---
title: Which AWS GenAI Approach Should I Use?
description: Bedrock managed models, SageMaker custom training, agents, or RAG on your data? Answer 4 questions and get an opinionated GenAI path with the comparison guide that goes deeper.
url: https://www.factualminds.com/decide/which-aws-genai-approach/
category: ai
publishDate: 2026-08-05
updateDate: 2026-08-05
---

# Which AWS GenAI Approach Should I Use?

> Pick Amazon Bedrock, SageMaker, Agents, or a RAG stack by data sensitivity, customization needs, and team capacity — in 4 questions, with the comparison guide that goes deeper.

## How to use this tree

This tree mirrors how we scope GenAI engagements: start from the product outcome (RAG, agents, generation, training), then decide how much infrastructure you want to own. The goal is a defensible default in under a minute — not an exhaustive catalog of every Bedrock model ID.

If two leaves feel close, walk both and read the comparison guides. The most common healthy starting shape is Bedrock foundation models + Knowledge Bases for RAG, with Agents only when tool use is required.

## Start Here

genai-goal

## Decision Questions

### genai-goal: What are you trying to ship first?

_Start from the product outcome. Model choice follows the workload shape._

- "Chat or Q&A over our documents / knowledge base (RAG)" → rag-ops
- "Call tools / APIs / workflows from a model (agents)" → agent-shape
- "Generate content, summarize, or classify with a foundation model" → fm-customization
- "Train or fine-tune our own model on proprietary data" → train-vs-tune

### rag-ops: How much retrieval infrastructure do you want to own?

_Bedrock Knowledge Bases abstracts chunking, embeddings, and retrieval. Custom RAG gives you full control of the vector store and eval harness._

- "Want managed RAG — sync S3/SharePoint and ship a chat API fast" → rec-bedrock-kb
- "Need custom chunking, hybrid search, or multi-index routing" → rec-custom-rag
- "Mostly need enterprise search / assistants without building agents" → rec-q-business

### agent-shape: How complex is the tool-calling workflow?

_Bedrock Agents cover many enterprise agent patterns. Step Functions wins when the workflow is long-running and deterministic._

- "LLM chooses tools dynamically; short-lived turns" → rec-bedrock-agents
- "Long-running, auditable, mostly deterministic orchestration" → rec-step-functions-agents
- "Need deep custom training loops around the agent runtime" → rec-sagemaker-agents

### fm-customization: Do you need to customize the foundation model?

_Prompting and Bedrock prompt caching cover most apps. Fine-tuning and continued pre-training raise cost and eval burden._

- "Prompt engineering / system prompts are enough" → rec-bedrock-fm
- "Need Bedrock fine-tuning or continued pre-training on our corpus" → rec-bedrock-ft
- "Need full training jobs, custom architectures, or GPU ops control" → rec-sagemaker-train

### train-vs-tune: Is this fine-tuning an existing FM or training from scratch / heavy customization?

_Most teams overestimate the need to train from scratch. Prefer Bedrock fine-tuning unless you have an ML platform team._

- "Fine-tune a Bedrock-supported model on our labeled data" → rec-bedrock-ft
- "Full training / custom model development on GPUs" → rec-sagemaker-train
- "Not sure — start with RAG + prompting first" → rec-bedrock-kb

## Recommendations

### rec-bedrock-kb: Amazon Bedrock Knowledge Bases (managed RAG)

Fastest path to grounded chat over enterprise documents. Bedrock owns embeddings, chunking defaults, and retrieval APIs so your team ships the product layer.
**When to use:**
- You need Q&A over S3, SharePoint, or similar corpora quickly
- You want AWS-managed retrieval with IAM-aligned access
- You do not yet need exotic hybrid search or multi-index routing
**When NOT to use:**
- You need custom chunking strategies, multi-vector routing, or non-Bedrock embedding models
- Your corpus is tiny and a single prompt with context fits the context window
**AWS services:**
- [Amazon Bedrock Consulting](/services/aws-bedrock/)
- [Generative AI on AWS](/services/generative-ai-on-aws/)
**Comparisons:**
- [Bedrock vs SageMaker](/compare/aws-bedrock-vs-sagemaker/)

### rec-custom-rag: Custom RAG (OpenSearch / Aurora pgvector / S3 Vectors) + Bedrock

You own the retrieval plane; Bedrock (or SageMaker endpoints) owns generation. Right when chunking, hybrid search, or eval harnesses need to be first-class.
**When to use:**
- Hybrid BM25 + vector search, multi-index routing, or custom rerankers
- Strict latency or tenancy isolation that managed KB defaults cannot meet
**When NOT to use:**
- You have not validated that managed Knowledge Bases fail your requirements
**AWS services:**
- [Amazon Bedrock Consulting](/services/aws-bedrock/)
- [AWS Data Analytics](/services/aws-data-analytics/)
**Comparisons:**
- [Bedrock vs SageMaker](/compare/aws-bedrock-vs-sagemaker/)

### rec-q-business: Amazon Q Business (existing deployments) / Quick Suite path

Enterprise assistant path for knowledge work without building a custom agent runtime. Prefer for IT/knowledge-worker assistants; not for productized customer-facing agents.
**When to use:**
- Internal knowledge assistants for employees
- You want AWS-managed connectors more than custom UX
**When NOT to use:**
- Customer-facing product agents with custom tool APIs — use Bedrock Agents or custom RAG
**AWS services:**
- [Generative AI on AWS](/services/generative-ai-on-aws/)

### rec-bedrock-agents: Amazon Bedrock Agents

Managed agent runtime for tool use, knowledge bases, and multi-step reasoning without standing up your own orchestrator.
**When to use:**
- Dynamic tool selection with short-lived conversational turns
- You want IAM, CloudWatch, and Bedrock guardrails in one path
**When NOT to use:**
- Long-running deterministic workflows — use Step Functions
- You need full control of training and serving stacks — SageMaker
**AWS services:**
- [Amazon Bedrock Consulting](/services/aws-bedrock/)
**Comparisons:**
- [Bedrock Agents vs Step Functions](/compare/aws-bedrock-agents-vs-step-functions/)

### rec-step-functions-agents: Step Functions + Bedrock (deterministic agentic workflows)

When the workflow is the product — retries, human approval, audit trails — put Step Functions in charge and call Bedrock as a step.
**When to use:**
- Multi-hour workflows with compensation and human-in-the-loop
- Compliance requires an auditable state machine
**When NOT to use:**
- Open-ended conversational tool use with no fixed graph
**AWS services:**
- [AWS Serverless Consulting](/services/aws-serverless/)
- [Amazon Bedrock Consulting](/services/aws-bedrock/)
**Comparisons:**
- [Bedrock Agents vs Step Functions](/compare/aws-bedrock-agents-vs-step-functions/)

### rec-bedrock-fm: Amazon Bedrock foundation models (prompt-first)

Invoke Claude, Llama, Titan, and other FMs via Bedrock APIs. Start here for summarization, classification, and generation before you fine-tune.
**When to use:**
- Prompting meets quality bars on eval sets
- You want multi-model flexibility without managing GPUs
**When NOT to use:**
- You already know you need domain fine-tuning or custom training
**AWS services:**
- [Amazon Bedrock Consulting](/services/aws-bedrock/)
**Comparisons:**
- [Bedrock vs SageMaker](/compare/aws-bedrock-vs-sagemaker/)

### rec-bedrock-ft: Amazon Bedrock fine-tuning / continued pre-training

Customize a Bedrock-supported model on your labeled corpus without owning training clusters. Raise the eval bar before production.
**When to use:**
- Prompting plateaus on domain language or format
- You can produce labeled datasets and offline eval harnesses
**When NOT to use:**
- You need arbitrary architectures or research-grade training loops — SageMaker
**AWS services:**
- [Amazon Bedrock Consulting](/services/aws-bedrock/)
**Comparisons:**
- [Bedrock vs SageMaker](/compare/aws-bedrock-vs-sagemaker/)

### rec-sagemaker-train: Amazon SageMaker (training & custom serving)

Full ML platform control — training jobs, pipelines, endpoints, and experiment tracking. Choose when Bedrock abstractions are the bottleneck.
**When to use:**
- Custom model architectures or heavy fine-tuning on GPUs you control
- Existing SageMaker MLOps investment
**When NOT to use:**
- You only need hosted FMs with light customization — Bedrock is faster
**AWS services:**
- [AWS SageMaker Consulting](/services/aws-sagemaker/)
- [Generative AI on AWS](/services/generative-ai-on-aws/)
**Comparisons:**
- [Bedrock vs SageMaker](/compare/aws-bedrock-vs-sagemaker/)

### rec-sagemaker-agents: SageMaker + custom agent runtime

When agent behavior is tightly coupled to models you train and serve yourself. Higher ops cost; justified only with an ML platform team.
**When to use:**
- Agent quality depends on custom-trained models
- You already operate SageMaker endpoints at scale
**When NOT to use:**
- Standard tool-calling over Bedrock FMs — use Bedrock Agents
**AWS services:**
- [AWS SageMaker Consulting](/services/aws-sagemaker/)

## FAQ

### Should every GenAI app start on Bedrock?
Most product teams should. Bedrock removes GPU capacity management and gives multi-model choice behind one API. Move to SageMaker when you hit hard limits on customization, training control, or existing MLOps investment — not because SageMaker sounds more “serious.”

### When is RAG better than fine-tuning?
RAG wins when answers must cite changing enterprise documents. Fine-tuning wins when you need style, format, or domain language that prompting cannot stabilize. Many production systems use both: RAG for facts, light fine-tuning or strong system prompts for behavior.

### Where do Bedrock Agents fit vs Step Functions?
Use Bedrock Agents when the model must choose tools dynamically in a conversation. Use Step Functions when the workflow graph is mostly known, long-running, and must be auditable. See the Bedrock Agents vs Step Functions comparison for the breakpoint.

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*Source: https://www.factualminds.com/decide/which-aws-genai-approach/*