--- title: Amazon SageMaker Reviews meta_title: 'Amazon SageMaker Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 57 reviews by the users' company size, role or industry to find out how Amazon SageMaker works for a business like yours. aggregate_rating: rating_value: 4.3 review_count: 57 scale: '5' date_modified: '2026-08-09' parent_category: name: Artificial Intelligence url: https://www.g2.com/categories/artificial-intelligence --- # Amazon SageMaker Reviews **Vendor:** Amazon Web Services (AWS) **Category:** [Data Science and Machine Learning Platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms) **Average Rating:** 4.3/5.0 **Total Reviews:** 57 ## About Amazon SageMaker Amazon SageMaker is a fully managed service that enables data scientists and developers to build, train, and deploy machine learning (ML) models at scale. It provides a comprehensive suite of tools and infrastructure, streamlining the entire ML workflow from data preparation to model deployment. With SageMaker, users can quickly connect to training data, select and optimize algorithms, and deploy models in a secure and scalable environment. Key Features and Functionality: - Integrated Development Environments (IDEs): SageMaker offers a unified, web-based interface with built-in IDEs, including JupyterLab and RStudio, facilitating seamless development and collaboration. - Pre-built Algorithms and Frameworks: It includes a selection of optimized ML algorithms and supports popular frameworks like TensorFlow, PyTorch, and Apache MXNet, allowing flexibility in model development. - Automated Model Tuning: SageMaker can automatically tune models to achieve optimal accuracy, reducing the time and effort required for manual adjustments. - Scalable Training and Deployment: The service manages the underlying infrastructure, enabling efficient training of models on large datasets and deploying them across auto-scaling clusters for high availability. - MLOps and Governance: SageMaker provides tools for monitoring, debugging, and managing ML models, ensuring robust operations and compliance with enterprise security standards. Primary Value and Problem Solved: Amazon SageMaker addresses the complexity and resource-intensive nature of developing and deploying ML models. By offering a fully managed environment with integrated tools and scalable infrastructure, it accelerates the ML lifecycle, reduces operational overhead, and enables organizations to derive insights and value from their data more efficiently. This empowers businesses to innovate rapidly and implement AI solutions without the need for extensive in-house expertise or infrastructure management. ## Amazon SageMaker Pros & Cons **What users like:** - Users find Amazon SageMaker's **ease of use** exceptional, enabling quick adaptation and efficient model training with user-friendly features. (3 reviews) - Users appreciate the **seamless AI integration** of Amazon SageMaker, enhancing the efficiency of the machine learning lifecycle. (2 reviews) - Users appreciate the **superior computing power** of Amazon SageMaker, significantly reducing model training time and enhancing efficiency. (2 reviews) - Users value the **exceptional efficiency** of Amazon SageMaker, significantly reducing model training time and streamlining workflows. (2 reviews) - Users commend the **fast processing** capabilities of Amazon SageMaker, significantly reducing model training time and enhancing usability. (2 reviews) - Users value the **comprehensive managed Jupyter notebooks** and seamless integration with popular ML frameworks and tools. (2 reviews) - Implementation Ease (2 reviews) - Model Management (2 reviews) - Setup Ease (2 reviews) - Time-saving (2 reviews) **What users dislike:** - Users find Amazon SageMaker **expensive** , with complex pricing that leads to unexpected costs for training and deployments. (3 reviews) - Users find the **pricing structure complex** and often face high costs with long training jobs and deployments. (2 reviews) - Users find that the **complexity of pricing** in Amazon SageMaker can lead to unexpected costs and confusion. (2 reviews) - Users note a **steep learning curve** with Amazon SageMaker, particularly for those new to AWS services and setups. (2 reviews) - Users experience a **difficult learning curve** during the initial setup of Amazon SageMaker, which can hinder productivity. (1 reviews) - Users find the **difficult setup** of Amazon SageMaker challenging, impacting their overall experience and cost estimation. (1 reviews) - Integration Difficulty (1 reviews) - Performance Issues (1 reviews) - Steep Learning Curve (1 reviews) ## Amazon SageMaker Reviews ### 1. Blazing Fast Model Training, Intuitive Experience **Rating:** 5.0/5.0 stars **Reviewed by:** Amrendra K. | Indigo squad Member , Small-Business (50 or fewer emp.) **Reviewed Date:** January 02, 2026 **What do you like best about Amazon SageMaker?** I use Amazon SageMaker for building a deep learning model, specifically an object detection model. It's a really great experience for me, especially because my laptop doesn't have advanced GPU support, and training a model would take around 7-8 hours. With Amazon SageMaker's virtual machine, training my deep learning model only takes 3-4 minutes. This platform is great, and even someone who has never used it before can adapt to it the first time and easily understand all the functionality given on SageMaker. I think the virtual machine of Amazon SageMaker is more advanced than the Microsoft Azure platform. It is more effective and less time-consuming. The ease of use is brilliant; I can easily adapt to this platform compared to Microsoft. The initial setup is very easy, and with single authentication, I have access to the resources I need for my work. In my view, I give it 10 out of 10. **What do you dislike about Amazon SageMaker?** This is great platform. I don't dislike this. **What problems is Amazon SageMaker solving and how is that benefiting you?** I use Amazon SageMaker to train deep learning models much faster, reducing training time from 7-8 hours on my laptop to just 3-4 minutes on SageMaker. It's easy to adapt even for first-time users. ### 2. Effortless Prototyping with a Developer-Friendly ML Training Platform **Rating:** 3.5/5.0 stars **Reviewed by:** Vaibhav R. | Full Stack Developer - BA4, Enterprise (> 1000 emp.) **Reviewed Date:** December 22, 2025 **What do you like best about Amazon SageMaker?** I like how easy it is to train ML models on Amazon SageMaker and conduct fast experiments. I can easily prototype and make changes to my ML models, and the training process is straightforward. All the logs are accessible, which helps in checking the training status and testing models. This makes experimenting and changing parameters directly in SageMaker efficient. **What do you dislike about Amazon SageMaker?** Better cost transparency can be there. also, there is a learning curve with initial setup. **What problems is Amazon SageMaker solving and how is that benefiting you?** Amazon SageMaker gives us a single destination to train, deploy, and scale ML models. It reduces the need for separate management, making it easy to prototype and experiment quickly. ### 3. Effortless Login and Simple Setup Make It a Winner **Rating:** 4.0/5.0 stars **Reviewed by:** Pawan N. | Administration and Operations Assistant, Consumer Goods, Enterprise (> 1000 emp.) **Reviewed Date:** December 20, 2025 **What do you like best about Amazon SageMaker?** The login process is straightforward, and setting up the software is not complicated. The user interface is also very user-friendly. **What do you dislike about Amazon SageMaker?** The portal could use some additional finishing touches to appear more presentable. **What problems is Amazon SageMaker solving and how is that benefiting you?** The process of collecting and annotating financial documents is handled efficiently. I found the data collection to be thorough, and the annotation work is accurate, which helps ensure the quality of the financial data. ## Amazon SageMaker Discussions - [What is the best way to integrate Sagemaker models with Kubernetes?](https://www.g2.com/discussions/28784-what-is-the-best-way-to-integrate-sagemaker-models-with-kubernetes) - 1 comment - [How do i make this platform reach to most of my developers?](https://www.g2.com/discussions/27976-how-do-i-make-this-platform-reach-to-most-of-my-developers) - 1 comment - [View Amazon SageMaker pricing details and edition comparison](https://www.g2.com/products/amazon-sagemaker/reviews?filters%5Bsentiment_snippet%5D=2396688&qs=pros-and-cons§ion=pricing&secure%5Bexpires_at%5D=2026-08-14+12%3A44%3A54+-0500&secure%5Bsession_id%5D=b35defde-eef8-4aa8-8b01-ff8ca98f56b5&secure%5Btoken%5D=8d3e09ddb21d6863ec106e738ac3035ad107b6c807314f52a55c3e57208aa71b&format=llm_user) ## Amazon SageMaker Integrations - [Amazon Redshift](https://www.g2.com/products/amazon-redshift/reviews) - [Amazon S3 Glacier](https://www.g2.com/products/amazon-s3-glacier/reviews) - [Amazon Sagemaker Ground Truth](https://www.g2.com/products/amazon-sagemaker-ground-truth/reviews) - [AWS Amplify](https://www.g2.com/products/aws-amplify/reviews) - [AWS Glue](https://www.g2.com/products/aws-glue/reviews) - [AWS Lambda](https://www.g2.com/products/aws-lambda/reviews) - [GitLab](https://www.g2.com/products/gitlab/reviews) ## Amazon SageMaker Features **Additional Functionality** - Tagging - Natural Language Processing - Data Extraction - Multi-Language - Predictive Analytics - Drag & Drop - Speech Recognition - Reporting/Analytics - Data Storage Management - Virtual Personal Assistant (VPA) - AI Copilot - Customer Segmentation - Collaboration Tools - Data Import/Export - Generative AI - For eCommerce - Role-Based Permissions - Customizable Branding - Search/Filter - Monitoring - Document Management - API - Data Visualization - Trend Analysis - Machine Learning - Access Controls/Permissions - Alerts/Escalation - Performance Metrics - Real-Time Data - Third-Party Integrations - Mobile App - Multiple Data Sources - For Sales Teams/Organizations - Sentiment Analysis - Activity Dashboard - Chatbot - Workflow Automation **Additional Functionality** - Code Generation - Text to Image - Generative AI - API - Natural Language Processing - Virtual Characters and Avatars - Content Generation - Personalization and Recommendation - Conditional Generation - Transformer Model - Automated Image & Video Editing - Interactive and Co-Creative Systems - Text Summarization - Data Augmentation - Variation Autoencoder Models - Adversarial Training - Transfer Learning and Fine-tuning - Simulation and Scenario Generation - Creative Design - AI Copilot - Prompt Engineering - Foundation Model **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **System** - Data Ingestion & Wrangling - Real-Time Data **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **Scalability and Performance - Generative AI Infrastructure** - AI High Availability - AI Model Training Scalability - AI Inference Speed **Data Ingestion & Preparation - Low-Code Machine Learning Platforms** - Automatic Data Profiling & Quality Assessment - Multi‑Source Connector Support - Schema Drift / Change Detection **Model Development** - Language Support - Drag and Drop - Pre-Built Algorithms - Model Training - Database Support - Multi-Language **Management** - Cataloging - Monitoring - Governing - Model Registry **Model Development** - Feature Engineering **Operations** - Metrics - Infrastructure management - Collaboration **Cost and Efficiency - Generative AI Infrastructure** - AI Cost per API Call - AI Resource Allocation Flexibility - AI Energy Efficiency **Model Construction & Automation - Low-Code Machine Learning Platforms** - Guided Algorithm & Hyperparameter Recommendation - Code Extensibility - Automated Feature Engineering **Machine/Deep Learning Services** - Computer Vision - Natural Language Processing - Natural Language Generation - Artificial Neural Networks **Machine/Deep Learning Services** - Natural Language Understanding - Deep Learning **Management** - Cataloging - Monitoring - Governing **Integration and Extensibility - Generative AI Infrastructure** - AI Multi-cloud Support - AI Data Pipeline Integration - AI API Support and Flexibility **Deployment** - Managed Service - Application - Scalability **Generative AI** - AI Text Generation - AI Text Summarization **Security and Compliance - Generative AI Infrastructure** - AI GDPR and Regulatory Compliance - AI Role-based Access Control - AI Data Encryption **Additional Functionality** - Customizable Reports - Collaboration Tools - Data Extraction - Semantic Search - Data Storage Management - Ad hoc Reporting - Reporting/Analytics - Predictive Analytics - Activity Dashboard - Access Controls/Permissions - Visual Analytics - Data Mapping - Data Synchronization - Statistical Analysis - Categorization/Grouping - Trend Analysis - Data Profiling - Linked Data Management - Data Visualization - API - Multiple Data Sources - Sentiment Analysis - Search/Filter - Data Import/Export - Data Capture and Transfer - AI Copilot - Monitoring - Data Connectors - Ad hoc Analysis - Text Mining - Reporting & Statistics - Predictive Modeling - Real-Time Analytics - Configurable Workflow - Tagging - Endpoint Management - No-Code - Data Preparation - Auditing - Big Data Analytics - ML Algorithm Library - Data Management - Activity Tracking - Data Security - Workflow Management **Generative AI** - AI Text Generation - AI Text Summarization - AI Text-to-Image - Generative AI **Usability and Support - Generative AI Infrastructure** - AI Documentation Quality - AI Community Activity **Agentic AI - Data Science and Machine Learning Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making - Third-Party Integrations ## Top Amazon SageMaker Alternatives - [Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews) - 4.3/5.0 (727 reviews) - [Dataiku](https://www.g2.com/products/dataiku/reviews) - 4.4/5.0 (213 reviews) - [Azure Machine Learning](https://www.g2.com/products/microsoft-azure-machine-learning/reviews) - 4.3/5.0 (87 reviews)