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If you don't know where to start, [Metaflow sandbox](https://outerbounds.com/sandbox) will have you running and exploring in seconds.\n\n---\n\n### Deploying infrastructure for Metaflow in your cloud\n<img src=\"./docs/multicloud.png\" width=\"800px\">\n\n\nWhile you can get started with Metaflow easily on your laptop, the main benefits of Metaflow lie in its ability to [scale out to external compute clusters](https://docs.metaflow.org/scaling/remote-tasks/introduction)\nand to [deploy to production-grade workflow orchestrators](https://docs.metaflow.org/production/introduction). To benefit from these features, follow this [guide](https://outerbounds.com/engineering/welcome/) to\nconfigure Metaflow and the infrastructure behind it appropriately.","github_created_at":"2019-09-17T17:48:25+00:00","created_at":"2026-07-07T17:41:45.017386+00:00","updated_at":"2026-08-20T06:01:28.114553+00:00","categories":[{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"agents","name":"agents"},{"slug":"ai","name":"ai"},{"slug":"aws","name":"aws"},{"slug":"azure","name":"azure"},{"slug":"cost-optimization","name":"cost-optimization"},{"slug":"datascience","name":"datascience"},{"slug":"distributed-training","name":"distributed-training"},{"slug":"gcp","name":"gcp"}],"trust":{"provenance":{"is_fork":false,"github_id":209120637,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-20T06:01:27.163Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":9,"days_since_push":1,"last_release_at":"2026-08-18T00:34:39Z","stars_delta_30d":38,"open_issues_delta_30d":9},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:19:27.954Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-20T06:01:27.797Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-08-20T06:01:27.797Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-20T06:01:27.797Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["- Your project requires scalable solutions that can extend to external compute clusters to handle complex ML tasks efficiently.","- You need robust infrastructure management tools like cost optimization features specific to AI/ML workflows.","- You aim for smooth deployment into production using orchestration services, making use of its cloud-friendly setup configurations on AWS, Azure, and GCP."],"when_not_to_use":["- If your team prefers working with a low-level infrastructure setup without integrated scaling and cost optimization tools.","- When prioritizing lightweight frameworks that don't require external compute clusters or sophisticated orchestration services for deployment."],"source":"enrich:decision_facts","observed_at":"2026-07-11T15:59:02.781Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Metaflow is a comprehensive Python framework for building, managing, and deploying AI/ML systems that stands out with its support for distributed training, cost optimization, and seamless integration across various cloud"}]}}