Key Takeaways
- Data stays within your AWS account: models process requests without data leaving your VPC, simplifying compliance for regulated industries.
- Multi-model access through one service: Claude, Llama, Mistral, Cohere, and Amazon Titan available via a consistent API.
- Knowledge Bases implement managed RAG with automatic chunking, embedding, and retrieval from your S3 documents.
- Agents for Bedrock create autonomous assistants that interact with Lambda functions and APIs to complete multi-step business tasks.
- Native AWS security integration: IAM roles, VPC endpoints, CloudTrail audit logging, and KMS encryption work seamlessly.
Available Models and Selection Guide
| Provider | Models | Best For | Pricing Model |
|---|---|---|---|
| Anthropic | Claude 3 Haiku/Sonnet/Opus | General AI, analysis, safety | Per token |
| Meta | Llama 3 8B/70B | Cost-effective processing | Per token |
| Mistral | Mistral 7B/Large, Mixtral | EU compliance, efficiency | Per token |
| Cohere | Command, Embed, Rerank | RAG, search, multilingual | Per token |
| Amazon | Titan Text, Titan Embeddings | AWS-native, embeddings | Per token |
| Stability AI | Stable Diffusion XL | Image generation | Per image |
Security and Compliance Configuration
- VPC endpoints: create PrivateLink endpoints so Bedrock traffic never traverses the public internet
- IAM policies: fine-grained access control specifying which users/roles can invoke which models
- CloudTrail logging: audit every Bedrock API call including model, timestamp, and caller identity
- KMS encryption: encrypt model inputs/outputs with customer-managed keys for maximum data control
- Guardrails: configurable content filters, PII detection, and topic restrictions applied per model deployment
Bedrock inherits AWS compliance certifications (SOC 2, HIPAA, FedRAMP, ISO 27001). For regulated workloads, this means AI capabilities without additional compliance work, leveraging your existing AWS compliance posture.
Knowledge Bases and Agents
Bedrock provides two high-level features that accelerate AI application development:
| Feature | What It Does | Setup Time | Use Case |
|---|---|---|---|
| Knowledge Bases | Managed RAG from S3 documents | Hours | Document Q&A, search |
| Agents | Autonomous multi-step task execution | Days | Business workflow automation |
| Guardrails | Content safety filtering | Hours | Customer-facing applications |
| Model evaluation | Compare models on your data | Hours | Model selection |
Knowledge Bases automatically chunk documents, generate embeddings, store vectors (in OpenSearch Serverless or Pinecone), and handle retrieval. This eliminates the need to build and maintain a custom RAG pipeline.
Cost Management and Optimization
| Pricing Mode | Best For | Discount | Commitment |
|---|---|---|---|
| On-demand | Variable workloads, development | None | None |
| Provisioned throughput | Production, consistent volume | 20-40% | 1-6 month terms |
| Batch inference | Large-scale processing | 50% | None (async) |
Use on-demand pricing during development and testing. Switch to provisioned throughput for production workloads with predictable volume. Use batch inference for non-real-time processing (document analysis, report generation) to save 50% on token costs.
Frequently Asked Questions
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