Comparison
dagster vs Awesome-LLMOps
Verdict
Pick dagster if dagster is an open-source orchestration platform developed in Python for managing and observing data pipelines and workflows; 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 · dagster alternatives · Awesome-LLMOps alternatives
GraphCanon updated Sep 20, 2026
15views this month
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Trust & integrity
| Signal | dagster | Awesome-LLMOps |
|---|---|---|
| Maintenance | Very active (2d since push) As of Sep 14, 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 14, 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
- dagster
- An orchestration platform for data assets
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- dagster
- 16k
- Awesome-LLMOps
- 5.9k
Forks
- dagster
- 2.3k
- Awesome-LLMOps
- 1.1k
Open issues
- dagster
- 2.6k
- Awesome-LLMOps
- 317
Language
- dagster
- Python
- Awesome-LLMOps
- Shell
Adopt for
- dagster
- Dagster is an open-source orchestration platform developed in Python for managing and observing data pipelines and workflows.
- 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
- dagster
- -
- Awesome-LLMOps
- -
Runtime
- dagster
- -
- Awesome-LLMOps
- -
License
- dagster
- Apache-2.0
- Awesome-LLMOps
- CC0-1.0
Last pushed
- dagster
- Sep 11, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- dagster
- Data & Retrieval, Evaluation & Observability
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- dagster
- Very active (96%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- dagster
- 2d
- Awesome-LLMOps
- 121d
Open issues (now)
- dagster
- 2.6k
- Awesome-LLMOps
- 317
Stars delta
- dagster
- +195 (30d)
- Awesome-LLMOps
- +26 (30d)
Open issues delta
- dagster
- -9 (30d)
- Awesome-LLMOps
- +70 (30d)
Full report
- dagster
- Trust report
- Awesome-LLMOps
- Trust report
Choose dagster if…
- dagster is primarily Python; Awesome-LLMOps is Shell.
- License: dagster is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- Tags unique to dagster: data-engineering, data-orchestrator, etl, workflow.
- When your project requires an Apache-2.0 licensed tool allowing broader reuse and modification of code.
When NOT to use dagster
- If you are restricted to proprietary or non-open-source licenses, as Dagster's Apache-2.0 might not align with compliance requirements.
- In environments where Python is not a preferred language, considering Dagster requires good knowledge of the Python ecosystem.
- For teams that do not require or benefit from extensive documentation and hands-on tutorials for onboarding.
- If specific features or integrations crucial to your workflow are found lacking in comparison to competitors.
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; dagster is Python.
- License: Awesome-LLMOps is CC0-1.0, dagster is Apache-2.0.
- Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops.
- Also covers Computer Vision, 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (dagster-io/dagster) · observed Sep 20, 2026
- GitHub forks (dagster-io/dagster) · observed Sep 20, 2026
- Last push (dagster-io/dagster) · observed Sep 11, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 16, 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: dagster 16k · Awesome-LLMOps 5.9k (synced Sep 20, 2026).
Common questions
- What is the difference between dagster and Awesome-LLMOps?
- dagster: An orchestration platform for data assets. 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 dagster over Awesome-LLMOps?
- Choose dagster over Awesome-LLMOps when dagster is primarily Python; Awesome-LLMOps is Shell; License: dagster is Apache-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to dagster: data-engineering, data-orchestrator, etl, workflow; When your project requires an Apache-2.0 licensed tool allowing broader reuse and modification of code.
- When should I choose Awesome-LLMOps over dagster?
- Choose Awesome-LLMOps over dagster when Awesome-LLMOps is primarily Shell; dagster is Python; License: Awesome-LLMOps is CC0-1.0, dagster is Apache-2.0; Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops; Also covers Computer Vision, 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 avoid dagster?
- If you are restricted to proprietary or non-open-source licenses, as Dagster's Apache-2.0 might not align with compliance requirements. In environments where Python is not a preferred language, considering Dagster requires good knowledge of the Python ecosystem. For teams that do not require or benefit from extensive documentation and hands-on tutorials for onboarding. If specific features or integrations crucial to your workflow are found lacking in comparison to competitors.
- 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 dagster or Awesome-LLMOps more popular on GitHub?
- dagster has more GitHub stars (16,144 vs 5,941). Stars measure visibility, not whether either tool fits your constraints.
- Are dagster and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (dagster: Apache-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to dagster or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at dagster alternatives and Awesome-LLMOps alternatives (dagster 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, dagster or Awesome-LLMOps?
- dagster: Very 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 dagster and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: dagster trust report; Awesome-LLMOps trust report.