Bedrock vs SageMaker: When to Use Each for Production AI
Two Tools, Very Different Jobs
Amazon Bedrock and SageMaker both fall under the 'AI on AWS' umbrella, but they solve fundamentally different problems. Bedrock gives you API access to foundation models (Claude, Titan, Llama, Mistral) with managed infrastructure. SageMaker gives you a full ML platform for training, fine-tuning, and deploying custom models. Choosing the wrong one wastes months. We've helped teams migrate off SageMaker to Bedrock when they realized they didn't need custom training, and we've moved teams off Bedrock to SageMaker when prompt engineering hit a wall and fine-tuning became necessary.
Start with Bedrock If You Can
If your use case can be solved with a foundation model plus good prompt engineering and RAG (retrieval-augmented generation), Bedrock is the faster path to production. You get managed API endpoints, built-in guardrails, knowledge bases backed by OpenSearch or Pinecone, and no infrastructure to manage. Common Bedrock wins: customer support chatbots, document summarization, content generation, code review assistants, and semantic search. We've shipped Bedrock-based solutions in 2-3 weeks that would have taken 3 months on SageMaker. The cost model is also simpler: you pay per token with no idle compute.
When SageMaker Is Worth the Complexity
SageMaker earns its complexity when you need to train models on proprietary data, run specialized architectures (computer vision, time series, custom NLP), or require fine-tuning that goes beyond what Bedrock's fine-tuning supports. If you're doing fraud detection on your transaction data, demand forecasting with your sales history, or medical image analysis, SageMaker is the right tool. SageMaker also gives you more control over inference infrastructure: custom endpoints, multi-model endpoints, and GPU instance selection. For high-throughput inference where you're running millions of predictions per day, the per-token Bedrock pricing can exceed the cost of a dedicated SageMaker endpoint.
The Architecture We Recommend
For most teams, we recommend starting with Bedrock for any LLM-based feature and using SageMaker only for custom model training. This hybrid approach lets you ship LLM features quickly while investing in custom models only where the ROI is proven. We connect Bedrock to your data layer using Knowledge Bases or custom RAG pipelines, deploy SageMaker endpoints for specialized predictions, and orchestrate everything through Step Functions or your application layer. This avoids the common mistake of over-engineering: building a full SageMaker pipeline for a use case that a well-prompted Claude API call handles perfectly.
Cost Comparison in Practice
On a recent engagement, a client was running a text classification model on a SageMaker ml.g4dn.xlarge endpoint 24/7, costing about $580/month for roughly 50,000 classifications per day. We migrated to Bedrock with Claude Haiku, and the cost dropped to about $90/month for the same volume with comparable accuracy. The SageMaker endpoint was overkill for the task. Conversely, another client running 2 million embeddings per day was spending $3,200/month on Bedrock Titan Embeddings. We moved them to a SageMaker endpoint with an open-source embedding model at $450/month. The right tool depends on volume and task complexity.