Comparison
dstack vs Awesome-LLMOps
Verdict
Pick dstack if vendor-agnostic AI workload orchestration tool supports GPU providers like NVIDIA and AMD across cloud, Kubernetes, and bare metal; 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 · dstack alternatives · Awesome-LLMOps alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | dstack | Awesome-LLMOps |
|---|---|---|
| Maintenance | Very active (0d since push) As of 3w · github_public_v1 | Steady (60d since push) As of 1mo · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 1mo · 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
- dstack
- Vendor-agnostic orchestration for AI workloads
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- dstack
- 2.2k
- Awesome-LLMOps
- 5.9k
Forks
- dstack
- 240
- Awesome-LLMOps
- 924
Open issues
- dstack
- 61
- Awesome-LLMOps
- 181
Language
- dstack
- Python
- Awesome-LLMOps
- Shell
Adopt for
- dstack
- Vendor-agnostic AI workload orchestration tool supports GPU providers like NVIDIA and AMD across cloud, Kubernetes, and bare metal.
- 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
- dstack
- -
- Awesome-LLMOps
- -
Runtime
- dstack
- -
- Awesome-LLMOps
- -
License
- dstack
- MPL-2.0
- Awesome-LLMOps
- CC0-1.0
Last pushed
- dstack
- Jul 24, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- dstack
- AI Agents, Inference & Serving, Model Training
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- dstack
- Very active (96%)
- Awesome-LLMOps
- Steady (60%)
Days since push
- dstack
- 0d
- Awesome-LLMOps
- 60d
Open issues (now)
- dstack
- 61
- Awesome-LLMOps
- 181
Full report
- dstack
- Trust report
- Awesome-LLMOps
- Trust report
Choose dstack if…
- dstack is primarily Python; Awesome-LLMOps is Shell.
- License: dstack is MPL-2.0, Awesome-LLMOps is CC0-1.0.
- Tags unique to dstack: agent-skills, agentic-orchestration, amd, cloud.
- Also covers AI Agents.
- If your project requires support for multiple hardware vendors such as NVIDIA, AMD, TPU, or Tenstorrent
When NOT to use dstack
- When sticking to single-vendor solutions where tightly integrated proprietary tools are preferred
- If the project strictly avoids open-source components with Mozilla Public License (MPL-2.0)
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; dstack is Python.
- License: Awesome-LLMOps is CC0-1.0, dstack is MPL-2.0.
- Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops.
- 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
dstack trust report →Awesome-LLMOps trust report →AI Agents category →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 (dstackai/dstack) · observed Jul 24, 2026
- GitHub forks (dstackai/dstack) · observed Jul 24, 2026
- Last push (dstackai/dstack) · observed Jul 24, 2026
- License file (MPL-2.0) · observed Jul 24, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Jul 21, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Jul 21, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Jul 21, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: dstack 2.2k · Awesome-LLMOps 5.9k (synced Jul 24, 2026).
Common questions
- What is the difference between dstack and Awesome-LLMOps?
- dstack: Vendor-agnostic orchestration for AI workloads. 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 dstack over Awesome-LLMOps?
- Choose dstack over Awesome-LLMOps when dstack is primarily Python; Awesome-LLMOps is Shell; License: dstack is MPL-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to dstack: agent-skills, agentic-orchestration, amd, cloud; Also covers AI Agents; If your project requires support for multiple hardware vendors such as NVIDIA, AMD, TPU, or Tenstorrent.
- When should I choose Awesome-LLMOps over dstack?
- Choose Awesome-LLMOps over dstack when Awesome-LLMOps is primarily Shell; dstack is Python; License: Awesome-LLMOps is CC0-1.0, dstack is MPL-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; 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 dstack?
- When sticking to single-vendor solutions where tightly integrated proprietary tools are preferred If the project strictly avoids open-source components with Mozilla Public License (MPL-2.0)
- 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 dstack or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,887 vs 2,192). Stars measure visibility, not whether either tool fits your constraints.
- Are dstack and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (dstack: MPL-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to dstack or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at dstack alternatives and Awesome-LLMOps alternatives (dstack 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, dstack or Awesome-LLMOps?
- dstack: Very active. Awesome-LLMOps: Steady. 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 dstack and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: dstack trust report; Awesome-LLMOps trust report.