Comparison
amazon-sagemaker-examples vs Awesome-LLMOps
Verdict
Pick amazon-sagemaker-examples if jupyter notebooks for illustrating machine learning workflows on Amazon SageMaker; 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 · amazon-sagemaker-examples alternatives · Awesome-LLMOps alternatives
GraphCanon updated Sep 20, 2026
11views this month
vs
Trust & integrity
| Signal | amazon-sagemaker-examples | Awesome-LLMOps |
|---|---|---|
| Maintenance | Active (10d since push) As of Sep 20, 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 20, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 15, 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
- amazon-sagemaker-examples
- Jupyter notebooks for building, training, and deploying ML models using Amazon SageMaker
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- amazon-sagemaker-examples
- 11k
- Awesome-LLMOps
- 5.9k
Forks
- amazon-sagemaker-examples
- 7.0k
- Awesome-LLMOps
- 1.1k
Open issues
- amazon-sagemaker-examples
- 854
- Awesome-LLMOps
- 317
Language
- amazon-sagemaker-examples
- Jupyter Notebook
- Awesome-LLMOps
- Shell
Adopt for
- amazon-sagemaker-examples
- Jupyter notebooks for illustrating machine learning workflows on Amazon SageMaker
- 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
- amazon-sagemaker-examples
- -
- Awesome-LLMOps
- -
Runtime
- amazon-sagemaker-examples
- -
- Awesome-LLMOps
- -
License
- amazon-sagemaker-examples
- Apache-2.0, allowing free use for any purpose with conditions on attribution and license preservation
- Awesome-LLMOps
- CC0-1.0
Last pushed
- amazon-sagemaker-examples
- Sep 9, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- amazon-sagemaker-examples
- Inference & Serving, Model Training
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- amazon-sagemaker-examples
- Active (82%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- amazon-sagemaker-examples
- 10d
- Awesome-LLMOps
- 121d
Open issues (now)
- amazon-sagemaker-examples
- 854
- Awesome-LLMOps
- 317
Stars delta
- amazon-sagemaker-examples
- +6 (30d)
- Awesome-LLMOps
- +26 (30d)
Open issues delta
- amazon-sagemaker-examples
- +5 (30d)
- Awesome-LLMOps
- +70 (30d)
Full report
- amazon-sagemaker-examples
- Trust report
- Awesome-LLMOps
- Trust report
Choose amazon-sagemaker-examples if…
- amazon-sagemaker-examples is primarily Jupyter Notebook; Awesome-LLMOps is Shell.
- License: amazon-sagemaker-examples is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- Tags unique to amazon-sagemaker-examples: aws, data-science, deep-learning, inference.
- When you need examples specific to building models with Amazon SageMaker
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
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; amazon-sagemaker-examples is Jupyter Notebook.
- License: Awesome-LLMOps is CC0-1.0, amazon-sagemaker-examples 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, 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
amazon-sagemaker-examples trust report →Awesome-LLMOps trust report →Inference & Serving category →Model Training category →Computer Vision category →Data & Retrieval category →Evaluation & Observability category →LLM Frameworks category →Speech & Audio category →All comparisonsStack workflowsTrending tools
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (aws/amazon-sagemaker-examples) · observed Sep 20, 2026
- GitHub forks (aws/amazon-sagemaker-examples) · observed Sep 20, 2026
- Last push (aws/amazon-sagemaker-examples) · observed Sep 9, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 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: amazon-sagemaker-examples 11k · Awesome-LLMOps 5.9k (synced Sep 20, 2026).
Common questions
- What is the difference between amazon-sagemaker-examples and Awesome-LLMOps?
- amazon-sagemaker-examples: Jupyter notebooks for building, training, and deploying ML models using Amazon SageMaker. 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 amazon-sagemaker-examples over Awesome-LLMOps?
- Choose amazon-sagemaker-examples over Awesome-LLMOps when amazon-sagemaker-examples is primarily Jupyter Notebook; Awesome-LLMOps is Shell; License: amazon-sagemaker-examples is Apache-2.0, Awesome-LLMOps is CC0-1.0; 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 Awesome-LLMOps over amazon-sagemaker-examples?
- Choose Awesome-LLMOps over amazon-sagemaker-examples when Awesome-LLMOps is primarily Shell; amazon-sagemaker-examples is Jupyter Notebook; License: Awesome-LLMOps is CC0-1.0, amazon-sagemaker-examples 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, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- 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 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 amazon-sagemaker-examples or Awesome-LLMOps more popular on GitHub?
- amazon-sagemaker-examples has more GitHub stars (10,990 vs 5,941). Stars measure visibility, not whether either tool fits your constraints.
- Are amazon-sagemaker-examples and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (amazon-sagemaker-examples: Apache-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to amazon-sagemaker-examples or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at amazon-sagemaker-examples alternatives and Awesome-LLMOps alternatives (amazon-sagemaker-examples 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, amazon-sagemaker-examples or Awesome-LLMOps?
- amazon-sagemaker-examples: Active. 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 amazon-sagemaker-examples and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: amazon-sagemaker-examples trust report; Awesome-LLMOps trust report.