LLM Tech Stack & Model Ecosystem
APIs, foundation models, embeddings, open vs. closed models, and infrastructure choices.
APIs, foundation models, embeddings, open vs. closed models, and infrastructure choices.
The LLM ecosystem includes model providers, APIs, embeddings, vector databases, model hosting choices, and the trade-offs between open-source and closed-source models. Understanding the stack helps teams select the right components for scalability, cost, and performance.
Models like GPT, Claude, Llama, and Mistral that serve as general-purpose reasoning engines.
Providers deliver inference endpoints with safety, reliability, and scaling built-in.
Vector representations for search, retrieval, recommendation, and memory systems.
Documents • Databases • Logs • Media
Converted into vectors for semantic search.
Vector DB surfaces relevant context.
Model generates an answer using retrieved data.
High-accuracy question answering with enterprise data.
Domain-specific assistants built on APIs or self-hosted LLMs.
LLMs coordinating multi-step workflows across systems.
Use closed models for best accuracy; open models for control and customization.
No, but they’re essential for RAG, search, and memory components.
Start with APIs, move to self-hosted only when cost or control necessitate it.
Choose the right foundation model, API, or open-source system to accelerate your AI development.
Get Started