--- title: "dstack vs pytorch-lightning" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dstackai-dstack-vs-lightning-ai-pytorch-lightning" tools: ["dstackai-dstack", "lightning-ai-pytorch-lightning"] --- # dstack vs pytorch-lightning *GraphCanon updated Aug 24, 2026* ## Verdict Pick dstack if vendor-agnostic AI workload orchestration tool supports GPU providers like NVIDIA and AMD across cloud, Kubernetes, and bare metal; pick pytorch-lightning if pyTorch Lightning scales PyTorch models across GPUs with minimal code changes. [dstack](https://dstack.ai/docs) reports 2.2k GitHub stars, 250 forks, and 66 open issues, last pushed Aug 23, 2026. [pytorch-lightning](https://lightning.ai/pytorch-lightning/?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) has 31k stars, 3.8k forks, and 1.1k open issues, last pushed Aug 3, 2026. Figures are from public GitHub metadata via [dstack's repository](https://github.com/dstackai/dstack) and [pytorch-lightning's repository](https://github.com/Lightning-AI/pytorch-lightning). | | [dstack](/tools/dstackai-dstack.md) | [pytorch-lightning](/tools/lightning-ai-pytorch-lightning.md) | | --- | --- | --- | | Tagline | Vendor-agnostic orchestration for AI workloads | Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes. | | Stars | 2,219 | 31,267 | | Forks | 250 | 3,768 | | Open issues | 66 | 1,060 | | Language | Python | Python | | Adopt for | Vendor-agnostic AI workload orchestration tool supports GPU providers like NVIDIA and AMD across cloud, Kubernetes, and bare metal. | PyTorch Lightning scales PyTorch models across GPUs with minimal code changes. | | Persona | - | - | | Runtime | - | - | | License | MPL-2.0 | Apache-2.0 | | Categories | AI Agents, Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dstack](/tools/dstackai-dstack.md) | [pytorch-lightning](/tools/lightning-ai-pytorch-lightning.md) | | --- | --- | --- | | Open issues (now) | 66 | 1.1k | | Stars delta | +27 (30d) | Unknown | | Open issues delta | +5 (30d) | Unknown | | Full report | [trust report](/tools/dstackai-dstack/trust.md) | [trust report](/tools/lightning-ai-pytorch-lightning/trust.md) | ## Decision facts: dstack - **Adopt for:** Vendor-agnostic AI workload orchestration tool supports GPU providers like NVIDIA and AMD across cloud, Kubernetes, and bare metal. ## Decision facts: pytorch-lightning - **Adopt for:** PyTorch Lightning scales PyTorch models across GPUs with minimal code changes. ## Choose when ### Choose dstack if… - License: dstack is MPL-2.0, pytorch-lightning is Apache-2.0. - 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 ### Choose pytorch-lightning if… - License: pytorch-lightning is Apache-2.0, dstack is MPL-2.0. - Tags unique to pytorch-lightning: ai, artificial-intelligence, data-science, deep-learning. - Scalable ML model training with consistent API across single to multiple GPUs ## 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) ## When NOT to use pytorch-lightning - For lightweight models requiring minimal configuration or manual control over model distribution - Projects that target environments without access to multi-GPU setups and do not require scalability features ## Common questions ### What is the difference between dstack and pytorch-lightning? dstack: Vendor-agnostic orchestration for AI workloads. pytorch-lightning: Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.. See the comparison table for live GitHub stats and shared categories. ### When should I choose dstack over pytorch-lightning? Choose dstack over pytorch-lightning when License: dstack is MPL-2.0, pytorch-lightning is Apache-2.0; 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 pytorch-lightning over dstack? Choose pytorch-lightning over dstack when License: pytorch-lightning is Apache-2.0, dstack is MPL-2.0; Tags unique to pytorch-lightning: ai, artificial-intelligence, data-science, deep-learning; Scalable ML model training with consistent API across single to multiple GPUs. ### 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 pytorch-lightning? For lightweight models requiring minimal configuration or manual control over model distribution Projects that target environments without access to multi-GPU setups and do not require scalability features ### Is dstack or pytorch-lightning more popular on GitHub? pytorch-lightning has more GitHub stars (31,267 vs 2,219). Stars measure visibility, not whether either tool fits your constraints. ### Are dstack and pytorch-lightning open source? Yes - both are open-source projects on GitHub (dstack: MPL-2.0, pytorch-lightning: Apache-2.0). ### Where can I find alternatives to dstack or pytorch-lightning? GraphCanon lists graph-backed alternatives at [dstack alternatives](/tools/dstackai-dstack/alternatives) and [pytorch-lightning alternatives](/tools/lightning-ai-pytorch-lightning/alternatives) ([dstack markdown twin](/tools/dstackai-dstack/alternatives.md), [pytorch-lightning markdown twin](/tools/lightning-ai-pytorch-lightning/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/dstackai-dstack-vs-lightning-ai-pytorch-lightning.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, dstack or pytorch-lightning? dstack: Very active. pytorch-lightning: Very active. 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 pytorch-lightning? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [dstack trust report](/tools/dstackai-dstack/trust); [pytorch-lightning trust report](/tools/lightning-ai-pytorch-lightning/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dstackai-dstack`](/api/graphcanon/graph?tool=dstackai-dstack) - 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/_