AWS SageMaker vs Databricks
AWS SageMaker
AWS-native managed machine learning platform for building, training, and deploying ML models at scale.
AWS-committed enterprises building ML pipelines, data scientists prioritizing deep AWS integration, teams needing production-grade model deployment within a single cloud vendor
Databricks
Commercial unified analytics platform providing managed Spark clusters with collaborative notebooks, built on Apache Spark.
Data teams managing complex ETL and analytics, organizations requiring multi-cloud flexibility, enterprises building data lakehouse architectures, collaborative data science teams
Short Answer
SageMaker is AWS's managed ML platform optimized for end-to-end model development within the AWS ecosystem, while Databricks is a unified data and AI platform built on Apache Spark that excels at data engineering and collaborative analytics across multi-cloud environments. SageMaker offers deeper AWS service integration, while Databricks provides superior data lakehouse capabilities and cross-cloud flexibility.
Our Verdict
AI-assistedChoose AWS SageMaker if you're deeply invested in the AWS ecosystem, need extensive AutoML capabilities, and primarily focus on model training and deployment with minimal multi-cloud requirements. Choose Databricks if you need a unified data and AI platform with strong data engineering workflows, require multi-cloud flexibility, or want native lakehouse architecture with superior collaborative features for data teams.
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Choose AWS SageMaker if
AWS-committed enterprises building ML pipelines, data scientists prioritizing deep AWS integration, teams needing production-grade model deployment within a single cloud vendor
Choose Databricks if
Data teams managing complex ETL and analytics, organizations requiring multi-cloud flexibility, enterprises building data lakehouse architectures, collaborative data science teams
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Key Differences at a Glance
Key Facts & Figures
| Metric | AWS SageMaker | Databricks | Diff |
|---|---|---|---|
| Supported ML Frameworks(count) | TensorFlow, PyTorch, Scikit-learn, MXNet, Hugging Face (5 major) | — | — |
| Monthly Subscription Cost (Baseline)(USD) | $0 (pay-per-use: $0.50-50/hour training) | — | — |
| Dashboard Visualization Types(chart types) | 12-15 basic charts | — | — |
| AWS Service Integrations(services) | 200+ AWS services (native) | — | — |
| Real-Time Team Collaboration Features(features) | Basic shared notebooks (3 features) | — | — |
| Starting Compute Cost (per hour)(USD) | $0.23 (ml.t3.medium on-demand) | $0.30 (1 DBU compute) | -23% |
| Pre-built AutoML Models(models) | 50+ algorithms via Autopilot | 12+ model families via AutoML | +317% |
| Real-Time Notebook Collaboration Users(concurrent users) | Up to 5 (with delays) | Unlimited simultaneous editing | — |
| Native AWS Service Integrations(services) | 70+ (S3, RDS, Glue, Lambda, etc.) | 15+ (S3, RDS, Kinesis) | +367% |
| Training Job Spot Instance Discount(%) | Up to 90% savings | Up to 70% savings | +29% |
| SQL Query Performance (sample 1TB table)(seconds) | 45-60 (via Athena integration) | 8-15 (native optimizations) | +333% |
| Starting Monthly Cost(USD) | $1,500-$4,000 | $1,500-$4,000 | — |
| Setup Time(minutes) | 3-7 days | 3-7 days | — |
| Query Performance (TPC-DS)(seconds) | 18-25 | 18-25 | — |
| ML/AI Integration Score(out of 10) | 9/10 | 9/10 | — |
| Global Enterprise Customers(count (2026)) | 6,500+ | 6,500+ | — |
| Setup Time to Production(minutes) | 1-2 weeks | 1-2 weeks | — |
| SQL Query Performance (TPC-DS benchmark)(seconds) | 12-35 seconds (with Delta Lake) | 12-35 seconds (with Delta Lake) | — |
All figures sourced from publicly available data. Last updated Jun 2026.
Key Differences
AWS SageMaker
Machine Learning & Model Training
Databricks
Data Engineering + ML + Analytics🏆
AWS SageMaker
AWS Only
Databricks
AWS, Azure, GCP🏆
AWS SageMaker
Limited (requires external setup)
Databricks
Native Delta Lake support🏆
AWS SageMaker
$0.23-$3.06 per instance hour
Databricks
$0.30-$0.60 per DBU
AWS SageMaker
70+ native AWS services🏆
Databricks
Basic AWS connectivity
AWS SageMaker
Basic sharing, limited real-time
Databricks
Real-time multi-user editing🏆
AWS SageMaker
SageMaker Autopilot included🏆
Databricks
AutoML available as add-on
Full Comparison
| Attribute | AWS SageMaker |  Databricks |
|---|---|---|
| Setup Time(hours) | 45-120 minutes | — |
| Supported ML Frameworks(count) | TensorFlow, PyTorch, Scikit-learn, MXNet, Hugging Face (5 major) | — |
| ML/AI Integration Score(out of 10) | 9/10 | — |
| Monthly Subscription Cost (Baseline)(USD) | $0 (pay-per-use: $0.50-50/hour training) | — |
| Starting Compute Cost (per hour)(USD) | $0.23 (ml.t3.medium on-demand) | $0.30 (1 DBU compute) |
| Starting Monthly Cost(USD) | $1,500-$4,000 | — |
| Dashboard Visualization Types(chart types) | 12-15 basic charts | — |
| Model Deployment Automation(automation level) | Full end-to-end (1-click production deployment) | — |
| AWS Service Integrations(services) | 200+ AWS services (native) | — |
| Real-Time Team Collaboration Features(features) | Basic shared notebooks (3 features) | — |
| Real-Time Notebook Collaboration Users(concurrent users) | Up to 5 (with delays) | Unlimited simultaneous editing |
| Users Per Collaborative Project(concurrent users) | Unlimited with real-time sync | — |
| Community Size(Stack Overflow questions) | 1.2M+ monthly active users | 8,000+ questions |
| Supported Cloud Platforms | AWS only | AWS, Azure, GCP |
| Pre-built AutoML Models(models) | 50+ algorithms via Autopilot | 12+ model families via AutoML |
| Native AWS Service Integrations(services) | 70+ (S3, RDS, Glue, Lambda, etc.) | 15+ (S3, RDS, Kinesis) |
| Delta Lake Support | Third-party integration only | Native Delta Lake engine |
| Training Job Spot Instance Discount(%) | Up to 90% savings | Up to 70% savings |
| SQL Query Performance (sample 1TB table)(seconds) | 45-60 (via Athena integration) | 8-15 (native optimizations) |
| Query Performance (TPC-DS)(seconds) | 18-25 | — |
| SQL Query Performance (TPC-DS benchmark)(seconds) | 12-35 seconds (with Delta Lake) | — |
| Setup Time(minutes) | 3-7 days | — |
| Global Enterprise Customers(count (2026)) | 6,500+ | — |
| Supported Data Formats(types) | All formats (Delta, Parquet, Images, Videos, Audio) | — |
| Data Sharing Standard(technology) | Delta Sharing (open standard) | — |
| Multi-Language Support(languages) | SQL, Python, Scala, R, Java | — |
| Initial Licensing Cost(USD) | $2,000-$15,000/month | — |
| Setup Time to Production(minutes) | 1-2 weeks | — |
| Cluster Management Required(hours/month) | Minimal (<5 hours/month) | — |
| Built-in Security Features(count) | 6+ (SSO, RBAC, audit logging, IP controls, encryption, workspace isolation) | — |
| Supported Data Formats(formats) | All Spark formats + native Delta Lake optimization | — |
DatabricksVisual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
AWS SageMaker
Pros
- 70+ native AWS service integrations (S3, RDS, DynamoDB, Lambda, EC2)
- SageMaker Autopilot for automated model selection and hyperparameter tuning
- End-to-end model governance with ML Lineage tracking
- Integrated Feature Store for enterprise feature management
- Spot Training reduces compute costs by up to 90%
Cons
- Locked into AWS ecosystem with no multi-cloud support
- Steep learning curve for non-AWS users; requires deep AWS knowledge
- Data preparation and lakehouse features are weak compared to specialized tools
Databricks
Pros
- Multi-cloud support across AWS, Azure, and GCP with unified experience
- Native Delta Lake lakehouse architecture with ACID transactions on data lakes
- Real-time multi-user notebook collaboration with live cursors and comments
- Unified SQL, Python, R, and Scala workspaces in single platform
- SQL AI Assistant with natural language query generation
Cons
- Higher baseline costs for small teams; DBU pricing can exceed per-instance compute
- Steeper learning curve for SQL-only teams unfamiliar with Spark/distributed computing
- Less mature AutoML compared to SageMaker Autopilot
Frequently Asked Questions
Databricks is superior for data engineering teams. It provides native Delta Lake support, distributed Spark processing, and unified ETL capabilities. SageMaker focuses primarily on ML model development, requiring external tools for complex data pipeline orchestration. Databricks' SQL, Python, and Scala support in a single workspace is ideal for data engineering workflows.
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