--- title: "dagster vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dagster-io-dagster-vs-tensorchord-awesome-llmops" tools: ["dagster-io-dagster", "tensorchord-awesome-llmops"] --- # dagster vs Awesome-LLMOps *GraphCanon updated Sep 20, 2026* ## 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. [dagster](https://dagster.io) reports 16k GitHub stars, 2.3k forks, and 2.6k open issues, last pushed Sep 11, 2026. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) has 5.9k stars, 1.1k forks, and 317 open issues, last pushed May 21, 2026. Figures are from public GitHub metadata via [dagster's repository](https://github.com/dagster-io/dagster) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [dagster](/tools/dagster-io-dagster.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | An orchestration platform for data assets | An awesome & curated list of best LLMOps tools for developers | | Stars | 16,144 | 5,941 | | Forks | 2,290 | 1,058 | | Open issues | 2,587 | 317 | | Language | Python | Shell | | Adopt for | Dagster is an open-source orchestration platform developed in Python for managing and observing data pipelines and workflows. | 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 | - | - | | Runtime | - | - | | License | Apache-2.0 | CC0-1.0 | | Categories | Data & Retrieval, Evaluation & Observability | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dagster](/tools/dagster-io-dagster.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 2d | 121d | | Open issues (now) | 2.6k | 317 | | Stars delta | +195 (30d) | +26 (30d) | | Open issues delta | -9 (30d) | +70 (30d) | | Full report | [trust report](/tools/dagster-io-dagster/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: dagster - **Adopt for:** Dagster is an open-source orchestration platform developed in Python for managing and observing data pipelines and workflows. ## Decision facts: Awesome-LLMOps - **Adopt for:** 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. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/dagster-io-dagster/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([dagster markdown twin](/tools/dagster-io-dagster/alternatives.md), [Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/alternatives.md)), 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](/compare/dagster-io-dagster-vs-tensorchord-awesome-llmops.md) 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](/tools/dagster-io-dagster/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dagster-io-dagster`](/api/graphcanon/graph?tool=dagster-io-dagster) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_