Comparing Capabilities and Costs of Amazon SageMaker and Databricks

When it comes to building and deploying machine learning models, both Amazon SageMaker and Databricks offer powerful platforms with distinct advantages. SageMaker is an AWS product focused on the model development and deployment aspects of machine learning, while Databricks has a broader focus on analytics and data processing in addition to supporting machine learning. The choice between the two will depend on the specific needs and requirements of your organization, users, and cost-sensitivity. 

Amazon SageMaker: A Streamlined, End-to-End Experience 

Amazon SageMaker provides a comprehensive, managed environment for the entire machine learning lifecycle. For customers that are comfortable with a fully AWS-based solution, SageMaker lowers the barrier to entry by integrating AWS services across the entire ML lifecycle. It may be better for teams with heavy data science expertise (i.e., experimentation) but lacking engineering expertise for deployment and monitoring. 

As an all-in-one platform, SageMaker contains various services and capabilities for building, training, and deploying models. It simplifies the deployment process, allowing you to create real-time inference endpoints with just a few lines of code. This can be particularly useful for organizations that need to quickly deploy models to production and monitor their performance. 

In late 2019, SageMaker Studio was introduced as the first fully integrated development environment for machine learning within the SageMaker platform. Both SageMaker and SageMaker Studio have built-in tools to help streamline the model development process and ensure ongoing performance. They both can host Jupyter Notebooks, providing data scientists an easy way to build, train, and deploy models using popular frameworks. The choice often comes down to how easily they fit into existing architectures, as SageMaker Studio may introduce breaking changes. 

One of SageMaker Studio’s biggest draws is the unification of the development environment, which allows data scientists to collaborate more effectively, a strength also found in Databricks. However, SageMaker Studio does this at the cost of modularity and simplicity. While the traditional SageMaker workflow is straightforward and focused on core tasks, the SageMaker Studio environment necessarily adds a layer of abstraction to create a more managed working environment. 

Databricks: Flexibility and Advanced Analytics Capabilities 

Databricks is a broadly focused analytics platform that provides a collaborative environment for big data processing, machine learning tasks, and analytics. It has a robust Notebook-like environment that supports frameworks and data engineering users. The user experience makes collaborating on notebooks easy and is very developer-friendly, with a feel similar to PyCharm or VSCode. It’s great for data engineers and data scientists comfortable with Spark or Python.Databricks allows more flexibility for custom implementations in a single place vs. having to put together separate AWS services as can be needed with SageMaker.  

For example, if you’re working on a complex data pipeline that involves ingesting data from multiple sources, transforming it, and then training a model, Databricks’ integration with data sources and powerful Spark-based processing capabilities can be a significant advantage. It’s a great all-in-one option for multiple user types. 

Databricks also provides advanced features like a feature store and vector database that can be useful for more complex use cases. For instance, you could use Databricks to train a model to detect anomalies in a large dataset, and then deploy that model directly into Amazon Redshift for remote inference. MLFlow, which Databricks created, also integrates directly and streamlines experiment tracking, model packaging for deployment or inference, and model management with either UI or programmatic controls

Comparing the Cost Efficiency of Amazon SageMaker and Databricks 

When it comes to the cost considerations between Amazon SageMaker and Databricks, Databricks is generally regarded as the most cost efficient. Both platforms offer different pricing models and cost-saving opportunities, depending on the specific needs of your organization.

Amazon SageMaker

Amazon SageMaker follows a pay-as-you-go pricing model, where users are charged based on the actual usage of compute resources, storage, and data transfer. This can be beneficial for organizations with more predictable or steady machine learning workloads, as it allows them to scale resources up or down as needed without incurring additional fixed costs. 

For example, if you’re running a web application that needs to perform real-time inference using a machine learning model, SageMaker’s pay-as-you-go pricing can be more cost-effective than provisioning and maintaining a dedicated infrastructure. You only pay for the compute resources used during the inference process, which can scale automatically to handle fluctuations in traffic.


Databricks allows you to scale up and down the cluster size as needed. This can be particularly beneficial for organizations with highly variable or unpredictable machine learning workloads. Cost-efficient compute is one of the areas where Databricks shines, particularly for the data engineering that is needed to prepare data for model execution. Databricks was born out of the creation of Apache Spark, and they continue to innovate with highly efficient compute offerings such as Photon, which seamlessly works with Spark. 

For example, if you’re running a batch processing job that requires a large amount of compute power for a short period, Databricks allows you to spin up a cluster of the appropriate size, run the job, and then shut down the cluster to avoid paying for unused resources. 

Additionally, Databricks provides the option to use spot instances, which can further reduce the cost of compute resources for certain workloads. This can be beneficial for organizations that are able to tolerate some interruptions in their machine learning workflows.

Contact Us for Assistance

If you need help evaluating the cost considerations and making an informed decision between Amazon SageMaker and Databricks, our team of experts is here to assist you. We have extensive experience in analyzing the cost implications of different machine learning platforms and can help you determine the most cost-effective solution for your specific needs. We’ll work closely with you to understand your organization’s requirements, budget, and long-term goals, and provide tailored recommendations to ensure you make the best decision for your needs. 

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