{"data":{"slug":"ebhy-budgetml","name":"budgetml","tagline":"Deploys ML inference service economically","github_url":"https://github.com/ebhy/budgetml","owner":"ebhy","repo":"budgetml","owner_avatar_url":"https://avatars.githubusercontent.com/u/76654256?v=4","primary_language":"Python","stars":1343,"forks":65,"topics":["api","data-science","deployment","fastapi","inference","machine-learning","mlops"],"archived":false,"github_pushed_at":"2024-02-12T17:29:24+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/ebhy-budgetml","markdown_url":"https://www.graphcanon.com/tools/ebhy-budgetml.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/ebhy-budgetml","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=ebhy-budgetml","description":"Deploy a ML inference service on a budget in less than 10 lines of code.","homepage_url":null,"license":"Apache-2.0","open_issues":4,"watchers":23,"ai_summary":"BudgetML provides a way to deploy machine learning models for inference using Google Cloud Preemptible instances to cut costs.","readme_excerpt":"## Cost comparison\nBudgetML uses Google Cloud Preemptible instances under-the-hood to reduce costs by 80%. This can potentially mean hundreds of dollars \nworth of savings. Here is a screenshot of the `e2-highmem` GCP series, which is regular family of instances to be using for \nmemory intense tasks like ML model inference functions. See the following price comparison (as of Jan 31, 2021 [[source](https://cloud.google.com/compute/vm-instance-pricing)])\n\n\n\nEven with the lowest machine_type, there is a **$46/month** savings, and with the highest configuration this is **$370/month** savings!\n\n---\n\n## Installation\nBudgetML is available for easy installation into your environment via PyPI:\n```bash\npip install budgetml\n```\nAlternatively, if you’re feeling brave, feel free to install the bleeding edge:\n\n**NOTE:** Do so at your own risk; no guarantees given!\n```bash\npip install git+https://github.com/ebhy/budgetml.git@main --upgrade\n```","github_created_at":"2020-12-27T14:24:57+00:00","created_at":"2026-07-11T23:16:11.005589+00:00","updated_at":"2026-08-02T12:00:20.744941+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"}],"tags":[{"slug":"api","name":"api"},{"slug":"data-science","name":"data-science"},{"slug":"deployment","name":"deployment"},{"slug":"fastapi","name":"fastapi"},{"slug":"inference","name":"inference"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"mlops","name":"mlops"}],"trust":{"provenance":{"is_fork":false,"github_id":324778385,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-02T12:00:19.958Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":901,"last_release_at":"2021-01-31T20:26:15Z"},"security_summary":{"status":"ok","scanner":"osv@v1","low_count":0,"high_count":0,"last_scan_at":"2026-07-11T23:16:13.437Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-02T12:00:20.417Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-08-02T12:00:20.417Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-02T12:00:20.417Z"}},"decision_facts":{"hosting":null,"pricing":{"model":"freemium","summary":"Free to use, but users will incur costs based on their usage of Google Cloud Preemptible instances."},"requirements":{"notes":["Requires a working Python environment and access to Google Cloud services to deploy on Preemptible VMs","The library is available via PyPI or can be installed directly from GitHub for the latest features, at users' own risk"]},"constraints":{"pricing_model":"freemium"},"when_to_use":["When you are looking to reduce costs significantly and have flexibility in your deployment schedule, since Preemptible instances can be interrupted","Ideal for non-mission-critical tasks where occasional interruptions do not severely impact operations"],"when_not_to_use":["If you need absolute certainty that your ML service will not be interrupted at any point during operation","Not suitable for continuous and uninterrupted services, as Google Cloud Preemptible instances can be terminated with short notice"],"source":"enrich:decision_facts","observed_at":"2026-07-17T13:24:23.686Z"},"constraint_facets":{"pricing_model":"freemium"},"decision_summary":[{"label":"Pricing","value":"freemium - Free to use, but users will incur costs based on their usage of Google Cloud Preemptible instances."},{"label":"Requirements","value":"Requires a working Python environment and access to Google Cloud services to deploy on Preemptible VMs; The library is available via PyPI or can be installed directly from GitHub for the latest features, at users' own risk"},{"label":"Adopt for","value":"BudgetML is a Python library that leverages Google Cloud Preemptible instances to decrease the cost of deploying machine learning models for inference."},{"label":"License detail","value":"The tool is distributed under the Apache-2.0 license, allowing for free use in both open source and commercial projects."}]}}