Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.
| Product | Mindshare (%) |
|---|---|
| Amazon SageMaker | 4.0% |
| Databricks | 9.3% |
| KNIME Business Hub | 6.8% |
| Other | 79.9% |
| Type | Title | Date | |
|---|---|---|---|
| Category | Data Science Platforms | Mar 28, 2026 | Download |
| Product | Reviews, tips, and advice from real users | Mar 28, 2026 | Download |
| Comparison | Amazon SageMaker vs Databricks | Mar 28, 2026 | Download |
| Comparison | Amazon SageMaker vs KNIME Business Hub | Mar 28, 2026 | Download |
| Comparison | Amazon SageMaker vs Dataiku | Mar 28, 2026 | Download |
| Title | Rating | Mindshare | Recommending | |
|---|---|---|---|---|
| Databricks | 4.1 | 9.3% | 96% | 93 interviewsAdd to research |
| KNIME Business Hub | 4.1 | 6.8% | 94% | 60 interviewsAdd to research |
| Company Size | Count |
|---|---|
| Small Business | 11 |
| Midsize Enterprise | 11 |
| Large Enterprise | 13 |
| Company Size | Count |
|---|---|
| Small Business | 217 |
| Midsize Enterprise | 114 |
| Large Enterprise | 591 |
Amazon SageMaker was previously known as AWS SageMaker, SageMaker.
DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit
| Author info | Rating | Review Summary |
|---|---|---|
| Python AWS & AI Expert at a tech consulting company | 4.0 | I use Amazon SageMaker to develop an assistant like Siri using BlazingText. It offers valuable integration options and tools, though integration with AWS Lambda could improve. It is fully managed on AWS, simplifying development with pre-trained models and flexible frameworks. |
| Data Lake and MLOps Lead at a energy/utilities company with 10,001+ employees | 3.5 | I've used Amazon SageMaker for years in various data science projects and found it stable and scalable, though scaling operations remains challenging. While effective for ML tasks, broader data infrastructure integration needs improvement. Overall, it's a solid tool. |
| Lead Consultant at Saama | 3.5 | My primary use of Amazon SageMaker involves provisioning for data scientists. I value its Feature Store sharing and Studio UI, though improvements are needed in no-code options and seamless UI updates. Competing solutions include DataIKU and Databricks. |
| Senior Solutions Architect at a tech vendor with 10,001+ employees | 4.0 | I found SageMaker good for ML operations and deployment, with great support. Setup was easy. However, the pricing is high, and GPU availability can be challenging. I rate it an 8/10, though I now mostly use Google's AI ecosystem. |
| President & CEO at Y12 | 4.0 | I use Amazon SageMaker for analytics and customer engagement, achieving a strong 4-5x ROI with valuable call insights. However, its integrations are challenging, and it's quite expensive, requiring AI knowledge for setup. I rate it an 8/10. |
| Data Scientist at a computer software company with 5,001-10,000 employees | 4.0 | I find Amazon SageMaker valuable for its rich ML libraries and seamless AWS integration, particularly its serverless nature and pay-as-you-go model. However, cost and GPU integration still need improvement, especially for large workloads. |
| Senior Actuary at Accelerant Holdings | 4.0 | I use Amazon SageMaker primarily to handle large datasets that exceed my local laptop's capacity, benefiting from its flexible resource selection and intuitive interface. Despite some integration and startup delays, it significantly reduced costs and improved project outcomes. |
| AWS & Azure Engineer at a media company with 11-50 employees | 4.5 | I use Amazon SageMaker with AWS services for building, training, and deploying AI models. Its valuable features include lifecycle configurations and VPC support. However, improving entry-point documentation on the front page would enhance accessibility for users. |
