Amazon Bedrock

Definition

Amazon Bedrock is a fully managed service that provides API access to high-performance foundation models (FMs) from Amazon and leading AI companies — including Anthropic (Claude), Meta (Llama), Mistral AI, Cohere, and Amazon’s own Nova and Titan models. Bedrock enables you to build, tune, and deploy generative AI applications without managing ML infrastructure. All model interactions occur within your AWS account — data never leaves your environment.

Available Model Families (2025/2026)

Amazon Nova (Amazon’s flagship models)

• Nova Micro, Lite, Pro: text-only multimodal models optimized for speed and cost
• Nova Premier: most capable reasoning model for complex tasks
• Nova Canvas: image generation
• Nova Reel: video generation

Anthropic Claude

• Claude Sonnet 4.6, Opus 4.6, Haiku 4.5: state-of-the-art reasoning, coding, and analysis
• Available via API with cross-region inference for high availability

Meta Llama

• Llama 4 (Scout, Maverick): open-weight multimodal models
• Available for fine-tuning and custom deployment

Mistral AI

• Mistral Large 3, Ministral 3: European-built models with multilingual strength

Bedrock provides ~100 serverless models accessible via a single API — no GPU infrastructure, no model hosting costs.

Core Bedrock Capabilities

Knowledge Bases (Managed RAG)

• Fully managed Retrieval-Augmented Generation pipeline
• Connect S3 data sources; Bedrock handles chunking, embedding, and sync automatically
• Vector stores: Amazon S3 Vectors, OpenSearch Serverless, Aurora, Pinecone, MongoDB Atlas
• Query via RetrieveAndGenerate API — answers grounded in your documents
• See also: RAG Pipeline

Agents

• Build multi-step AI agents that plan, reason, and take actions
• Agents call your APIs, query knowledge bases, and execute Lambda functions
• Memory: session-level and persistent cross-session memory
• Bedrock AgentCore (new 2025): managed agent runtime with built-in API Gateway integration, memory, and tool execution infrastructure

Guardrails

• Content filtering policies applied to model inputs and outputs
• Block categories: hate speech, violence, sexual content, PII, custom topics
• Applied across any model — consistent safety layer regardless of provider
• Mandatory for regulated industries (healthcare, financial services)

Model Customization

• Fine-tuning: Adapt a base model to your domain using labeled examples
• Continued pre-training: Train on large unlabeled domain corpus
• Custom models are private to your AWS account

Model Evaluation

• Compare models on your own prompts and evaluation criteria
• Automatic scoring (ROUGE, BERTScore) or human evaluation workflows
• Choose the right model for your use case and cost requirements

Bedrock vs Self-Hosted Models

Common Mistakes

Mistake 1: Not using Guardrails in production. Without Guardrails, your application may generate harmful, off-topic, or PII-revealing content. Apply Guardrails regardless of which model you use.

Mistake 2: Skipping model evaluation before choosing a model. Different models have dramatically different performance on specific tasks. Use Bedrock Model Evaluation to benchmark on your actual prompts before committing.

Mistake 3: Treating Bedrock as a single-model service. Bedrock’s value is model diversity — use the cheapest model that meets your quality bar. Haiku-class models are 20x cheaper than Opus-class; use them for classification, routing, and simple extraction tasks.

Related AWS Services

• Amazon Bedrock Knowledge Bases: Managed RAG for grounding responses in your data
• Amazon Bedrock Agents: Build autonomous AI agents with tool use
• Amazon SageMaker: For custom model training, fine-tuning, and high-volume serving
• AWS Lambda: Execute business logic as Bedrock Agent tools
• Amazon S3 Vectors: Vector storage for Bedrock Knowledge Bases (new 2025)

Related FactualMinds Content

• Generative AI on AWS Bedrock
• AWS Bedrock Consulting
• Fine-Tuning vs RAG on AWS Bedrock: When to Use Each