Comparison
dstack vs Awesome-LLMOps
Verdict
Pick dstack if dstack is a vendor-agnostic orchestration tool for AI workloads, including training, inference, and agentic tasks, across various hardware and environments; 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 Sep 20, 2026
15views this month
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Trust & integrity
| Signal | dstack | Awesome-LLMOps |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 18, 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 18, 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 Sep 18, 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
- dstack
- Vendor-agnostic orchestration for training, inference and agentic workloads
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- dstack
- 2.3k
- Awesome-LLMOps
- 5.9k
Forks
- dstack
- 262
- Awesome-LLMOps
- 1.1k
Open issues
- dstack
- 64
- Awesome-LLMOps
- 317
Language
- dstack
- Python
- Awesome-LLMOps
- Shell
Adopt for
- dstack
- dstack is a vendor-agnostic orchestration tool for AI workloads, including training, inference, and agentic tasks, across various hardware and environments.
- 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
- dstack is licensed under the Mozilla Public License 2.0, which is an open-source license.
- Awesome-LLMOps
- CC0-1.0
Last pushed
- dstack
- Sep 17, 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
- Slowing (36%)
Days since push
- dstack
- 0d
- Awesome-LLMOps
- 121d
Open issues (now)
- dstack
- 64
- Awesome-LLMOps
- 317
Stars delta
- dstack
- +61 (30d)
- Awesome-LLMOps
- +26 (30d)
Open issues delta
- dstack
- +3 (30d)
- Awesome-LLMOps
- +70 (30d)
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.
- Pricing: The core functionality of dstack is free and open-source under the Mozilla Public License 2.0. Additional services or support may be available for a fee..
- Requirements: Requires Docker; Requires Git and OpenSSH for CLI installation and configuration..
- Tags unique to dstack: agent-skills, agentic-orchestration, amd, cloud.
- Also covers AI Agents.
- When you need orchestration for AI workloads that can run on a variety of hardware like NVIDIA, AMD, TPU, and Tenstorrent, and across different environments such as clouds, Kubernetes, and bare metal.
When NOT to use dstack
- If you are looking for a tool that is tightly integrated with a specific cloud provider or hardware vendor, as dstack is designed to be vendor-agnostic.
- When your project strictly requires proprietary licensing, as dstack is licensed under the Mozilla Public License 2.0, which is open source.
- If your team does not have the capability to install and configure the dstack CLI, which requires Git and OpenSSH, and involves setting up a server and configuring it with a user token and project URL
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 Sep 20, 2026
- GitHub forks (dstackai/dstack) · observed Sep 20, 2026
- Last push (dstackai/dstack) · observed Sep 17, 2026
- License file (MPL-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 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: dstack 2.3k · Awesome-LLMOps 5.9k (synced Sep 20, 2026).
Common questions
- What is the difference between dstack and Awesome-LLMOps?
- dstack: Vendor-agnostic orchestration for training, inference and agentic 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; Pricing: The core functionality of dstack is free and open-source under the Mozilla Public License 2.0. Additional services or support may be available for a fee.; Requirements: Requires Docker; Requires Git and OpenSSH for CLI installation and configuration.; Tags unique to dstack: agent-skills, agentic-orchestration, amd, cloud; Also covers AI Agents; When you need orchestration for AI workloads that can run on a variety of hardware like NVIDIA, AMD, TPU, and Tenstorrent, and across different environments such as clouds, Kubernetes, and bare metal.
- 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?
- If you are looking for a tool that is tightly integrated with a specific cloud provider or hardware vendor, as dstack is designed to be vendor-agnostic. When your project strictly requires proprietary licensing, as dstack is licensed under the Mozilla Public License 2.0, which is open source. If your team does not have the capability to install and configure the dstack CLI, which requires Git and OpenSSH, and involves setting up a server and configuring it with a user token and project URL
- 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 (5,941 vs 2,253). 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: 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 dstack and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: dstack trust report; Awesome-LLMOps trust report.