LLM Tech Stack & Model Ecosystem

Understand APIs, foundation models, embeddings, open vs closed systems, and infrastructure decisions.

Overview

Modern LLM systems combine APIs, foundation models, embeddings, compute infrastructure, and orchestration layers. Choosing between open‑source and closed‑source models defines cost, control, and performance trade‑offs.

Key Concepts

APIs

Easy access to hosted LLMs with no infrastructure overhead.

Foundation Models

Large pretrained models like GPT, Llama, Claude, and Gemini.

Embeddings

Vector representations for retrieval and semantic search.

Open vs Closed Models

Trade‑offs in performance, cost, transparency, and deployment control.

How the LLM Stack Works

1. Data → Embeddings

Your documents or inputs are transformed into vector embeddings.

2. Model Selection

Pick a foundation model (open or closed) based on needs.

3. Inference & Orchestration

APIs, routers, and compute infrastructure serve responses.

Common Use Cases

Chatbots

Conversational interfaces powered by large models.

RAG Systems

Retrieval‑augmented generation using embeddings.

Automation

Document processing, analysis, and workflow orchestration.

Open vs Closed Models

Open‑Source

• Full control & self‑hosting
• Customization & fine‑tuning
• Lower long‑term cost

Closed‑Source

• Higher performance
• No infrastructure required
• Proprietary and less customizable

FAQ

Do I need my own infrastructure?

Not if you use hosted APIs; open models require compute.

Which model type should I choose?

Closed models for performance, open models for control.

Are embeddings essential?

Yes for search and RAG systems, optional for simple chatbots.

Build Your LLM Stack

Choose your model, infrastructure, and integration strategy.

Get Started