LLM Tech Stack & Model Ecosystem
APIs, foundation models, embeddings, open vs closed systems, and infrastructure choices.
APIs, foundation models, embeddings, open vs closed systems, and infrastructure choices.
Modern LLM applications rely on a layered tech stack involving APIs, foundation models, embedding systems, and the infrastructure running them. Developers must choose between open and closed models, weigh hosting options, and design the pipeline supporting inference, fine‑tuning, vector search, and integration.
Access models via hosted providers like OpenAI, Anthropic, Google, and others.
Large pretrained models forming the base of modern AI capabilities.
Vector representations powering semantic search, retrieval, and classification.
Self‑hostable and fine‑tunable models like Llama, Mistral, DeepSeek.
Proprietary, highly capable models available via API only.
Options include cloud GPU clusters, managed inference endpoints, and on‑device execution.
User query or structured data.
Semantic search and context building.
Open or closed LLM generates output.
Response, action, or downstream pipeline.
Knowledge retrieval enriched with embeddings.
Autonomous tools orchestrating tasks.
Domain‑specific assistants and automation.
Closed models for quality; open models for customization and scale.
Yes for retrieval‑augmented systems, search, and long‑context tasks.
Only if you need privacy, control, or lower cost at large volumes.
Choose the right models, infrastructure, and architecture for your AI projects.
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