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.
Modern LLM systems rely on interconnected layers of model types, infrastructure systems, and API interfaces. Choosing the right stack impacts performance, cost, and flexibility.
Hosted interfaces for model inference, offering convenience and scalability without managing hardware.
Large pre-trained models such as GPT, Claude, Llama, optimized for reasoning, generation, and instruction following.
Vector representations enabling semantic search, retrieval, classification, and memory systems.
Text, documents, or structured data sent to the model.
API call or self‑hosted inference engine processes the request.
Model outputs vectors or text predictions depending on usage.
RAG, agents, chat, automation, analytics, or custom tools.
Embedding-powered search across knowledge bases.
Conversational flows using foundation model APIs.
Task orchestration, data extraction, and workflow automation.
Easiest setup, highest reliability, provider-controlled scaling.
Mix of local inference and API usage for cost-performance balance.
Full control, optimized cost, hardware required.
No. Embeddings are required for retrieval-based applications like RAG but not for pure generation tasks.
Yes, when deployed properly. They also offer full privacy since data stays within your infrastructure.
APIs are best for convenience; hybrid and self-hosted setups are ideal for scale and customization.
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