What Is Open Source AI and How Do You Use It?

TL;DR

Open source AI gives users greater control, customization, privacy, auditability, and freedom from vendor lock-in by making core system components publicly available. You can run models locally with a model manager such as Ollama, then connect them to tools, knowledge, memory, guardrails, speech components, and orchestration frameworks to build agents with either no-code tools or code.

Transcript

I learned all about open source AI for you. So here's the cliffnotes version to save you the hours and hours that I have spent digging into this topic and building with open source models and frameworks. So in this video I'm going to explain what is open source AI and why you should care about it. The open source AI stack if you want to build thing... Read More

Key Insights

• Open source AI is an approach in which some or all core components are publicly available, including model architecture, model weights, training or inference code, and licensing that permits use, modification, and redistribution.
• Closed source AI is accessed through controlled services such as APIs, web applications, or enterprise platforms because its model weights and training processes remain proprietary. The transcript identifies GPT, Claude, Gemini, and Grok models as examples.
• DeepSeek R1 marked a major shift in January 2025 because the transcript describes it as the first open source model able to compete with the strongest closed source models available at that time.
• The primary advantages of open source AI are deployment control, customization, lower cost, auditability, reduced vendor lock-in, and collaborative innovation. Users can deploy systems on premises, at the edge, or in a private cloud.
• Local deployment is a privacy option because an open source model does not have to run on its creator's servers. Users can download and host the model themselves, keeping deployment and data handling under their control.
• The main drawbacks of open source AI are setup complexity, hardware requirements, weaker out-of-the-box capabilities, and operational responsibility. Users must manage concerns such as security, scalability, maintenance, and uptime themselves.
• Quantization is helping make open source models smaller, allowing more of them to run on personal computers. Community-built frameworks and packages are also simplifying setup, maintenance, and scaling as the ecosystem develops.
• An open source AI agent uses the same basic components as other agents: models, tools, knowledge, memory, audio and speech, guardrails, and orchestration. The main difference is using an open source model and model-management software such as Ollama.

Questions & Answers

Q: What is open source AI?

Open source AI refers to systems in which some or all core components are publicly available. Those components can include the model architecture, model weights, training code, inference code, and a license permitting use, modification, and redistribution. This differs from closed source AI, whose weights and training process remain proprietary and are generally accessed through APIs, web applications, or enterprise platforms.

Q: Why should companies consider using open source AI?

Companies can consider open source AI for greater control, customization, auditability, privacy, and reduced vendor lock-in. Models can be deployed on premises, at the edge, or in a private cloud. Organizations can fine-tune them, modify their architecture, and add preferred guardrails. The transcript also identifies lower cost and community-driven innovation as important advantages, particularly for regulated industries such as healthcare and finance.

Q: What are the disadvantages of open source AI?

Open source AI can require more setup than a hosted closed source service, and model size may limit what can run on the available hardware. Models can also lack built-in features commonly provided by services such as Claude or GPT. Because no external provider manages the complete system, users must take responsibility for security, scalability, maintenance, uptime, and appropriate infrastructure.

Q: Can open source AI models run locally?

Open source AI models can be downloaded and hosted locally rather than being run on the model creator's servers. A model manager such as Ollama can be installed on a computer, used to download a selected model, and used to run that model locally. The hardware still needs to support the model's size and resource requirements, although quantization is making more models practical on personal computers.

Q: What tools are needed to use an open source AI model?

The basic stack starts with an open source model and model-management software such as Ollama. A simple local setup may stop there, but more capable applications can add tools, knowledge sources, memory, audio and speech features, guardrails, and orchestration. Users can compare models through ranking and evaluation websites, then select compatible frameworks according to whether they prefer a no-code or developer-focused workflow.

Q: How do you build an agent with open source AI?

Building an open source agent uses the same fundamental components as building an agent with a closed source model. Select an open source model, run it through model-management software such as Ollama, and connect it to tools, knowledge, memory, audio and speech components, guardrails, and orchestration. The transcript says frameworks such as n8n, LangGraph, LlamaIndex, OpenAI's Agents SDK, and Google's ADK can support these systems.

Q: How is open source AI becoming easier to run?

Open source AI is becoming easier to run because developers are improving models, producing smaller versions, and creating frameworks and packages that simplify setup and operations. Inference quantization reduces model size, helping more models run on personal computers. Community tools are also making maintenance and scaling more manageable, reducing barriers that previously required commercial-grade hardware and professional coding skills.

Q: Which open source AI models does the video highlight?

As of the video's stated filming date in February 2026, the highlighted model families are Kimi models from Moonshot AI, GLM models from Z.AI, previously referenced as Zhipu AI, and Tencent's Hunyuan models for image processing. The presenter cautions that leadership changes quickly, so users should consult current model rankings, test candidates, and compare them before choosing a model.

Summary & Key Takeaways

• Open source AI systems make some or all core components publicly available, potentially including model architecture, weights, training or inference code, and a license allowing use, modification, and redistribution. Unlike proprietary systems accessed through APIs or hosted applications, these models can be downloaded, customized, audited, and deployed on local or privately controlled infrastructure.

• The main benefits are deployment control, customization, lower costs, auditability, privacy, community-driven innovation, and reduced vendor lock-in. The tradeoffs include more complicated setup, substantial hardware needs, fewer built-in capabilities, and responsibility for security, scalability, and uptime. Quantization, frameworks, and packages are steadily reducing these practical barriers.

• The open source AI stack begins with a model and a model manager such as Ollama. Agentic systems then add tools, knowledge, memory, audio and speech, guardrails, and orchestration. Existing agent concepts still apply, while frameworks such as n8n, LangGraph, LlamaIndex, OpenAI's Agents SDK, and Google's ADK can support open source models.