LLM Tech Stack & Model Ecosystem
Understanding APIs, foundation models, embeddings, open vs closed systems, and infrastructure choices.
Understanding APIs, foundation models, embeddings, open vs closed systems, and infrastructure choices.
Modern large language model (LLM) systems rely on a layered technology stack. This includes model APIs, foundational model options, embedding systems, infrastructure environments, and key decisions between open-source and closed-source models.
Interfaces like OpenAI, Anthropic, Google, and open‑model endpoints allow developers to run LLMs without hosting infrastructure.
Base models such as GPT‑4, Claude, Llama, and Mistral serve as general‑purpose reasoning engines trained on large corpora.
Vector representations of text enabling search, retrieval, semantic matching, RAG, and knowledge systems.
Closed models provide cutting‑edge performance; open models provide flexibility, control, and lower costs.
Models can run via cloud APIs, self-hosting on GPUs, edge devices, or optimized inference servers.
Frameworks like LangChain, LlamaIndex, and vector DBs support RAG pipelines and orchestration.
User queries, documents, structured data.
Chunking, vectorization, semantic search.
Foundation model processes prompt + context to generate output.
Safety checks, formatting, validation, enrichment.
APIs, dashboards, agents, automation workflows.
Use embeddings + models to answer questions from private knowledge sources.
LLMs control tools, APIs, and multi‑step workflows.
Semantic matching using vector databases and embeddings.
No. API‑based closed models are often easiest to start with. Self‑hosting is useful for cost control or privacy.
No, but they are essential for retrieval‑augmented generation and semantic search.
Choose closed models for best accuracy and open models for customizability or lower cost.
Start integrating foundation models, embeddings, and model APIs into your workflows.
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