This is a solid practical guide. One thing that often gets missed in Bedrock discussions: the model selection decision has architectural implications that compound over time.
When you start with Titan for chatbots and Claude for code generation, you're not just choosing models—you're choosing pricing tiers, latency profiles, and context window constraints that affect downstream architecture.
The Lambda + API Gateway pattern here is clean for getting started, but in production I've seen teams hit three scaling walls:
Cold starts + streaming: Lambda works well for InvokeModel, but InvokeModelWithResponseStream requires connection pooling that Lambda's execution model fights against.
Cost attribution: Bedrock doesn't surface per-request token costs directly. You need CloudWatch custom metrics to track inputTokenCount and outputTokenCount per invocation if you want actual unit economics.
Model drift monitoring: Foundation models update quietly. A prompt that works with Titan v1 might behave differently with v2. Version pinning via model ARN isn't always documented clearly.
The best practices section covers security well, but I'd add: treat your prompt templates like schema contracts. When you send prompt templates into Titan, you're implicitly trusting that input structure. In production, prompt templates should be versioned and validated like API schemas.
For anyone scaling beyond the MVP here: ECS Fargate with connection pooling to Bedrock gives you streaming responses without Lambda cold start latency—and better observability into model behavior over time.
Thanks for the practical walkthrough with actual code samples. The IAM policy snippet and SDK examples save a lot of ramp-up time.