Amazon SageMaker vs Databricks
Amazon SageMaker is a dedicated ML platform with managed notebooks, training, and deployment. Databricks is a unified lakehouse platform combining data engineering, SQL analytics, and ML on one platform. Choose SageMaker for focused ML workflows on AWS, Databricks for unified data + ML on a lakehouse architecture.
Quick Comparison
| Feature | Amazon SageMaker | Databricks |
|---|---|---|
| Best For | Fully managed service to build, train, and deploy machine learning models at scale. | Unified analytics and AI platform with lakehouse architecture combining data lake and warehouse |
| Architecture | Cloud-based SaaS | Cloud-native |
| Pricing Model | Pricing based on instance hours and data processing; free tier not available | Standard $289/mo (5TB), Premium $1,499/mo (50TB) |
| Ease of Use | Moderate — standard setup and configuration | Moderate — standard setup and configuration |
| Scalability | High — cloud-native auto-scaling | Moderate — suited for teams and growing companies |
| Community/Support | Commercial support included | Documentation and community forums |
Amazon SageMaker
- Best For:
- Fully managed service to build, train, and deploy machine learning models at scale.
- Architecture:
- Cloud-based SaaS
- Pricing Model:
- Pricing based on instance hours and data processing; free tier not available
- Ease of Use:
- Moderate — standard setup and configuration
- Scalability:
- High — cloud-native auto-scaling
- Community/Support:
- Commercial support included
Databricks
- Best For:
- Unified analytics and AI platform with lakehouse architecture combining data lake and warehouse
- Architecture:
- Cloud-native
- Pricing Model:
- Standard $289/mo (5TB), Premium $1,499/mo (50TB)
- Ease of Use:
- Moderate — standard setup and configuration
- Scalability:
- Moderate — suited for teams and growing companies
- Community/Support:
- Documentation and community forums
Interface Preview
Amazon SageMaker
Feature Comparison
| Feature | Amazon SageMaker | Databricks |
|---|---|---|
| Model Development | ||
| Experiment Tracking | ⚠️ | ⚠️ |
| Model Training | ✅ | ✅ |
| AutoML / Built-in Algorithms | ⚠️ | ⚠️ |
| Deployment & Monitoring | ||
| Model Deployment | ✅ | ⚠️ |
| Model Registry | ⚠️ | ⚠️ |
| Model Monitoring | ⚠️ | ⚠️ |
| General | ||
| Documentation Quality | Good | Good |
| API Availability | ✅ | ✅ |
| Community Support | Active | Active |
| Enterprise Support | ✅ | ✅ |
Model Development
Experiment Tracking
Model Training
AutoML / Built-in Algorithms
Deployment & Monitoring
Model Deployment
Model Registry
Model Monitoring
General
Documentation Quality
API Availability
Community Support
Enterprise Support
Legend:
Our Verdict
Amazon SageMaker is a dedicated ML platform with managed notebooks, training, and deployment. Databricks is a unified lakehouse platform combining data engineering, SQL analytics, and ML on one platform. Choose SageMaker for focused ML workflows on AWS, Databricks for unified data + ML on a lakehouse architecture.
💡 This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Frequently Asked Questions
Should I use SageMaker or Databricks for ML?
SageMaker for dedicated ML workflows (training, tuning, deployment) on AWS. Databricks for teams that want data engineering, SQL analytics, and ML on one platform. If your data is already in Databricks, use Databricks ML; if you're AWS-native, SageMaker integrates better.
Can I use both SageMaker and Databricks?
Yes, some organizations use Databricks for data engineering and feature preparation, then SageMaker for model training and deployment. MLflow (created by Databricks) works with both platforms.
Which is more expensive?
Both are pay-per-use. Databricks charges $0.07-$0.55/DBU on top of cloud compute. SageMaker charges for instances directly ($0.05-$4.90/hour). For pure ML, SageMaker can be cheaper; for combined data+ML, Databricks may be more cost-effective.