If you are just starting to look into AI (or any software) development, you might hear terms like API, SDK, etc. One of my friends who just got into this hype of AI development asked me these questions, and he did not have any technical background: “What are APIs? What are the differences between API and SDK?” I thought it would also be nice to have a simple-to-understand explanation for you guys.
API (Application Programming Interface)
Let’s start with the API. To put it simply, API is how software talks to each other.
If this is too vague, consider it a clearly defined way one program can request services or information from another. Instead of a human clicking a button, one piece of software makes a structured call to another.
Many larger software platforms are providing API services to interact with their software. Let’s take X (formerly Twitter) as an example.
Their APIs are like a set of specific, clearly labeled “service windows” or “access doors,” often called “API endpoints.” Each door is designed for a particular task, e.g., one for posting a tweet, another for getting the followers count, and so on.
Imagine you are building an AI agent. When your bot wants to post a tweet, it goes to the “PostTweet()” door (this might not be the actual name, just an example) provided by the X API, and presents the tweet text in the required format. The API takes care of the rest, ensuring it gets posted correctly within X’s system. Your bot doesn’t need to know the complex inner workings of X; it just needs to learn how to use the designated API doors.
Your next question might be: I am a non-technical person who wants to learn about AI. Do I need to know all these things?
Well, yes.
Today’s most powerful AI capabilities are services that developers can tap into through APIs. Companies like OpenAI (GPT models), Google (Gemini), and Anthropic (Claude) provide APIs. This means developers can integrate sophisticated features like advanced text generation, image creation, or speech recognition directly into their applications without building these incredibly complex AI models from the ground up. APIs are making advanced AI accessible to a much broader range of builders.
SDK (Software Development Kit)
API provides the rules and access points for software communication. But sometimes, especially when you’re working with a complex service or a new platform, just having the list of “doors” (API endpoints) and the rules for knocking isn’t quite enough to get you building quickly and efficiently.
This is where an SDK, or Software Development Kit, becomes incredibly helpful in making a developer’s life easier when working with a particular platform or API.
A helpful analogy for this:
Imagine you’ve bought some flat-pack furniture (IKEA or anything). The API might be the design specifications — how the pieces should connect. The SDK, then, is the box that arrives containing:
- Pre-drilled holes and essential components (these are like code libraries — pre-written chunks of code that handle everyday tasks, like formatting your API requests or managing authentication, so you don’t have to write them from scratch).
- The tools needed for assembly (like helper utilities or specific compilers).
- Clear, step-by-step instructions and diagrams (this is the documentation, code samples, and tutorials that guide you through the process). An SDK bundles these libraries, tools, documentation, and code samples to help developers build applications for a specific platform (like the Android SDK for building Android apps) or integrate a particular service (like using an AI provider’s SDK) much more smoothly.
Suppose the API is the blueprint for the conversation. In that case, the SDK provides ready-made phrases, connectors, and helpful tools in your preferred programming language (like Python, Java, or JavaScript) to make that conversation happen with less friction. It helps you get up and running faster.
How do we call an API?
If we were to interact with an AI model’s API directly without an SDK, we would construct an HTTP (Hypertext Transfer Protocol) request. This involves specifying the endpoint URL, setting headers (for authentication with an API key and content type), and sending a JSON payload with our request details.
Conceptually, using a command-line tool like curl (which sends HTTP requests), it might look something like this:
curl -X POST https://api.ai-provider.com/v1/completions \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "the-ai-model-name",
"prompt": "If I have a next life, I would want to become a...",
"max_tokens": 50
}'
You don’t have to understand what a “curl” is, but it is a tool or interface that we use to send an HTTP request. We are “calling” the API of this AI provider’s services, with the endpoint of “completions.” APIs of these services are generally provided in the form of a URL. In this example, there would be a base URL: ”https://api.ai-provider.com”, and the endpoint is “completions”.
This is a high-level idea of calling an API (there are more concepts about different HTTP request methods, such as the “POST” you see in the example, but we shall save that for later).
How do we use an SDK?
Many AI providers offer SDKs in various programming languages. Let’s imagine we’re using Python with an SDK from an AI provider, similar to how you would use OpenAI’s or Google’s SDK.
from ai_provider_sdk import AIClient
# this is to initialize the client (often handles API key automatically if configured)
client = AIClient()
try:
response = client.completions.create(
model="the-ai-model-name",
prompt="If I have a next life, I would want to become a...",
max_tokens=50
)
# the exact way to access the text will vary by SDK
print(response.choices[0].text.strip())
except Exception as e:
print(f"An error occurred: {e}")
`
For example, the AI provider mentions that they provide a Python SDK for their services. In that case, you could just import the AI provider SDK and use their services. For example, we can initialise the SDK client and use the SDK function (the “client.completions.create()”) to call the services without knowing the underlying API.
You can focus more on what you want the AI to do, rather than the nitty-gritty of how to make the web request. This is why developers love SDKs when they are available, they speed up development and reduce the chance of errors in the communication layer.
Here’s another handy benefit: SDKs can often act as a buffer if the API provider makes behind-the-scenes changes to their API. If the provider, for instance, updates an internal address or tweaks a minor detail, the SDK maintainers will typically update the SDK to adapt. For you, this often means you can update to the newer version of the SDK, and your code that uses the SDK’s functions might not need to change at all, or only require minimal adjustments. This makes maintaining your application easier over time. Of course, for extensive, fundamental changes to an API (which are less common), the SDK itself might have major updates requiring more code changes on your end. However, it is generally a smoother process than deciphering raw API changes yourself.
I hope this article simplifies these two glossaries and ensures you don’t get intimidated by them going forward in learning AI application development!
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