More than 80 percent of AI projects fail – and that’s twice the failure rate of regular IT projects. While companies like GM, Block, McDonald’s, and J.P. Morgan Chase are successfully deploying AI solutions, many organizations struggle to move their machine learning models from promising prototypes to production systems that actually deliver business value.
The challenge isn’t just about having smart data scientists or cutting-edge algorithms. It’s about having the right platform that can handle the entire AI lifecycle – from data preparation to model deployment and monitoring. This is where the choice between Databricks vs SageMaker
Both platforms promise to solve the deployment problem, but they take fundamentally different approaches. SageMaker focuses on streamlined machine learning workflows within AWS, while Databricks emphasizes unified data analytics across multiple clouds. The question isn’t which one has more features – it’s which one fits your team’s workflow and accelerates your path from data to deployed AI solutions. Let’s break down what really matters for your next AI project.
Amazon SageMaker and Databricks: A Quick Overview
What is SageMaker?
Amazon SageMaker is a fully managed machine learning (ML) service from AWS that simplifies the process of building, training, and deploying ML models at scale. Whether you’re a data scientist, a developer, or a business analyst, SageMaker provides all the necessary tools to manage the end-to-end lifecycle of machine learning projects.
Core Focus: Model Development and Deployment
End-to-End Workflow: SageMaker helps you through every step of the ML process, from data collection and model training to deployment and monitoring.
Training & Tuning: It provides built-in algorithms, automatic model tuning, and scalable training environments.
Deployment: Once your model is ready, SageMaker helps deploy it to production, either in the cloud or on edge devices for real-time inference.
Key Target Users: AWS-Native Organizations
Seamless AWS Integration: If your organization is already using AWS, SageMaker fits right in, offering native support for other AWS services like S3 for data storage, IAM for security, and CloudWatch for monitoring.
Multi-user Collaboration: With SageMaker Studio, teams can collaborate easily on ML projects through a web-based IDE.
Primary Strengths: Seamless AWS Integration
Ecosystem Integration: SageMaker is designed to integrate seamlessly with AWS’s ecosystem of services, making it ideal for users who are already leveraging AWS for other cloud infrastructure needs.
Scalability & Flexibility: The platform offers automatic scaling to handle both small and large ML workloads. It’s built to grow with your business as data and model complexity increase.
What is Databricks?
Databricks is a unified analytics platform designed to simplify big data analytics and machine learning (ML) workflows. Built on top of Apache Spark, Databricks offers a collaborative environment that helps teams accelerate the process of gathering insights from large datasets and deploying ML models.
Core Focus: Big Data Analytics + Machine Learning
Big Data Processing:
End-to-End ML Workflow: Beyond data processing, Databricks provides tools for building, training, and deploying machine learning models at scale, leveraging MLflow for model management.
Key Target Users: Multi-Cloud Data Teams
Multi-Cloud Support: Databricks works across multiple cloud providers, including AWS, Azure, and Google Cloud . This flexibility allows organizations to work with the best cloud environment for their needs.
Collaborative Workspace: Databricks is designed for team collaboration, offering shared notebooks and real-time editing, making it ideal for data engineers , data scientists, and analysts working together on projects.
Primary Strengths: Spark-Based Data Processing
Powerful Data Engine:
Scalability and Speed: Whether you’re working with batch processing or real-time streaming data, Databricks provides a robust platform to scale operations efficiently.
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Databricks vs Sagemaker: Head-to-Head Comparison
1. Machine Learning Capabilities
Development Environment SageMaker provides a comprehensive ML-focused development environment with SageMaker Studio, which includes Jupyter notebooks with Python and Scala support. SageMaker also provides the dev platform (Jupyter Notebook) which supports Python and Scala (sparkmagic kernal) developing. The platform offers built-in algorithms and frameworks optimized for machine learning workflows.
Databricks excels with its collaborative notebook environment that many users find superior. Databricks has unbeatable Notebook environment, providing seamless integration with Apache Spark and supporting multiple languages including Python, R, Scala, and SQL in the same workspace.
AutoML and Model Training SageMaker offers AutoPilot for automated machine learning, which can automatically build, train, and tune models. Amazon SageMaker provides a variety of algorithms that you can use to train your own ML models. These algorithms include linear regression, logistic regression, decision trees , random forests, and support vector machines.
Databricks provides AutoML capabilities with integrated MLflow for experiment tracking and model management. SageMaker is a user-friendly no-code platform, with Ground Truth, Canvas and Studio services, while Databricks offers a comprehensive end-to-end platform with seamless MLflow integration.
Experiment Management
SageMaker : Built-in experiment tracking with SageMaker Experiments
Databricks : Native MLflow integration for comprehensive experiment management and model versioning
2. Data Processing & Big Data Analytics
Big Data Handling SageMaker is primarily designed for machine learning workflows and has limitations when handling large-scale data processing. According to consumer reviews, Sagemaker just doesn’t have the same power for large data models as Databricks.
Databricks was built from the ground up for big data analytics with Apache Spark at its core. Databricks provides scalability through its integrated Spark clusters. This makes it an excellent choice for big data. This makes it exceptionally powerful for:
Large-scale data processing
Real-time streaming analytics
Data Pipeline Management SageMaker offers SageMaker Pipelines for ML workflows but is more focused on model training and deployment rather than comprehensive data processing.
Databricks provides robust data pipeline capabilities with:
Native Spark integration for distributed processing
Delta Lake for reliable data storage
Advanced streaming capabilities
Better support for complex data transformations
3. Deployment & Model Serving
Model Deployment Speed SageMaker excels in deployment speed and simplicity. Amazon SageMaker provides a variety of deployment options, including real-time endpoints, batch transform jobs, and multi-model endpoints that can be deployed quickly.
Databricks has more variable deployment times. Databricks’ deployment time varies; simple models can take minutes, but complex scenarios may extend to days, particularly without prior Spark experience.
Production-Ready Features SageMaker provides:
Built-in monitoring and logging
Databricks offers:
Model serving through MLflow
Delta Live Tables for streaming
Advanced monitoring capabilities
Integration with various serving frameworks
4. Cost Analysis
Pricing Structure SageMaker uses a pay-as-you-go model with separate pricing for:
Databricks operates on a Databricks Unit (DBU) system with consumption-based pricing . When it comes to the cost considerations between Amazon SageMaker and Databricks, Databricks is generally regarded as the most cost efficient.
Cost Efficiency Databricks tends to be more cost-effective for:
Large-scale data processing workloads
Organizations with heavy analytics requirements
Teams that need both data processing and ML capabilities
SageMaker can be more cost-effective for:
Pure ML model training and deployment
Organizations already heavily invested in AWS
Projects with predictable ML workloads
5. Integration & Ecosystem
Cloud Integration SageMaker provides seamless integration within the AWS ecosystem:
Native AWS service connectivity
Direct access to AWS data services
Optimized for AWS-centric architectures
Databricks offers multi-cloud flexibility:
Available on AWS, Azure, and Google Cloud
Cross-cloud data sharing capabilities
Consistent experience across cloud providers
Third-Party Integrations SageMaker integrates well with:
Business intelligence tools
AWS marketplace solutions
Databricks provides extensive integrations with:
Multiple data sources and formats
Popular data science libraries
BI and visualization tools
SageMaker offers:
Optimized training instances
Distributed training capabilities
Spot instance support for cost savings
GPU and specialized hardware access
Databricks provides:
Spark-based distributed computing
Auto-scaling cluster management
Optimized Spark performance
Support for various hardware configurations
Scalability Approach SageMaker scales through:
Serverless inference options
Elastic training clusters
Databricks scales via:
Spark’s distributed architecture
Intelligent resource allocation
Cost-optimized scaling strategies
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7. User Experience & Learning Curve
Ease of Use SageMaker is designed for:
Data scientists focused on ML workflows
Teams familiar with AWS services
Organizations wanting managed ML infrastructure
SageMaker is a user-friendly no-code platform, with Ground Truth, Canvas and Studio services
Databricks appeals to:
Data engineers and analysts
Teams working with big data
Organizations requiring collaborative environments
Databricks scores higher on usability, support, pricing, and professional services receiving an 8.8 out of 10 overall
Learning Requirements SageMaker requires:
Understanding of AWS ecosystem
ML model development knowledge
Familiarity with managed services approach
Databricks requires:
Apache Spark knowledge (for advanced features)
Understanding of distributed computing concepts
Data engineering skills for optimal utilization
Documentation and Support SageMaker provides:
Comprehensive AWS documentation
Extensive tutorials and examples
Having all documentation easily accessible on the front page of SageMaker would be a great improvement
Databricks offers:
Databricks users desire improved UI, enhanced data visualization , better integration, clearer error messages, robust support, and comprehensive documentation
Databricks vs Sagemaker: Key Differences
Aspect Databricks SageMaker Primary Focus Big data analytics + machine learning Pure machine learning workflows Development Environment Collaborative notebooks with multi-language support Jupyter-based ML-focused studio Data Processing Spark-based distributed processing for big data Limited data processing, ML-focused AutoML Capabilities AutoML with MLflow integration AutoPilot for automated model building Model Deployment Variable deployment times (minutes to days) Fast, consistent deployment speeds Pricing Model Databricks Unit (DBU) consumption-based Pay-as-you-go for training/inference Cost Efficiency More cost-effective for big data workloads Better for predictable ML workloads Cloud Support Multi-cloud (AWS, Azure, GCP) AWS-only ecosystem Scalability Spark-based auto-scaling clusters Managed infrastructure with auto-scaling Learning Curve Requires Spark knowledge for advanced features AWS ecosystem familiarity needed Best for Big Data Excellent for large-scale data processing Limited big data capabilities Integration Cross-cloud, open-source ecosystem Deep AWS service integration Collaboration Superior collaborative notebook environment Individual-focused development Experiment Tracking Native MLflow integration Built-in SageMaker Experiments User Experience 8.8/10 overall user satisfaction User-friendly no-code platform Documentation Community-driven with professional services Comprehensive AWS documentation Deployment Options MLflow model serving, various frameworks Real-time endpoints, batch transform Data Pipeline Robust ETL with Delta Lake SageMaker Pipelines for ML workflows Target Users Data engineers, analysts, data scientists AWS-focused data scientists Vendor Lock-in Minimal due to multi-cloud approach High due to AWS ecosystem dependency
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Databricks vs SageMaker: Ideal Use Cases
1. End-to-End Machine Learning Workflows SageMaker provides a comprehensive suite of tools for building, training, deploying, and monitoring models. It’s great for teams that need an all-in-one platform for ML.
Example: A healthcare organization building and deploying models for medical image analysis, from data collection to deployment.
2. AWS-Centric Workflows If your organization is heavily invested in AWS, SageMaker integrates seamlessly with other AWS services like S3, Lambda, and Redshift, making it an obvious choice for AWS-centric environments.
Example: A logistics company using AWS services to manage its data and ML models to predict delivery times based on historical data.
3. Automated Model Tuning and Deployment With SageMaker’s built-in automated model tuning (Hyperparameter Optimization) and deployment tools, it’s ideal for organizations that want to deploy models with minimal intervention.
Example: An e-commerce platform using automated recommendations to personalize product suggestions for customers.
4. Edge Deployment and IoT Use Cases SageMaker Neo allows models to be optimized for edge devices, enabling real-time predictions and decision-making at the edge.
Example: A smart device manufacturer deploying ML models to IoT devices like security cameras or wearable devices.
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At Kanerika, we specialize in agentic AI and AI/ML solutions that empower businesses across industries like manufacturing, retail, finance, and healthcare. Our purpose-built AI agents and custom Gen AI models address critical business bottlenecks, driving innovation and elevating operations. With our expertise, we help businesses enhance productivity, optimize resources, and reduce costs.
Our AI solutions offer capabilities such as faster information retrieval, real-time data analysis , video analysis, smart surveillance, inventory optimization, sales and financial forecasting, arithmetic data validation, vendor evaluation, and smart product pricing, among many others.
Through our strategic partnership with Databricks, we leverage their powerful platform to build and deploy exceptional AI/ML models that address unique business needs . This collaboration ensures that we provide scalable, high-performance solutions that accelerate time-to-market and deliver measurable business outcomes. Partner with Kanerika today to unlock the full potential of AI in your business.
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Frequently Asked Questions
Is SageMaker similar to Databricks? SageMaker and Databricks both provide cloud-based solutions for machine learning, but they serve different needs. SageMaker focuses on end-to-end ML workflows within the AWS ecosystem, offering tools for model training, deployment, and monitoring. In contrast, Databricks specializes in big data analytics and Spark-based ML, with strong collaboration features.
Which is better, Databricks or AWS? Choosing between Databricks and AWS depends on your needs. AWS is a comprehensive cloud platform with a wide range of services, while Databricks is specialized in big data analytics and Spark-based ML workflows. If you’re focused on scalable analytics and ML, Databricks may offer better tools, but AWS offers broader flexibility.
Is Databricks good for machine learning? Yes, Databricks is great for machine learning. It integrates well with popular frameworks like TensorFlow and PyTorch and provides MLflow for model tracking and management. The platform is built for collaborative data science work, making it a strong choice for both data engineers and ML practitioners working on large datasets.
Who is Databricks' biggest competitor? Databricks’ biggest competitors include AWS SageMaker, Google AI Platform, and Microsoft Azure ML. These platforms also offer robust tools for big data analytics, machine learning, and model deployment. Each has its strengths, but Databricks stands out for its Spark-based processing and multi-cloud capabilities, making it a strong competitor.
What is the alternative to SageMaker? Alternatives to SageMaker include Databricks, Google AI Platform, Microsoft Azure ML, and IBM Watson Studio. These platforms offer similar capabilities for machine learning model development, training, and deployment. The choice depends on factors like cloud environment, scalability, and specific business requirements.
Is Databricks good for ETL? Yes, Databricks is highly efficient for ETL (Extract, Transform, Load) tasks, particularly when working with large-scale data. Its Spark-based engine allows for fast, distributed data processing, making it an excellent choice for building complex, scalable ETL pipelines that can handle batch and real-time data transformation needs.
Why is Databricks so successful? Databricks’ success lies in its powerful combination of Apache Spark for big data processing, an easy-to-use collaborative workspace, and its flexibility in supporting multi-cloud environments. It provides a seamless experience for data engineers, data scientists, and analysts, enabling efficient workflows for large-scale data processing and machine learning.
Does Databricks do MLOps? Yes, Databricks supports MLOps with tools like MLflow, which enables model versioning, tracking, and deployment. The platform’s end-to-end capabilities for managing machine learning workflows allow teams to streamline the development, deployment, and monitoring of models, making it a strong choice for MLOps in production environments.
Is Databricks a SAAS or PaaS? Databricks is a Platform-as-a-Service (PaaS) offering. It provides a cloud-based platform that enables users to build, deploy, and manage data analytics and machine learning models. Databricks integrates with various cloud providers, offering a unified environment for big data processing and ML without requiring infrastructure management.
