{"data":{"slug":"runllm-aqueduct","name":"aqueduct","tagline":"Orchestrate LLM and ML workloads on any cloud infrastructure using Go.","github_url":"https://github.com/RunLLM/aqueduct","owner":"RunLLM","repo":"aqueduct","owner_avatar_url":"https://avatars.githubusercontent.com/u/74574122?v=4","primary_language":"Go","stars":517,"forks":20,"topics":["ai","data","data-science","kubernetes","llm","llms","machine-learning","ml","ml-infrastructure","ml-monitoring","mlops","orchestration","python","python3"],"archived":false,"github_pushed_at":"2023-06-07T19:24:59+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/runllm-aqueduct","markdown_url":"https://www.graphcanon.com/tools/runllm-aqueduct.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/runllm-aqueduct","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=runllm-aqueduct","description":"Aqueduct is no longer being maintained. Aqueduct allows you to run LLM and ML workloads on any cloud infrastructure.","homepage_url":"https://aqueducthq.com","license":"Apache-2.0","open_issues":11,"watchers":7,"ai_summary":"Aqueduct is a deprecated tool for orchestrating machine learning and large language model workloads across diverse cloud infrastructures with support for Kubernetes orchestration, resource allocation like GPUs, and monitoring.","readme_excerpt":"# Or write a custom op on your favorite infrastructure!\n@op(\n  engine='kubernetes',\n  # Get a GPU.\n  resources={'gpu_resource_name': 'nvidia.com/gpu'}\n)\ndef train(featurized_logs):\n  return model.train(features) # Train your model.\n\ntrain(features)\n```\n\nOnce you publish this workflow to Aqueduct, you can see it on the UI: \n\n\n\nTo see how to build your first workflow, check out our **[quickstart guide! →](https://docs.aqueducthq.com/quickstart-guide)**","github_created_at":"2022-05-27T03:07:09+00:00","created_at":"2026-07-11T23:29:55.951913+00:00","updated_at":"2026-08-03T18:01:04.108811+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":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"},{"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":"ai","name":"ai"},{"slug":"data","name":"data"},{"slug":"data-science","name":"data-science"},{"slug":"kubernetes","name":"kubernetes"},{"slug":"llm","name":"llm"},{"slug":"ml","name":"ml"},{"slug":"ml-infrastructure","name":"ml-infrastructure"},{"slug":"ml-monitoring","name":"ml-monitoring"}],"trust":{"provenance":{"is_fork":false,"github_id":496844646,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-03T18:01:03.315Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":1152,"last_release_at":"2023-06-07T19:29:11Z"},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T23:29:59.536Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-03T18:01:03.806Z"},"languages":{"value":["go"],"source":"github.language","observed_at":"2026-08-03T18:01:03.806Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-03T18:01:03.806Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you need to deploy legacy workflows that depend on Aqueduct's specific implementation of custom ops for resource allocation and training.","For scenarios where the team already has expertise in Go language and wishes to leverage available codebases integrating Aqueduct."],"when_not_to_use":["Avoid if active project maintenance or community support is required as Aqueduct is no longer maintained.","Skip this tool for new projects focusing on state-of-the-art ML orchestration, opting instead for actively supported alternatives."],"source":"enrich:decision_facts","observed_at":"2026-07-17T00:17:20.674Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Aqueduct is a deprecated Go-based tool for orchestrating LLM and ML workloads across various cloud infrastructures with Kubernetes support."}]}}