--- source_url: "https://www.qwak.com/compare/vertex-ai-vs-amazon-sagemaker" title: "Vertex AI vs SageMaker: Key ML Platform Comparison | Qwak" mirrored_at: 2026-08-25T03:02:03.545Z host: www.qwak.com cited_in_42a: true mirror_canonical: "https://index.42a.ai/www.qwak.com/compare/vertex-ai-vs-amazon-sagemaker" --- > **Original source:** https://www.qwak.com/compare/vertex-ai-vs-amazon-sagemaker ## Vertex AI vs. Amazon SageMaker on Ease of Use Feature ![Vertex AI](https://cdn.prod.website-files.com/64b3ee21cac9398c75e5d3ac/6576d498ba4e1da8271c0a52_Vertex%20Full.webp) ![Vertex AI](https://cdn.prod.website-files.com/64b3ee21cac9398c75e5d3ac/6576d44330428e640921227a_SageMaker%20Full.webp) Zero-config model build & deploy X X Data source integration GCP AWS data sources Multi-cloud support GCP AWS Intuitive UI X X Support Standard GCP support Standard AWS support ### Vertex AI ease of use While offering a robust set of features, Vertex AI has a steeper learning curve, especially for those not already familiar with Google Cloud Platform. The platform is feature-rich but may require navigating through various services and configurations. ### Amazon SageMaker ease of use SageMaker, though powerful, demands a solid grasp of AWS and engineering expertise. Its UI is less intuitive than specialized platforms, requiring navigation and expertise through multiple AWS services. ## Vertex AI vs. Amazon SageMaker on Model Building and Model Deployment Feature ![Vertex AI](https://cdn.prod.website-files.com/64b3ee21cac9398c75e5d3ac/6576d498ba4e1da8271c0a52_Vertex%20Full.webp) ![Vertex AI](https://cdn.prod.website-files.com/64b3ee21cac9398c75e5d3ac/6576d44330428e640921227a_SageMaker%20Full.webp) Model build system X X Model deployment & serving V V Real-time model endpoints V Engineers required Model auto scaling V Engineers required Model A/B deployments Engineers required Engineers required Inference analytics Engineers required Engineers required Managed notebooks V V Automatic model retraining Engineers required Engineers required ### Vertex AI model building and model deployment Using Vertex AI in production demands a broad skill set, including ML engineering, containerization, Kubernetes orchestration, Infrastructure as Code (with tools like Terraform or Google Cloud Deployment Manager), and networking (VPC, firewall rules). Additional GCP services like Google Cloud Storage, Google Kubernetes Engine (GKE), and Google Cloud Monitoring add complexity, requiring diverse engineering skills for effective management. ### Amazon SageMaker model building and model deployment SageMaker does not have Training Jobs or simple deployment, and its Experiments feature and Studio IDE introduce complexity. The deployment and monitoring processes entail manual engineering setup, with limited out-of-the-box support. ## Vertex AI vs. Amazon SageMaker on Feature Platform Feature ![Vertex AI](https://cdn.prod.website-files.com/64b3ee21cac9398c75e5d3ac/6576d498ba4e1da8271c0a52_Vertex%20Full.webp) ![Vertex AI](https://cdn.prod.website-files.com/64b3ee21cac9398c75e5d3ac/6576d44330428e640921227a_SageMaker%20Full.webp) Managed feature store V V Vector database V V Batch features V Engineers required Realtime features V Engineers required Streaming features Engineers required Engineers required Streaming aggregation features Engineers required X Online and offline store auto sync X X ### Vertex AI feature platform Vertex AI is partially managed, meaning some services are fully managed while others may require manual setup. For example, AutoML is fully managed, but custom training and data pipelines might require additional configurations or integration with other GCP services. ### Amazon SageMaker feature platform The AWS Sagemaker Feature Store requires manual setup for feature processes and lacks support for streaming aggregations, necessitating additional services like Elasticsearch, Chorma, Pinecone, and others for similar functionality. ## Vertex AI vs. Amazon SageMaker on Pricing ### Vertex AI pricing Vertex AI offers various pricing options based on usage and project requirements. ### Amazon SageMaker feature platform SageMaker offers a flexible pay-as-you-go pricing model that's ideal for various project sizes. Users can choose between On-Demand Pricing, with no minimum fees or upfront commitments, and the SageMaker Savings Plans, which provide a flexible, usage-based pricing model in exchange for a consistent ## Vertex AI vs. Amazon SageMaker on Maintenance ### Vertex AI maintenance Managing GCP Vertex AI involves understanding Google Cloud's infrastructure, configuring multiple services, and utilizing its data and AI tools. Key tasks include optimizing costs, ensuring security and compliance, and maintaining efficient data pipelines and ML workflows. Regular updates and platform changes also demand continuous learning and adaptation. ### Amazon SageMaker maintenance Amazon SageMaker's maintenance can be challenging primarily due to its complex features and deep AWS integration. Engineers must navigate a steep learning curve to effectively utilize its extensive options, manage intricate configurations within the AWS ecosystem, and stay updated with frequent service updates. ## Vertex AI vs. Amazon SageMaker on Scalability ### Vertex AI scalabilty Vertex AI is built on Google Cloud, which provides scalable cloud-based ML services. Google's infrastructure is known for its elasticity, making Vertex AI suitable for projects with fluctuating workloads. ### Amazon SageMaker scalability Integrated deeply with Amazon Web Services (AWS), SageMaker leverages AWS's vast infrastructure for significant scalability. This integration makes it a robust solution for organizations with dynamic or growing workloads and those already embedded within the AWS ecosystem while might be changing ## Vertex AI vs. Amazon SageMaker on Support ### Vertex AI support Vertex AI benefits from Google Cloud's support resources, including a community, documentation, and various support plans. Users can access assistance and expertise as needed. ### Amazon SageMaker support Support is provided through the standard AWS support system. ## Compare Vertex AI with Others ![Vertex AI](https://cdn.prod.website-files.com/64b3ee21cac9398c75e5d3ac/65636b4bd69792905384fb86_Vertex%20AI.svg) Vertex AI vs. ## Don’t just take our word for it Qwak was brought onboard to enhance Lightricks' existing machine learning operations. Their MLOps was originally concentrated around image analysis with a focus on enabling fast delivery of complex tabular models. [Read Case Study](https://www.qwak.com/case-studies/lightricks-and-qwak) ![](https://cdn.prod.website-files.com/64b3ee21cac9398c75e5d3ac/656c45eddabb71952a9d6e20_LIGHTRICKS.png) ## More on Vertex AI vs. Amazon SageMaker