Open-Source AI Stack Guide
Key Points
- Open‑source AI can be built end‑to‑end with freely available components—models, data pipelines, orchestration, and application layers—offering a multi‑trillion‑dollar value and rapid community‑driven innovation.
- The core of the stack is the model: open‑source options include base LLMs, community‑fine‑tuned variants for specific tasks or domains, and specialized models (e.g., biomedical image anomaly detectors), whereas closed models are accessed via managed APIs.
- Using open‑source models requires you to provide your own inference engine (e.g., Ollama for local use, vLLM or TensorRT‑LLM for servers), while closed‑source APIs handle inference, optimization, and infrastructure for you.
- The data layer is largely similar for both approaches—developers must identify data sources, build connectors, and convert unstructured inputs (like PDFs) into structured formats before feeding them to the model.
Sections
- Open‑Source AI Stack Overview - The passage outlines the key components, benefits, and trade‑offs of building AI systems entirely with open‑source models, data, orchestration, and application layers, guiding developers on when to choose open versus closed AI solutions.
- Open vs Closed AI Deployment - The speaker contrasts open‑source AI stacks—offering full control over deployment location and customizable orchestration through agent frameworks—with closed‑source, fully managed APIs that simplify integration but limit deployment and orchestration flexibility.
Full Transcript
# Open-Source AI Stack Guide **Source:** [https://www.youtube.com/watch?v=_QfxGZGITGw](https://www.youtube.com/watch?v=_QfxGZGITGw) **Duration:** 00:08:21 ## Summary - Open‑source AI can be built end‑to‑end with freely available components—models, data pipelines, orchestration, and application layers—offering a multi‑trillion‑dollar value and rapid community‑driven innovation. - The core of the stack is the model: open‑source options include base LLMs, community‑fine‑tuned variants for specific tasks or domains, and specialized models (e.g., biomedical image anomaly detectors), whereas closed models are accessed via managed APIs. - Using open‑source models requires you to provide your own inference engine (e.g., Ollama for local use, vLLM or TensorRT‑LLM for servers), while closed‑source APIs handle inference, optimization, and infrastructure for you. - The data layer is largely similar for both approaches—developers must identify data sources, build connectors, and convert unstructured inputs (like PDFs) into structured formats before feeding them to the model. ## Sections - [00:00:00](https://www.youtube.com/watch?v=_QfxGZGITGw&t=0s) **Open‑Source AI Stack Overview** - The passage outlines the key components, benefits, and trade‑offs of building AI systems entirely with open‑source models, data, orchestration, and application layers, guiding developers on when to choose open versus closed AI solutions. - [00:04:51](https://www.youtube.com/watch?v=_QfxGZGITGw&t=291s) **Open vs Closed AI Deployment** - The speaker contrasts open‑source AI stacks—offering full control over deployment location and customizable orchestration through agent frameworks—with closed‑source, fully managed APIs that simplify integration but limit deployment and orchestration flexibility. ## Full Transcript
Whether you're creating a simple AI chatbot or a complex AI agent, it's possible to architect a
solution from end-to-end fully with open-source components. Understanding these pieces, how they
work, the benefits, the frictions will help you evaluate where you want to consider
open versus closed solutions in what you're
building. Researchers from Harvard Business School estimate the value of all open-source software—which
means software whose source code is publicly available and distributed freely—to be
worth 8.8 trillion dollars. Within AI specifically, many of the most
exciting new features from commercial AI tools are rapidly recreated as open-source
implementations, which are made by—and distributed freely among—the AI community. Here, we'll cover
the main components of open-source AI—models, data, orchestration and the application layer—and
talk about the tradeoffs for each. Because open source focuses on software, we'll exclude the
infrastructure and hardware layer, but that's a really important consideration too. Deciding
whether to use open or closed AI in your stack is
one of the most important choices a developer will make. The central point of the AI stack is
the model. Different types of open-source models are available. They
can range from LLMs—large language models—that are base tuned, as
well as fine-tuned versions that are created by the community and made available for others to
use. These could be fine-tuned on specific tasks, like question and answer, or on specific domains,
like the legal domain. There's also other specialized models
that are available in the open source. An example of this would be a model to do anomaly detection
in biomedical images. If you're using an open-source model, you will also have to uh, implement
your own inference engine to actually run these models. Options to run these models
include open-source libraries that allow you to run these models on your laptop—one
of the most famous ones being Ollama—or open-source inference engines to run
on a server. Popular examples of this include vLLM or TensorRT LLM.
On the other hand, if you're using a closed model, these are usually available via an API.
You have to worry about making a call to the API, but this often means that the other layers of the
stack are fully managed for you. You don't have to worry about the inference engine to run the model—including
the optimizations to make it efficient—or worry about the infrastructure it runs on. The
next layer of the stack is data. Now, this is one layer where actually the elements
for both open and closed are the same. So, first you have to consider what are your data sources
that you want to bring in to supplement or augment your AI model. There's your data
connectors or integration to pull in data in a more automated way
from tools or other sources. data conversion. If
there is data you want to use in your AI system, but it exists in an unstructured way in a PDF, you
first have to convert that to a more structured format. And then there is RAG
pipelines and vector DBs, which is
where you store your data once it's been vectorized into embeddings so that your model can
pull it into context. Now these elements are the same between open and closed, but what varies is,
of course, one is uh, open-source code that is freely available, one is not. Uh,
so, one benefit of open source is that it's available for free, Closer, closed source
is usually part of a commercial tool. So, one consideration is:
Is it freely available? Next is, with open source, because the source code is
available, you can actually make customizations or adaptations that you need to. Whereas with closed
source, some of those might actually already be built out of the box. But if they aren't, you don't
have the ability to customize.
And then third is control over where it's deployed. So, because the open-source code is
source code that's freely available to you, you can set this up on any server that you choose. You
can keep it on premise, or you can deploy it to a public cloud. With closed offerings, these are
mostly available via an API in a fully managed hosted solution, so you don't have as much control
over where your private data might be going to or coming from.
The next layer is orchestrate. Orchestrate defines how
you break down your AI system into smaller tasks. This could be things like reasoning and
planning how your AI system will tackle a problem.
It could also include executing, so actually making tool calls or function
calls. It could include loops to review what your agent
has k-come up with and improve the quality of the response. How you actually implement these things
is determined by which open-source agent framework you choose.
On the other hand, in a fully closed source stack, there are some commercial platforms that allow
you to do agentic tasks and control the orchestration through an API. So
what you have to worry about is making an API call that matches up with those specs. It is, on
the one hand, simpler, but in some cases might be oversimplified because you don't have as much
control around the exact structure of your agent and ability to customize it uh, as if you're dealing
with an open-source agent framework. Finally, is the application layer. This
defines the interface that your user will use to interact with your AI solution. On the open
side, the solutions to do this emphasize customizability.
So you could use things like open web UI or anything LLM to give you full control over the
user experience. There's also options to optimize for quick setup.
Things like Gradio or Streamlit, which let you very quick-quickly create web-based interfaces
with minimal setup for interacting with an LLM or AI-based solution. On the closed side,
the primary route would be to build from scratch.
So this would mean embedding your AI solution directly in the application, whether it's a web
application or the mobile application that this fits into. Understanding each of these layers—models,
data, orchestration and application—gives you insight to make informed choices. There may be
cases where you want the convenience of prebuilt closed-source solutions, but it's also valuable to
remember open-source AI options, which offer transparent and adaptable solutions that benefit
from community innovations.