GenAI Platform Architecture on AWS
As GenAI moves from experiments to production, organizations need a platform layer that provides shared infrastructure for model access, prompt management, cost controls, evaluation, and observability across teams.
Why a Platform?​
Without a platform, every team builds its own:
- Model integration (different SDKs, different error handling)
- Prompt storage (hardcoded strings, no versioning)
- Cost tracking (surprise bills, no attribution)
- Evaluation (no quality metrics, no regression testing)
A GenAI platform centralizes these concerns so application teams focus on building features, not infrastructure.
Platform Components​
| Layer | Component | Purpose |
|---|---|---|
| Access | Model Gateway | Unified API for multiple models (Claude, Titan, Llama) with routing, fallback, and rate limiting |
| Prompts | Prompt Registry | Version-controlled prompt templates with A/B testing support |
| Cost | Usage Tracking | Per-team, per-application cost attribution and budget enforcement |
| Quality | Evaluation Pipeline | Automated quality scoring, regression detection, human review workflows |
| Safety | Guardrails | Organization-wide content policies, PII detection, denied topics |
| Ops | Observability | Latency, token usage, error rates, cost dashboards across all applications |
What This Course Covers​
| Module | Topic |
|---|---|
| 1 | Platform vs point solutions: when to invest |
| 2 | Model gateway with Bedrock and API Gateway |
| 3 | Prompt registry with versioning and rollback |
| 4 | Cost attribution and budget enforcement |
| 5 | Evaluation pipeline design |
| 6 | Organization-wide guardrails |
| 7 | Observability stack (CloudWatch, X-Ray) |
| 8 | Multi-team governance and access control |
| 9 | Reference architecture (CloudFormation) |
Premium
GenAI Platform Architecture
Get the complete 9-module reference architecture with CloudFormation templates, model gateway implementation, and governance patterns.