Comparison
budgetml vs Awesome-LLMOps
Verdict
Pick budgetml if budgetML is a Python library that facilitates the deployment of machine learning inference services using Google Cloud Preemptible instances, offering significant cost reductions; pick Awesome-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Markdown twin · budgetml alternatives · Awesome-LLMOps alternatives
GraphCanon updated Sep 20, 2026
6views this month
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Trust & integrity
| Signal | budgetml | Awesome-LLMOps |
|---|---|---|
| Maintenance | Dormant (948d since push) As of Sep 18, 2026 · github_public_v1 | Slowing (121d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No published findings from this source as of 2026-09-18 As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- budgetml
- Deploy ML inference service on a budget
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- budgetml
- 1.3k
- Awesome-LLMOps
- 5.9k
Forks
- budgetml
- 65
- Awesome-LLMOps
- 1.1k
Open issues
- budgetml
- 4
- Awesome-LLMOps
- 317
Language
- budgetml
- Python
- Awesome-LLMOps
- Shell
Adopt for
- budgetml
- BudgetML is a Python library that facilitates the deployment of machine learning inference services using Google Cloud Preemptible instances, offering significant cost reductions.
- Awesome-LLMOps
- Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Persona
- budgetml
- -
- Awesome-LLMOps
- -
Runtime
- budgetml
- -
- Awesome-LLMOps
- -
License
- budgetml
- Apache-2.0
- Awesome-LLMOps
- CC0-1.0
Last pushed
- budgetml
- Feb 12, 2024
- Awesome-LLMOps
- May 21, 2026
Categories
- budgetml
- Inference & Serving
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- budgetml
- Dormant (18%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- budgetml
- 948d
- Awesome-LLMOps
- 121d
Open issues (now)
- budgetml
- 4
- Awesome-LLMOps
- 317
Stars delta
- budgetml
- +1 (30d)
- Awesome-LLMOps
- +26 (30d)
Open issues delta
- budgetml
- 0 (30d)
- Awesome-LLMOps
- +70 (30d)
OSV dependency advisories
- budgetml
- No published findings from this source as of 2026-09-18
- Awesome-LLMOps
- No lockfile (source not queried)
Full report
- budgetml
- Trust report
- Awesome-LLMOps
- Trust report
Choose budgetml if…
- budgetml is primarily Python; Awesome-LLMOps is Shell.
- License: budgetml is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- Pricing: BudgetML is free to use under the Apache-2.0 license, but users must cover the cost of Google Cloud Preemptible instances, which can be up to 80 percent cheaper than regular instances..
- Requirements: Min 4 GB RAM; Requires access to Google Cloud Preemptible instances..
- Tags unique to budgetml: api, data-science, deployment, fastapi.
- When you need to deploy machine learning inference services with a tight budget, as BudgetML can reduce costs by up to 80 percent using Google Cloud Preemptible instances.
When NOT to use budgetml
- If your application cannot tolerate the risk of instance preemption, as BudgetML relies on Google Cloud Preemptible instances that can be terminated with short notice.
- When you require high availability and reliability, as preemptible instances can be interrupted, which might not be suitable for mission-critical applications.
- If you are working in an environment where the use of external cloud services is restricted, BudgetML's reliance on Google Cloud might not be feasible.
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; budgetml is Python.
- License: Awesome-LLMOps is CC0-1.0, budgetml is Apache-2.0.
- Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops.
- Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, Model Training, Speech & Audio.
- - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
When NOT to use Awesome-LLMOps
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list.
- - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (ebhy/budgetml) · observed Sep 20, 2026
- GitHub forks (ebhy/budgetml) · observed Sep 20, 2026
- Last push (ebhy/budgetml) · observed Feb 12, 2024
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Sep 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Sep 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: budgetml 1.3k · Awesome-LLMOps 5.9k (synced Sep 20, 2026).
Common questions
- What is the difference between budgetml and Awesome-LLMOps?
- budgetml: Deploy ML inference service on a budget. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories.
- When should I choose budgetml over Awesome-LLMOps?
- Choose budgetml over Awesome-LLMOps when budgetml is primarily Python; Awesome-LLMOps is Shell; License: budgetml is Apache-2.0, Awesome-LLMOps is CC0-1.0; Pricing: BudgetML is free to use under the Apache-2.0 license, but users must cover the cost of Google Cloud Preemptible instances, which can be up to 80 percent cheaper than regular instances.; Requirements: Min 4 GB RAM; Requires access to Google Cloud Preemptible instances.; Tags unique to budgetml: api, data-science, deployment, fastapi; When you need to deploy machine learning inference services with a tight budget, as BudgetML can reduce costs by up to 80 percent using Google Cloud Preemptible instances.
- When should I choose Awesome-LLMOps over budgetml?
- Choose Awesome-LLMOps over budgetml when Awesome-LLMOps is primarily Shell; budgetml is Python; License: Awesome-LLMOps is CC0-1.0, budgetml is Apache-2.0; Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops; Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, Model Training, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- When should I avoid budgetml?
- If your application cannot tolerate the risk of instance preemption, as BudgetML relies on Google Cloud Preemptible instances that can be terminated with short notice. When you require high availability and reliability, as preemptible instances can be interrupted, which might not be suitable for mission-critical applications. If you are working in an environment where the use of external cloud services is restricted, BudgetML's reliance on Google Cloud might not be feasible.
- When should I avoid Awesome-LLMOps?
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
- Is budgetml or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,941 vs 1,344). Stars measure visibility, not whether either tool fits your constraints.
- Are budgetml and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (budgetml: Apache-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to budgetml or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at budgetml alternatives and Awesome-LLMOps alternatives (budgetml markdown twin, Awesome-LLMOps markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, budgetml or Awesome-LLMOps?
- budgetml: Dormant. Awesome-LLMOps: Slowing. 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 budgetml and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: budgetml trust report; Awesome-LLMOps trust report.