A common question we hear from API developers is:

Why would I want an SDK? The API is what matters for developers. An SDK is just a convenient wrapper around the API, right?

Consider that the largest API-forward companies—Stripe, Twilio, and OpenAI, for example—all have first-class, official SDKs. If an SDK was just a convenient wrapper, why would all these API providers have one? That’s because an SDK can be much more than an API wrapper.

A great SDK lets users focus on discovering and using your API, and cuts the time that users spend dealing with irrelevant implementation details. And the quicker that your users can be successful with your API, the likelier it is that they’ll keep using it.

Seamless first impressions

When a developer first uses your API, what does their experience look like?

Suppose you’re a novice developer who’s interested in using OpenAI’s APIs. If there wasn’t an OpenAI SDK, your code might look like this:

  
// Without an SDK

const response = await fetch("https://api.openai.com/v1/responses", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
    "Authorization": `Bearer ${process.env.OPENAI_API_KEY}`,
  },
  body: JSON.stringify({
    "modal": "gpt-4.1",
    "input": "Write a one-sentence bedtime story about a unicorn.",
  })
});
const data = await response.json();

console.log(data);
  
  

By this point, you’ve signed up for an OpenAI API key, and typed several lines of code by copying an example and changing some details. After all that work, you run your program to get an error:

  
{
  error: {
    message: "Missing required parameter: 'model'. You provided 'modal', did you mean to provide 'model'?",
    type: 'invalid_request_error',
    param: 'model',
    code: 'missing_required_parameter'
  }
}
  
  

With the OpenAI SDK, the story is different. You install the SDK (a one-line command, npm install openai), and write a shorter program:

  
// With an SDK

import OpenAI from "openai";
const openai = new OpenAI();

const response = await openai.responses.create({
  model: "gpt-4.1",
  input: "Write a one-sentence bedtime story about a unicorn.",
});

console.log(response);
  
  

There’s no room for making a typo here:

• As soon as you type the m, your editor brings up the auto-completion, and the first suggestion is model.
• You press Tab, and your editor shows inline documentation, right under your cursor: “Model ID used to generate the response.”
• And even if you missed the auto-completion and wrote modal instead, your editor would show you a type hint, showing you the typo before you run into any errors.

Your first interaction with the OpenAI API would not only be error-free, but seamless. A well-designed SDK smooths over the rough edges inherent to programmatic API interaction, giving developers confidence in your product's quality and maturity.

Reduced support burdens

An SDK not only lets users focus more on interacting with your API, it also lets your API developers focus more on issues with the API itself.

Without an SDK, the back-and-forth on a support ticket might look like:

User: I’m having issues with the GET /todos endpoint. Here’s the request I’m making: [pastes request]
Support: The request you’re making seems fine. Can you share your full implementation?
User: Sure, here it is: [pastes 300 lines of code]
Support: [spends an hour reading unfamiliar code]
Support: The retry logic in your request implementation seems not quite correct. Can you try making these changes to your code, and check whether that works? [pastes 300 lines of code]
User: [spends an hour reading unfamiliar code]
User: That works, thanks!

Multiply this by hundreds of interactions, and the hours add up for everyone involved. Now compare what could happen if the user’s API interactions happened via an SDK:

User: I’m having issues with the GET /todos endpoint. Here’s the request I’m making: [pastes request]
Support: The request you’re making seems fine. What SDK and version are you on?
User: I’m on todos-python 2.4.1.
Support: This is a known issue in 2.4.1, and it should be fixed in 2.4.2. Can you try upgrading, and check whether that works?
User: [spends a few seconds upgrading a package]
User: That works, thanks!

Many “build vs. buy” calculations fail to account for how much effort it takes to debug unfamiliar code and switch contexts frequently. The burden of support compounds superlinearly, scaling faster than the number of people using your API. Thus, any effort to reduce this burden pays off superlinearly, and an SDK is an impactful way to do that.

First-party ownership

Without an official SDK, not only will users write their own integrations—they’ll publish them as libraries for other people to use. At first glance, this doesn’t seem like an issue: isn’t it great that users are helping each other? But deeper inspection shows how problematic third-party SDKs can be.

Imagine a world where Anthropic never published an official TypeScript SDK. You go to the NPM package registry and search “Anthropic”. The top three results are:

1. node-anthropic, the most popular of the results, with hundreds of thousands of downloads each week, and an Anthropic logo in the README. It hasn’t been updated in six months, so it doesn’t have some of the newer API changes.
2. anthropic-chat, which is a great, up-to-date SDK, but it only supports the chat completion endpoints. You’re only going to use the chat completion endpoints, so it’s fine for now. You wonder what you’re going to do if you ever have to use the other API endpoints, though.
3. @ai-platform/anthropic, which was last updated a week ago, and has a table in its README with green checkmarks next to each API endpoint. To use it, you also have to install @ai-platform/core, and maybe you need to sign up for an account on ai-platform.com.

Confused with the array of options, you go to your favorite search engine and look up “Anthropic SDK”. The top result is a listicle comparing the three libraries, and concluding that node-anthropic is the best. That listicle was published a year ago. The second result is a blog post from ai-platform.com that, understandably, promotes @ai-platform/anthropic. The third result is a Hacker News thread announcing anthropic-chat, and the comments praise the library’s design, while also criticizing its lack of support for the other Anthropic endpoints.

None of these results are from Anthropic themselves. You’ve spent several minutes researching the options, only to conclude that none of them are good.

In our world, Anthropic does have an official TypeScript SDK. It’s the top result when you search for it in NPM. It’s updated, comprehensive, well-designed, and officially supported. There’s no confusion for developers about which SDK to use. Which world would you rather be in?

High-fidelity analytics

When an HTTP request leads to an error, the first thing an API developer will look at is the metadata. Context is indispensable for investigation. An endpoint with a 5% error rate might be inscrutable on its own, but if those errors all came from clients with Ruby version 3.3.8 and SDK version 1.2.1, then you have a likely method to reproduce it.

Beyond diagnostics, metadata is essential for decision making. If you roll out a new version of your TypeScript SDK, and notice that the TypeScript SDK calls have all switched to the new version, you’ve learned that your users update quickly. If you’re planning to push a change that’ll break the Python SDK, and all the Python API requests you’ve gotten in the past month are from internal users, you can make that change with confidence. If 80% of your API calls are coming from Go users, you might prioritize the Go-related issues on your tracker.

This kind of precise data would be difficult to acquire otherwise. You could email a survey to your users, and maybe you’d get a 10% response rate. You could spend dozens of hours interviewing users and compensating them for their time. Or you could release an SDK.

AI-forward interfaces

In the past few years, waves of AI development have always preceded waves of API usage.

• When GitHub released Copilot in 2022, the front-page TypeScript example turned the prompt “Determine whether the sentiment of a text is positive” to an API request against a web service.
• When OpenAI made GPT4 available for public beta in 2023, its first users interacted with it via chatbox, and its second users interacted via API requests.
• When Anthropic open-sourced the Model Context Protocol in 2024, it gave LLMs the tools to interact with APIs, mediated via MCP server.

It’s impossible to predict what new AI development will come next. We’ll go on-the-record with this prediction, though: your API will integrate better with AI if it has an SDK. This is already true for each of these technologies:

• Copilot, and other AI-assisted code completion tools like Cursor, are much more likely to suggest making API requests with an SDK instead of manually.
• From the beginning, the primary integration path for programmatically interacting with AI-related APIs has always been with an SDK.
• The first APIs to release comprehensive MCP servers were ones that already had SDKs, as both of them are built on the same information.

For AI, a major weakness of a raw API and its documentation is the lack of organized information. An SDK is more than a wrapper: it’s an API interface structured for ease of use, whether that user happens to be a human or a computer.

SDKs as strategic imperative

SDKs have evolved from nice-to-haves to crucial product offerings. They create great developer experiences, reduce internal developer time, and increase confidence in your API. The growth of an API-forward company is often driven by a great SDK, as we’ve seen again and again, over multiple examples.

And as agentic experience starts to supass traditional developer experience, pairing your API with solid, idiomatic SDKs will matter even more.