--- title: "amazon-sagemaker-examples vs generative-ai" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aws-amazon-sagemaker-examples-vs-googlecloudplatform-generative-ai" tools: ["aws-amazon-sagemaker-examples", "googlecloudplatform-generative-ai"] --- # amazon-sagemaker-examples vs generative-ai *GraphCanon updated Sep 20, 2026* ## Verdict Pick amazon-sagemaker-examples if jupyter notebooks for illustrating machine learning workflows on Amazon SageMaker; pick generative-ai if the 'generative-ai' repository offers sample code and notebooks for developing and managing generative AI workflows with Google Cloud's Generative AI and Gemini Enterprise Agent Platform. [amazon-sagemaker-examples](https://sagemaker-examples.readthedocs.io) reports 11k GitHub stars, 7.0k forks, and 854 open issues, last pushed Sep 9, 2026. [generative-ai](https://docs.cloud.google.com/gemini-enterprise-agent-platform/) has 18k stars, 4.5k forks, and 90 open issues, last pushed Sep 17, 2026. Figures are from public GitHub metadata via [amazon-sagemaker-examples's repository](https://github.com/aws/amazon-sagemaker-examples) and [generative-ai's repository](https://github.com/GoogleCloudPlatform/generative-ai). | | [amazon-sagemaker-examples](/tools/aws-amazon-sagemaker-examples.md) | [generative-ai](/tools/googlecloudplatform-generative-ai.md) | | --- | --- | --- | | Tagline | Jupyter notebooks for building, training, and deploying ML models using Amazon SageMaker | Sample code and notebooks for Generative AI on Google Cloud, with Gemini Enterprise Agent Platform | | Stars | 10,990 | 17,726 | | Forks | 6,955 | 4,463 | | Open issues | 854 | 90 | | Language | Jupyter Notebook | Jupyter Notebook | | Adopt for | Jupyter notebooks for illustrating machine learning workflows on Amazon SageMaker | The 'generative-ai' repository offers sample code and notebooks for developing and managing generative AI workflows with Google Cloud's Generative AI and Gemini Enterprise Agent Platform. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0, allowing free use for any purpose with conditions on attribution and license preservation | Apache-2.0 | | Categories | Inference & Serving, Model Training | AI Agents, Data & Retrieval, Developer Tools, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [amazon-sagemaker-examples](/tools/aws-amazon-sagemaker-examples.md) | [generative-ai](/tools/googlecloudplatform-generative-ai.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 10d | 0d | | Open issues (now) | 854 | 90 | | Stars delta | +6 (30d) | +132 (30d) | | Open issues delta | +5 (30d) | +3 (30d) | | Full report | [trust report](/tools/aws-amazon-sagemaker-examples/trust.md) | [trust report](/tools/googlecloudplatform-generative-ai/trust.md) | ## Decision facts: amazon-sagemaker-examples - **Adopt for:** Jupyter notebooks for illustrating machine learning workflows on Amazon SageMaker - **License detail:** Apache-2.0, allowing free use for any purpose with conditions on attribution and license preservation ## Decision facts: generative-ai - **Adopt for:** The 'generative-ai' repository offers sample code and notebooks for developing and managing generative AI workflows with Google Cloud's Generative AI and Gemini Enterprise Agent Platform. ## Choose when ### Choose amazon-sagemaker-examples if… - Tags unique to amazon-sagemaker-examples: aws, data-science, deep-learning, inference. - When you need examples specific to building models with Amazon SageMaker ### Choose generative-ai if… - Tags unique to generative-ai: agents, gcp, gemini, gemini-api. - Also covers AI Agents, Data & Retrieval, Developer Tools. - When you need to leverage the latest Gemini models, such as Gemini 3.8 Flash, for generative AI tasks on Google Cloud. ## When NOT to use amazon-sagemaker-examples - For non-AWS environments where cost and integration complexities could outweigh benefits - If seeking open-source tools without ties to a single cloud provider ## When NOT to use generative-ai - If you are looking for a platform that supports a wide range of cloud providers beyond Google Cloud. - When your project does not require the specific features of the Gemini models or the Gemini Enterprise Agent Platform. - If your project does not benefit from Google-managed solutions like Agent Search and you prefer to build everything from scratch. - When you do not need the integration capabilities with Google Cloud services such as Vertex AI and the Gemini Enterprise Agent Platform. ## Common questions ### What is the difference between amazon-sagemaker-examples and generative-ai? amazon-sagemaker-examples: Jupyter notebooks for building, training, and deploying ML models using Amazon SageMaker. generative-ai: Sample code and notebooks for Generative AI on Google Cloud, with Gemini Enterprise Agent Platform. See the comparison table for live GitHub stats and shared categories. ### When should I choose amazon-sagemaker-examples over generative-ai? Choose amazon-sagemaker-examples over generative-ai when Tags unique to amazon-sagemaker-examples: aws, data-science, deep-learning, inference; When you need examples specific to building models with Amazon SageMaker. ### When should I choose generative-ai over amazon-sagemaker-examples? Choose generative-ai over amazon-sagemaker-examples when Tags unique to generative-ai: agents, gcp, gemini, gemini-api; Also covers AI Agents, Data & Retrieval, Developer Tools; When you need to leverage the latest Gemini models, such as Gemini 3.8 Flash, for generative AI tasks on Google Cloud. ### When should I avoid amazon-sagemaker-examples? For non-AWS environments where cost and integration complexities could outweigh benefits If seeking open-source tools without ties to a single cloud provider ### When should I avoid generative-ai? If you are looking for a platform that supports a wide range of cloud providers beyond Google Cloud. When your project does not require the specific features of the Gemini models or the Gemini Enterprise Agent Platform. If your project does not benefit from Google-managed solutions like Agent Search and you prefer to build everything from scratch. When you do not need the integration capabilities with Google Cloud services such as Vertex AI and the Gemini Enterprise Agent Platform. ### Is amazon-sagemaker-examples or generative-ai more popular on GitHub? generative-ai has more GitHub stars (17,726 vs 10,990). Stars measure visibility, not whether either tool fits your constraints. ### Are amazon-sagemaker-examples and generative-ai open source? Yes - both are open-source projects on GitHub (amazon-sagemaker-examples: Apache-2.0, generative-ai: Apache-2.0). ### Where can I find alternatives to amazon-sagemaker-examples or generative-ai? GraphCanon lists graph-backed alternatives at [amazon-sagemaker-examples alternatives](/tools/aws-amazon-sagemaker-examples/alternatives) and [generative-ai alternatives](/tools/googlecloudplatform-generative-ai/alternatives) ([amazon-sagemaker-examples markdown twin](/tools/aws-amazon-sagemaker-examples/alternatives.md), [generative-ai markdown twin](/tools/googlecloudplatform-generative-ai/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/aws-amazon-sagemaker-examples-vs-googlecloudplatform-generative-ai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, amazon-sagemaker-examples or generative-ai? amazon-sagemaker-examples: Active. generative-ai: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for amazon-sagemaker-examples and generative-ai? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [amazon-sagemaker-examples trust report](/tools/aws-amazon-sagemaker-examples/trust); [generative-ai trust report](/tools/googlecloudplatform-generative-ai/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aws-amazon-sagemaker-examples`](/api/graphcanon/graph?tool=aws-amazon-sagemaker-examples) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_