Generative AI Tech Stack – Layer by layer

𝗧𝗵𝗶𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸 — 𝗹𝗮𝘆𝗲𝗿 𝗯𝘆 𝗹𝗮𝘆𝗲𝗿.

Everyone wants AI magic. But creating real value takes more than just a flashy model — it requires thoughtful architectural decisions across a complex system.

Because the future of AI won’t be shaped by models alone. It will be defined by the systems around them: infrastructure, orchestration, data, and governance. Behind every successful AI product is a series of deliberate, system-level choices — and this is where the real work begins.

𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗶𝘀 𝘀𝘁𝗮𝗰𝗸 𝗶𝘀 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 — 𝗹𝗲𝘁’𝘀 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻:

1. Cloud Hosting & Inference → AWS, Azure, GCP, NVIDIA
– The foundation of every GenAI system — providing the scalable compute and infrastructure required to train and serve models at speed and scale.

2. Foundation Models → GPT, Claude, Gemini, Mistral, DeepSeek
– These are the pre-trained engines of intelligence — capable of reasoning, generating, and adapting across a wide range of tasks and domains.

3. Frameworks → LangChain, HuggingFace, FastAPI
– The orchestration layer that enables developers to build structured workflows, chains, and agent systems on top of large models.

4. Vector DBs & Orchestration → Pinecone, Weaviate, Milvus, LlamaIndex
– Responsible for memory, context retrieval, and connecting unstructured data to
AI systems — critical for applications like RAG and agents.

5. Fine-Tuning → Weights & Biases, HuggingFace, OctoML
– The process and tooling that adapt general-purpose models to specific use cases, industries, or internal knowledge — enhancing relevance and accuracy.

6. Embeddings & Labeling → Cohere, ScaleAI, JinaAI, Nomic
– Transform raw data into structured, machine-understandable formats — powering similarity search, semantic indexing, and supervised learning.

7. Synthetic Data → Gretel, Tonic AI, Mostly
– Used when real-world data is limited or sensitive — generating high-quality, privacy-safe data for training, testing, or simulation.

8. Model Supervision → WhyLabs, Fiddler, Helicone
– Enables visibility into model behavior through monitoring, debugging, and performance tracing — essential for reliability and governance.

9. Model Safety → LLM Guard, Arthur AI, Garak
– Ensures responsible AI by enforcing output filtering, ethical constraints, and compliance — critical for enterprise adoption and trust.

If you want to build AI that lasts, you don’t just need better models — you need better systems.

Kudos to ByteByteGo for this brilliant visual.