LLM Tech Stack & Model Ecosystem
APIs, foundation models, embeddings, open vs closed models, and infrastructure decisions that shape modern AI systems.
APIs, foundation models, embeddings, open vs closed models, and infrastructure decisions that shape modern AI systems.
Building an AI system involves stacking multiple components: model selection, embeddings for search and memory, APIs for integration, and infrastructure to deploy at scale.
Large-scale pretrained models that serve as the base for downstream tasks.
Vector representations enabling similarity search, retrieval, and context injection.
Interfaces to access LLM capabilities without managing infrastructure.
Models like Llama or Mistral that allow fine‑tuning and full control.
Proprietary models like GPT-4 or Claude with high performance and built‑in safety.
Cloud, on‑prem, or hybrid deployments depending on latency, privacy, and cost.
Choose open or closed models based on use case.
Convert text into vectors for search & memory.
Store embeddings in a vector DB for fast recall.
Connect LLM outputs into products or workflows.
Combine embeddings with LLMs to answer domain‑specific queries.
Orchestrate tools, memory, and APIs for automated tasks.
Specialized AI for legal, medical, finance, or internal workflows.
Yes, if you want retrieval‑based search or memory.
Start with closed models for accuracy, open models for control and cost.
Yes, hybrid orchestration is common in production systems.
Choose the right model, infra, and stack to power your applications.
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