AI Stack
An AI stack is a layered architecture that helps teams design, operate, and govern AI systems end to end. Start at the bottom with reliable infrastructure, then move upward through models, data, and orchestration to business-facing applications.
AI Stack Layers (Top to Bottom)
- Application
- Orchestration
- Data
- Model
- Infrastructure
This top-to-bottom view reflects what users see at the top while reminding teams that each upper layer depends on the layer below it.
Visual Layer Diagram
AI Stack at a glance Application -> Orchestration -> Data -> Model -> Infrastructure
Key point Start with stable infrastructure and governance, then build upward to reliable user-facing applications.
1) Application
This is where business value is delivered: copilots, search, recommendation, automation, and decision support experiences.
- Define clear user outcomes and success metrics.
- Design for trust, explainability, and safe user interactions.
- Integrate with existing business workflows and systems of record.
2) Orchestration
Orchestration coordinates prompts, tools, policies, routing, and multi-step workflows across services.
- Manage prompt templates, routing logic, and agent/tool calls.
- Enforce guardrails, approvals, and policy checks.
- Capture observability signals (latency, failures, cost, quality).
3) Data
Data provides context, grounding, and quality signals for training and inference.
- Build governed pipelines for ingestion, transformation, and serving.
- Maintain quality controls, lineage, ownership, and access controls.
- Support retrieval patterns (for example, vector search and metadata filtering).
4) Model
The model layer includes foundation models, fine-tuned models, and evaluation practices.
- Select models by capability, risk, latency, and total cost.
- Evaluate continuously for quality, bias, drift, and robustness.
- Version and govern models with clear release and rollback controls.
5) Infrastructure
Infrastructure is the base layer that powers every other layer: compute, storage, networking, security, and platform operations.
- Provide scalable compute for training and inference.
- Implement reliability, security, secrets management, and disaster recovery.
- Track unit economics with cost controls and capacity planning.
Implementation Guidance
- Build from the bottom up for reliability, then optimize from the top down for user value.
- Assign ownership per layer to avoid cross-team ambiguity.
- Define measurable SLAs across latency, quality, security, and cost.
CTA
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