AI/ML tools
SageMaker vs Bedrock
Should you build custom ML models with SageMaker, or use pre-trained foundation models through Bedrock? Compare both services side-by-side and get a tailored recommendation based on your workload.
SageMaker
Bedrock
Use Case
SageMaker
Custom ML model training, fine-tuning, and deployment. Build, train, and host any ML model from scratch or customize pre-trained models.
Bedrock
Pre-built foundation model inference via API. Access Claude, Titan, Llama, Mistral, and other models without managing infrastructure.
Custom ML model training, fine-tuning, and deployment. Build, train, and host any ML model from scratch or customize pre-trained models.
Pre-built foundation model inference via API. Access Claude, Titan, Llama, Mistral, and other models without managing infrastructure.
Pricing Model
SageMaker
Pay for compute instances (training + inference endpoints), storage, and data processing. Costs scale with instance type and runtime hours.
Bedrock
Pay per token (input + output) for on-demand inference. No upfront costs, no idle capacity charges. Provisioned throughput available for predictable workloads.
Pay for compute instances (training + inference endpoints), storage, and data processing. Costs scale with instance type and runtime hours.
Pay per token (input + output) for on-demand inference. No upfront costs, no idle capacity charges. Provisioned throughput available for predictable workloads.
Model Flexibility
SageMaker
Bring any model — PyTorch, TensorFlow, Hugging Face, scikit-learn, or custom algorithms. Full control over model architecture and training.
Bedrock
Choose from curated foundation models (Claude, Titan, Llama, Mistral, Cohere, Stability AI). Cannot bring arbitrary custom architectures.
Bring any model — PyTorch, TensorFlow, Hugging Face, scikit-learn, or custom algorithms. Full control over model architecture and training.
Choose from curated foundation models (Claude, Titan, Llama, Mistral, Cohere, Stability AI). Cannot bring arbitrary custom architectures.
Fine-Tuning
SageMaker
Full fine-tuning with custom datasets, hyperparameter tuning jobs, and distributed training across GPU clusters. Complete control over the training loop.
Bedrock
Simplified fine-tuning for select models (Titan, Llama, Cohere). Upload your data and Bedrock handles training. Less control but much simpler.
Full fine-tuning with custom datasets, hyperparameter tuning jobs, and distributed training across GPU clusters. Complete control over the training loop.
Simplified fine-tuning for select models (Titan, Llama, Cohere). Upload your data and Bedrock handles training. Less control but much simpler.
Deployment
SageMaker
Real-time endpoints, batch transform, async inference, serverless inference. Full control over instance types, auto-scaling, and multi-model endpoints.
Bedrock
Fully managed API — no endpoints to configure. Just call the InvokeModel API. Optional provisioned throughput for guaranteed capacity.
Real-time endpoints, batch transform, async inference, serverless inference. Full control over instance types, auto-scaling, and multi-model endpoints.
Fully managed API — no endpoints to configure. Just call the InvokeModel API. Optional provisioned throughput for guaranteed capacity.
Operational Overhead
SageMaker
Significant — manage training jobs, endpoint scaling, model versioning, A/B testing, monitoring, and infrastructure. Requires ML engineering expertise.
Bedrock
Minimal — no infrastructure to manage. AWS handles scaling, availability, and model hosting. Focus on prompt engineering and application logic.
Significant — manage training jobs, endpoint scaling, model versioning, A/B testing, monitoring, and infrastructure. Requires ML engineering expertise.
Minimal — no infrastructure to manage. AWS handles scaling, availability, and model hosting. Focus on prompt engineering and application logic.
Best For
SageMaker
Teams with ML engineering expertise who need custom models, full training control, or specialized model architectures not available as foundation models.
Bedrock
Teams building AI-powered applications who want fast time-to-market with pre-trained models, minimal ops overhead, and pay-per-use pricing.
Teams with ML engineering expertise who need custom models, full training control, or specialized model architectures not available as foundation models.
Teams building AI-powered applications who want fast time-to-market with pre-trained models, minimal ops overhead, and pay-per-use pricing.
Build ML infrastructure hands-on
Go beyond comparisons. Deploy SageMaker endpoints and Bedrock applications in a live AWS playground. Follow guided missions that build real ML infrastructure — no simulations.
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