The Complete LLM Tech Stack

Whether you’re building an AI Agent, fine-tuning a domain-specific LLM, or launching a GenAI-powered product, understanding the modern LLM tech stack is crucial for building real-world AI products. So, in this article, I’ll take you through the complete LLM tech stack you should know to develop & deploy real-world LLM applications.

The Complete LLM Tech Stack

Let’s understand the complete LLM Tech Stack used by professionals by going through the entire pipeline from model selection to deployment.

Model Selection: Use, Fine-Tune, or Train from Scratch

Let’s start with the first big question: Should you use a pretrained model, fine-tune it, or train your own?

Use a pretrained LLM (most common) when:

1. You need fast results (like building an AI chatbot or a content summarizer).
2. The task is generic, or the model generalizes well (e.g., GPT-4, Claude, Gemini, LLaMA, Mistral).

Popular APIs and models to use pretrained LLMs:

1. OpenAI (gpt-4, gpt-3.5-turbo)
2. Anthropic (claude-3)
3. Google (Gemini)
4. Meta (LLaMA 3)
5. Mistral (Mixtral)

Next, Fine-Tune an LLM when:

1. You want better domain adaptation (like in legal, medical, finance).
2. You need tighter control over behaviour or tone.

Tools you can use to fine-tune LLMs:

1. Hugging Face Transformers
2. LoRA / QLoRA (for efficient fine-tuning)
3. PEFT, Axolotl, Llama-Factory
4. Datasets from Hugging Face or your custom data
5. Weights & Biases or Comet for tracking experiments

And, train an LLM from Scratch (Rare) when:

1. You have proprietary data + massive compute budget.
2. You want total control or are building a foundational model.

Stack to train an LLM from scratch includes DeepSpeed, Megatron-LM, TPU/GPU clusters, Ray, NVIDIA NeMo, and MosaicML (now acquired by Databricks).

Data Pipeline: Curation, Cleaning, and Preprocessing

Your model is only as good as the data on which it is trained. So, ML professionals always use tools like:

1. Pandas / Polars – for tabular data
2. LangChain DataLoader – for unstructured documents
3. Hugging Face Datasets – for ready-made LLM datasets
4. LlamaIndex – great for document parsing & chunking (esp. for RAG)
5. Apache Arrow / Parquet – for efficient storage

Vector Databases: For Memory & Retrieval-Augmented Generation (RAG)

RAG = LLM + External Knowledge. Top Vector Databases used by ML professionals include:

1. FAISS (simple, open-source)
2. ChromaDB (lightweight, Pythonic)
3. Weaviate (scalable, semantic filtering)
4. Pinecone (fully managed, great for production)
5. Qdrant (open-source, Rust-based)
6. Milvus, Redis, PGVector (for Postgres lovers)

ML Professionals also use Embedding Models for RAGs, which include:

1. OpenAI Embeddings (text-embedding-3-small)
2. Sentence Transformers (all-MiniLM, bge-base)

Use these to turn your documents into searchable vectors.

Application Frameworks: Orchestrating the LLM

This is where it all comes together. You control how the LLM behaves. ML Professionals use Orchestration Frameworks like:

1. LangChain – agents, chains, tools, RAG pipelines
2. LlamaIndex – better for structured RAG + document loaders
3. CrewAI – multi-agent collaboration (e.g., researcher + writer agents)
4. Haystack – great for QA pipelines
5. Semantic Kernel – from Microsoft, agentic workflows

As a beginner, you can start by learning LangChain and CrewAI.

Infrastructure and Deployment: Where to Run and Deploy Your LLMs

You’ve got options. Depending on your budget and latency needs, here’s what to consider:

1. Cloud APIs (Easy Start): OpenAI, Anthropic, Gemini via hosted APIs
2. Hosted OSS Models: Together AI, Fireworks.ai, Groq, Perplexity Labs
3. Local / On-Prem: Ollama, LM Studio, vLLM

Once you have built it, to package your models, you can use:

1. Docker – for containerizing your app
2. FastAPI / Flask – backend serving the LLM pipeline
3. Gradio / Streamlit – easy frontend for demos
4. LangServe – serve LangChain apps via API

And, for scaling your models, you can use:

1. Kubernetes – container orchestration
2. Cloud Functions / Serverless – event-based deployments
3. AWS SageMaker / Azure ML / GCP Vertex AI – managed LLM infra

To get started, you can build a PDF Q&A chatbot using LangChain + OpenAI + FAISS. Add memory and tool use with LangChain Agents. And deploy it with FastAPI + Docker.

Final Words

Here are some examples of LLM projects you can start with to learn more about LLMs:

1. Building an AI Agent Using OpenAI API
2. Text Summarization Model
3. Building a Retrieval-Augmented Generation Pipeline
4. LLM-Based AI Agent to Generate Responses
5. Building AI Agents with CrewAI
6. Document Analysis Using LLMs

I hope you liked this article on the complete LLM tech stack you should know to develop & deploy real-world LLM applications. Feel free to ask valuable questions in the comments section below. You can follow me on Instagram for many more resources.