in a single step. It can also be used in Protobuf, GraphQL(https://github.com/microsoft/typespec/issues/4933), and other formats, making it a versatile choice for multi-platform development.

Also, TypeSpec can be validated and tested using its CLI tools, which provide feedback on the correctness of your API definitions. We've used it in out CI/CD pipeline to ensure that our API definitions are always up-to-date and valid before deploying any changes. This has significantly reduced the risk of introducing breaking changes or inconsistencies across platforms.

By introducing TypeSpec into our workflow, we solved many of the pain points we previously encountered:

• No more copy-pasting between visual tools and source code.
• No more guessing whether frontend and backend are aligned.
• No more bloated YAML files that are hard to maintain.

TypeSpec turned API design into a declarative, composable, and reliable process, integrated into our codebase, version control, and tooling ecosystem. It became the missing link in our journey toward true multi-platform API alignment.

Conclusion & Best Practices

Achieving reliable multi-platform API alignment is a common but often underestimated challenge in modern software development. From manual synchronization and scattered discussions to visual tools and raw OpenAPI files, we’ve tried various approaches—each solving part of the problem, but introducing trade-offs of its own.

By evolving our approach from manual discussions to a declarative, tool-driven workflow, we significantly improved our ability to manage APIs across platforms—reducing bugs, boosting confidence in releases, and freeing up time for actual development.

TypeSpec didn’t just solve our API alignment problem—it transformed how we think about API design altogether.