AWS Bedrock Best Practices Advanced
This final lesson provides comprehensive best practices for building production-grade applications with AWS Bedrock. These practices cover security, cost management, monitoring, prompt engineering, and production deployment patterns.
Production Checklist
Checklist
SECURITY: [ ] IAM roles scoped to specific models (bedrock:InvokeModel) [ ] VPC endpoint for private Bedrock access [ ] CloudTrail logging for all API calls [ ] Guardrails configured for content safety [ ] Input validation before sending to model [ ] Output sanitization before returning to users COST MANAGEMENT: [ ] Token budgets per application/user [ ] CloudWatch metrics for token usage tracking [ ] Smaller models for simple tasks (Haiku vs Sonnet) [ ] Caching for repeated/similar queries [ ] Batch API for offline processing (50% savings) RELIABILITY: [ ] Retry logic with exponential backoff [ ] Circuit breaker for model endpoint failures [ ] Fallback to alternative model on errors [ ] Request timeout configuration [ ] Rate limiting in API Gateway MONITORING: [ ] CloudWatch dashboards for latency, throughput, errors [ ] Custom metrics for response quality [ ] Logging prompts and responses (with PII redaction) [ ] Alert on token budget overruns [ ] Track model invocation costs by application
Cost Optimization Strategies
| Strategy | Savings | Implementation |
|---|---|---|
| Model tiering | 50-80% | Use Haiku for simple tasks, Sonnet for complex ones |
| Prompt caching | Up to 90% | Cache system prompts and repeated context |
| Batch inference | 50% | Use batch API for offline processing |
| Response caching | Variable | Cache identical queries with ElastiCache |
| Token optimization | 20-40% | Concise prompts, lower max_tokens where possible |
Prompt Engineering Tips for Bedrock
- Use system prompts - Set role and constraints in the system message for consistent behavior
- Be specific about format - Tell the model exactly how to structure its output (JSON, lists, tables)
- Include examples - Few-shot prompting dramatically improves output quality for specific tasks
- Set guardrails in prompts - Layer prompt-level instructions with Bedrock Guardrails for defense in depth
- Version your prompts - Store prompts in S3 or Parameter Store for versioning and A/B testing
Course Complete: You now have comprehensive knowledge of building generative AI applications with AWS Bedrock. From foundation model selection to agents and knowledge bases, you are equipped to build production-ready AI applications on AWS.
Continue Your Learning
Explore the full AWS AI infrastructure stack to understand the compute and networking that powers Bedrock.
AWS AI Infrastructure →Ready to Go Deeper?
Live instructor-led courses from our partners. Affiliate disclosure.
AI & ML Courses - 30% Off
Live instructor-led AI, machine learning, data science, and cloud courses for working professionals. Use code Limited30 at checkout.
EdurekaDataCamp - AI & Data Science
Hands-on Python, machine learning, and AI courses with interactive exercises and real projects.
DataCampedX - Top AI Courses
University-level AI courses from MIT, Harvard, Stanford. Earn certificates that employers recognize.
edX