🧠 Open-Source vs. Closed-Source: What’s the Difference?

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Welcome back AI prodigies!

In today’s Sunday Special:

• 📜The Prelude

• 🤝Open-Source Approach

• 🔐Closed-Source Approach

• 👷Using Open-Source to Build a Fitness Coach

• 🔑Key Takeaway

Read Time: 6 minutes

🎓Key Terms

• Tokens: Units of text that represent words, characters, and punctuation.

• Generative AI (GenAI): When AI models create new content such as text, images, audio, video, or code.

🩺 PULSE CHECK

📜THE PRELUDE

When OpenAI launched ChatGPT in November 2022, GenAI shifted from academic circles to everyday relevance seemingly overnight. Within weeks, millions of people were leveraging the conversational chatbot to draft emails, brainstorm ideas, and double-check their grammar.

But behind the scenes, a divide was forming. As the dust settled, two approaches to GenAI developments emerged: open-source and closed-source. These two approaches shape how GenAI is built, optimized, and deployed.

So, what’s the difference between open-source and closed-source? How do developers leverage open-source resources to build custom GenAI tools?

🤝OPEN-SOURCE APPROACH

When a GenAI tool is open-source, it means the “source code” is publicly available, and developers can freely explore, experiment, and build upon it.

The “source code” refers to the underlying software that defines how a GenAI tool is trained and used.

In simple terms, the “source code” tells a GenAI tool what to do and how to do it. It’s like a recipe, which outlines a specific meal’s exact ingredients, measurements, and preparations. If you can access the recipe, you know how the specific meal was made.

Unlike closed-source GenAI tools, developers can “peek inside” open-source GenAI tools and understand how they work. This ability to “peek inside” offers three distinct advantages:

1. ✅ Collaboration and Innovation

2. ✅ Equity and Accessibility

3. ✅ Trust and Transparency

Popular open-source “foundation models” include Meta’s Llama 4, Mistral AI’s Mistral Small 3.1, and DeepSeek’s DeepSeek-V3-0324. A “foundation model” is trained on massive amounts of general data so developers can use it to build their own GenAI tools for text generation, image recognition, or speech processing.

For example, NVIDIA developed Super-49B-v1, a Reasoning Engine distilled from Llama-3.3-70B-Instruct to excel at complex, multi-step workflows while processing information quickly.

🔐CLOSED-SOURCE APPROACH

When a GenAI tool is closed-source, it means the “source code” isn’t publicly available, and only the company that created it can access, modify, or share it. This privacy provides three distinct advantages:

1. ✅ Control

2. ✅ Security

3. ✅ Economic Sustainability

Developers can still access closed-source GenAI tools via paid APIs, which are automatically updated, managed, and optimized by the company providing them.

For example, the OpenAI Developer Platform offers five tools to help developers build useful, reliable, and powerful AI Agents: 

1. File Search: Allows AI Agents to retrieve relevant information from large documents.

2. Web Search: Provides AI Agents the ability to access up-to-date information from the Internet.

3. Computer Use: Enables AI Agents to navigate your computer autonomously.

4. Agents SDK: Supports building Multi-Agent Systems (MAS), where multiple AI Agents work collectively to tackle complex tasks.

5. Response API: Combines these four tools into a single API Call.

An Application Programming Interface (API) acts as the messenger between software applications, while API Calls are the messages.

Without the Response API tool, developers would have to create tons of different API Calls within complex codebases to equip their AI Agents with these capabilities.

👷USING OPEN-SOURCE TO BUILD A FITNESS COACH

Now, we’ll break down the three core open-source components a developer would leverage to build an AI-powered fitness coach.

1. Documentation: Outlines the technical aspects of building customized AI models. For instance, an AI-powered fitness coach requires the appropriate “tokenization settings” and “vectorization frameworks.” A Tokenizer breaks down sentences into Tokens for processing. A Vectorizer converts those Tokens into numerical representations that the AI-powered fitness coach can understand.

2. Architecture: The internal structure of an AI-powered fitness coach includes Transformer Layers and Attention Heads. Transformer Layers process each word within the context of an entire sentence (i.e., “What’s the best way to tone my arms without a machine?”). Attention Heads help the AI-powered fitness coach focus on the most essential words within the context of the entire sentence, such as “tone,” “arms,” and “machine.”

3. Pre-Trained Weights: Each word must be converted into Vector Embeddings or a list of numbers (e.g., [0.23, -0.56, 0.98]). As Vector Embeddings are processed, Pre-Trained Weights help the AI-powered fitness coach associate and connect words with similar meanings. For example, the word “apple” is often linked with “fruit,” “red,” or “sweet.”

These three core open-source components enable developers to customize a “foundation model” into an AI-powered fitness coach.

1. Documentation ensures the Tokenizer and Vectorizer correctly convert words like “superset” and “max rep” into numerical representations that the AI-powered fitness coach can understand, enabling it to process inputs accurately.

2. Architecture makes sure the Transformer Layers and Attention Heads correctly understand the relationships between words within the context of an entire sentence, allowing the AI-powered fitness coach to generate contextually accurate outputs.

3. Pre-Trained Weights help the AI-powered fitness coach figure out which words within a user query (i.e., “prompt”) should be prioritized to comprehend the request accurately. For example, if a user asks: “How do I strengthen my calves?” the AI-powered fitness coach knows that ”strengthen” and “calves” are essential to understanding the request.

🔑KEY TAKEAWAY

GenAI developments have split into open-source and closed-source. In general, open-source serves developers, while closed-source serves enterprises.

This divide reflects the distinct needs of each group. Open-source offers freedom, flexibility, and collaboration, empowering developers to customize and innovate. Closed-source provides control, security, and reliability, which enterprises need to scale and manage.

📒FINAL NOTE

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