Amazon SageMaker
Reviews
Star rating
Rank in MLOps Platforms
Pros and cons
Pros
- Ease of Use
- Implementation Ease
- Model Management
- Time-Saving
- Features
- Fast Processing
Cons
- Expensive
- Complexity Issues
- Learning Curve
- Complexity
- Steep Learning Curve
- Difficult Learning
Traffic & LLM citations
Source: G2 review data
Winning from
Losing to
Key stats
Adoption by company size
| Segment | Share of reviews |
|---|---|
| Small Business | 30.9% |
| Mid-Market | 34.5% |
| Enterprise | 34.5% |
Quarterly history
| Quarter | Reviews | Star rating |
|---|---|---|
| 2025 Q2 | 2 | 4.5 |
| 2025 Q3 | 4 | 4.3 |
| 2025 Q4 | 2 | 3.8 |
| 2026 Q1 | 1 | 5.0 |
| 2026 Q2 | 8 | 4.4 |
| 2026 Q3 | 1 | 4.5 |
Satisfaction ratings
What reviewers say
SageMaker Brilliantly Scales Training Beyond Local Limits
“Working locally is great—until your dataset outgrows your RAM, or you realize you need a multi-GPU cluster to train a model in hours rather than days. SageMaker addresses that exact friction point, and it does so brilliantly.”
End-to-End ML Workflow in One Tool: Build, Scale, and Monitor
“Best is how it helps in end to end working -model building, scaling, monitoring so that everything is done in one tool.”
Fully Managed End-to-End ML in AWS with Powerful Distributed Training
“Amazon SageMaker’s biggest strength is that it delivers a fully managed, end-to-end machine learning (ML) environment within AWS’s secure ecosystem. It covers the whole workflow, from data preparation and model training to deployment and ongoing monitoring. For teams, this can sa”