SageMaker and Bedrock are both AWS AI services but they serve fundamentally different purposes. Choosing between them - or deciding to use both - is one of the first architecture decisions in any enterprise AI project on AWS.

The Core Distinction

Bedrock is a managed API for accessing pre-trained foundation models. You write prompts, you receive responses. AWS handles everything from model infrastructure to scaling. You do not train, host, or manage any model.

SageMaker is infrastructure for training, hosting, and managing your own models. You bring algorithms, training data, and compute requirements. SageMaker provisions and manages the compute, but you control the model lifecycle.

When Bedrock Is the Right Choice

Bedrock is appropriate when:

  • Your use case is served by an existing foundation model’s capabilities (text generation, summarization, classification, extraction, Q&A)
  • You want to ship fast with minimal ML infrastructure knowledge
  • Your data does not require a custom model - prompting or RAG achieves sufficient quality
  • You need enterprise data controls (your data stays in your AWS account; Bedrock does not use your inputs for training)
  • You are starting an AI project and want to validate the use case before investing in custom infrastructure

Most enterprise AI use cases today are in this category. The majority of projects that start with “we need to train a custom model” discover that a well-prompted foundation model with RAG meets requirements at 10% of the cost and timeline.

When SageMaker Is the Right Choice

SageMaker is appropriate when:

  • You need to fine-tune a foundation model on your proprietary data to achieve required performance on specialized tasks
  • Your use case involves tabular/structured data where traditional ML models (XGBoost, LightGBM) outperform LLMs
  • You need a custom computer vision, time series, or other ML model not available via Bedrock
  • Inference cost requirements at scale favor a self-hosted model over per-token API pricing
  • You need full control over model architecture for research or specialized applications

The Cost Comparison

Bedrock: No fixed costs; pay per token consumed. Claude Haiku (the cheapest tier) processes roughly 2 million tokens for $1. For moderate workloads under 1 million tokens per day, Bedrock is almost always cheaper than self-hosting.

SageMaker: Fixed costs for running inference endpoints (hourly per instance, whether serving requests or idle) plus per-request variable costs. Cost-effective only when utilization is consistently high. An ml.g4dn.xlarge endpoint runs approximately $0.50/hour at full utilization; idle instances represent wasted spend.

For most teams, Bedrock is cheaper until they are running consistently high inference volumes (hundreds of requests per minute sustained). At that scale, provisioned throughput in Bedrock or SageMaker self-hosting become cost-competitive.

Using Both Together

Many mature enterprise AI architectures use both services:

  • Bedrock for LLM-based features: document processing, chatbots, content generation
  • SageMaker for custom models: specialized classifiers, fine-tuned models for domain-specific tasks, tabular ML

The services integrate well. A pipeline might use Textract for document extraction, a SageMaker-hosted custom classifier for routing, then Bedrock for the LLM analysis step - each service doing what it does best.

Decision Summary

Start with Bedrock. If quality requirements cannot be met with prompting and RAG, evaluate fine-tuning via SageMaker JumpStart. If you have custom ML (tabular data, computer vision, time series) or are processing at massive scale, SageMaker becomes the right operational platform.