AI Platforms & Infrastructure
Explain the infrastructure needed when AI moves from individual applications into an enterprise-wide capability.
Why This Matters
One team's GPT wrapper does not scale to twenty products. Platforms are how routing, spend, evals, and identity become shared services.
Architecture Position
Platform Layer: model gateways, registries, and developer experience sitting under every application. See it on the architecture map.
Start Here
Core Concepts
AI Gateway
- Provider abstraction
- Authentication
- Routing
- Rate limits
- Usage controls
Model Infrastructure
- Hosted inference
- Private inference
- GPUs
- Serverless inference
- Quantization
Platform Services
- Model registry
- Prompt registry
- Tool registry
- Evaluation services
Cost
- Token accounting
- Budget enforcement
- AI FinOps
- Semantic caching
Multi-Tenancy
- Tenant isolation
- Data isolation
- Resource allocation
Delivery
- CI/CD
- Model versioning
- Prompt versioning
- Feature flags
Developer Platform
- SDKs
- APIs
- Internal developer experience
- Platform governance
Advanced
Advanced material
Deeper guides for this domain are on the way.
Coming SoonArchitecture Patterns
Applied patterns for this domain live in the pattern library.
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Enterprise walkthroughs that apply this topic in a full system.
Browse case studiesRelated Topics
Evaluation, Observability & Reliability
How AI systems are measured, debugged, and operated safely in production.
Open →Distributed Systems for AI
Classic production architecture connected to modern AI systems.
Open →Enterprise AI Architecture & Governance
How these technologies become an enterprise platform and operating model.
Open →Identity & Authorization
How humans, agents, and workloads establish identity and receive authority to act.
Open →Roadmaps
This topic appears in the Enterprise AI Architect path.
Follow the Enterprise AI Architect roadmap