30/01/2026
Modern AI systems are no longer built from a single model or tool.
They emerge from an ecosystem of interoperating layers that span data, models, orchestration, governance, and deployment.
As open-source innovation accelerates, teams face a new challenge:
understanding how these components fit together into a coherent, production-ready stack.
This infographic maps the modern open-source AI ecosystem across its core layers:
• Foundation Models (LLMs) – powering reasoning and generation
• Embedding Models – enabling retrieval and semantic search
• Vector Databases – storing and querying embeddings efficiently
• Data Sources & Corpora – grounding models in structured and unstructured knowledge
• Data Processing & ETL Tools – preparing data for training and inference
• Model Training Frameworks – training and fine-tuning models at scale
• Prompting & Fine-Tuning Tools – efficiently adapting models to new tasks
• Applications & Use Cases – enabling chatbots, automation, analytics, and retrieval systems
• Security & Governance Layers – enforcing access control, auditability, and policy compliance
• Evaluation & Guardrails – testing hallucinations, bias, safety, and output reliability
• MLOps & Model Management – supporting versioning, monitoring, and experiment tracking
• Deployment & Serving – running models efficiently in production
• Agent & Orchestration Frameworks – coordinating tasks across tools and agents
• RAG & Search Frameworks – connecting LLMs with live and proprietary data
A system-level view for engineers, product leaders, and strategists designing AI with clarity and intent.
See how the stack fits together below.
