Comparison
Awesome-LLMOps vs uptrain
Verdict
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; pick uptrain if upTrain, an open-source platform, evaluates and enhances Generative AI applications with preconfigured checks, root cause analysis, and actionable insights.
Markdown twin · Awesome-LLMOps alternatives · uptrain alternatives
GraphCanon updated 1d
vs
Trust & integrity
| Signal | Awesome-LLMOps | uptrain |
|---|---|---|
| Maintenance | Slowing (91d since push) As of 1d · github_public_v1 | Dormant (731d since push) As of 1d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1d · github_public_v1 | Not a fork · Organization account As of 1d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
- uptrain
- Unified platform for evaluating and improving Generative AI applications
Stars
- Awesome-LLMOps
- 5.9k
- uptrain
- 2.4k
Forks
- Awesome-LLMOps
- 993
- uptrain
- 204
Open issues
- Awesome-LLMOps
- 247
- uptrain
- 58
Language
- Awesome-LLMOps
- Shell
- uptrain
- Python
Adopt for
- 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.
- uptrain
- UpTrain, an open-source platform, evaluates and enhances Generative AI applications with preconfigured checks, root cause analysis, and actionable insights.
Persona
- Awesome-LLMOps
- -
- uptrain
- -
Runtime
- Awesome-LLMOps
- -
- uptrain
- -
License
- Awesome-LLMOps
- CC0-1.0
- uptrain
- The tool is available under the Apache-2.0 license, suitable for both free and commercial use with appropriate attribution.
Last pushed
- Awesome-LLMOps
- May 21, 2026
- uptrain
- Aug 18, 2024
Categories
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
- uptrain
- Evaluation & Observability
Trust and health
Maintenance
- Awesome-LLMOps
- Slowing (36%)
- uptrain
- Dormant (18%)
Days since push
- Awesome-LLMOps
- 91d
- uptrain
- 731d
Open issues (now)
- Awesome-LLMOps
- 247
- uptrain
- 58
Stars delta
- Awesome-LLMOps
- +28 (30d)
- uptrain
- +4 (30d)
Open issues delta
- Awesome-LLMOps
- +66 (30d)
- uptrain
- +3 (30d)
Full report
- Awesome-LLMOps
- Trust report
- uptrain
- Trust report
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; uptrain is Python.
- License: Awesome-LLMOps is CC0-1.0, uptrain is Apache-2.0.
- Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Data & Retrieval, Inference & Serving, 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.
Choose uptrain if…
- uptrain is primarily Python; Awesome-LLMOps is Shell.
- License: uptrain is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- UpTrain can be installed on-premises using pip or accessed through a managed version.
- Tags unique to uptrain: autoevaluation, evaluation, experimentation, hallucination-detection.
- uptrain ships Docker support for self-hosted deployment.
- - When you need to evaluate Generative AI applications across various use-cases including language models, code generation, and embeddings.
When NOT to use uptrain
- - When your application does not require extensive monitoring or do not need insights into improving Generative AI performance through root cause analysis.
- - If you prioritize a highly hands-off user experience without the capability to customize evaluation checks, consider using UpTrain's managed version instead of self-managing it.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Aug 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (uptrain-ai/uptrain) · observed Aug 20, 2026
- GitHub forks (uptrain-ai/uptrain) · observed Aug 20, 2026
- Last push (uptrain-ai/uptrain) · observed Aug 18, 2024
- License file (Apache-2.0) · observed Aug 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-LLMOps 5.9k · uptrain 2.4k (synced Aug 20, 2026).
Common questions
- What is the difference between Awesome-LLMOps and uptrain?
- Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. uptrain: Unified platform for evaluating and improving Generative AI applications. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-LLMOps over uptrain?
- Choose Awesome-LLMOps over uptrain when Awesome-LLMOps is primarily Shell; uptrain is Python; License: Awesome-LLMOps is CC0-1.0, uptrain is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Inference & Serving, 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 choose uptrain over Awesome-LLMOps?
- Choose uptrain over Awesome-LLMOps when uptrain is primarily Python; Awesome-LLMOps is Shell; License: uptrain is Apache-2.0, Awesome-LLMOps is CC0-1.0; UpTrain can be installed on-premises using pip or accessed through a managed version; Tags unique to uptrain: autoevaluation, evaluation, experimentation, hallucination-detection; uptrain ships Docker support for self-hosted deployment; - When you need to evaluate Generative AI applications across various use-cases including language models, code generation, and embeddings.
- 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.
- When should I avoid uptrain?
- - When your application does not require extensive monitoring or do not need insights into improving Generative AI performance through root cause analysis. - If you prioritize a highly hands-off user experience without the capability to customize evaluation checks, consider using UpTrain's managed version instead of self-managing it.
- Is Awesome-LLMOps or uptrain more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,915 vs 2,359). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-LLMOps and uptrain open source?
- Yes - both are open-source projects on GitHub (Awesome-LLMOps: CC0-1.0, uptrain: Apache-2.0).
- Where can I find alternatives to Awesome-LLMOps or uptrain?
- GraphCanon lists graph-backed alternatives at Awesome-LLMOps alternatives and uptrain alternatives (Awesome-LLMOps markdown twin, uptrain 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, Awesome-LLMOps or uptrain?
- Awesome-LLMOps: Slowing. uptrain: Dormant. 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 Awesome-LLMOps and uptrain?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-LLMOps trust report; uptrain trust report.