--- title: "dstack vs blast" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dstackai-dstack-vs-stanford-mast-blast" tools: ["dstackai-dstack", "stanford-mast-blast"] --- # dstack vs blast *GraphCanon updated Aug 25, 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 blast if blast provides open-source VMs-as-a-service for deploying AI agents and facilitating large-language-model inference, with support for Python. [dstack](https://dstack.ai/docs) reports 2.2k GitHub stars, 250 forks, and 66 open issues, last pushed Aug 23, 2026. [blast](http://blastproject.org/) has 778 stars, 51 forks, and 6 open issues, last pushed May 29, 2026. Figures are from public GitHub metadata via [dstack's repository](https://github.com/dstackai/dstack) and [blast's repository](https://github.com/stanford-mast/blast). | | [dstack](/tools/dstackai-dstack.md) | [blast](/tools/stanford-mast-blast.md) | | --- | --- | --- | | Tagline | Vendor-agnostic orchestration for AI workloads | Open-source VMs-as-a-service | | Stars | 2,219 | 778 | | Forks | 250 | 51 | | Open issues | 66 | 6 | | Language | Python | Python | | Adopt for | Vendor-agnostic AI workload orchestration tool supports GPU providers like NVIDIA and AMD across cloud, Kubernetes, and bare metal. | Blast provides open-source VMs-as-a-service for deploying AI agents and facilitating large-language-model inference, with support for Python. | | Persona | - | - | | Runtime | - | - | | License | MPL-2.0 | MIT | | Categories | AI Agents, Inference & Serving, Model Training | AI Agents, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dstack](/tools/dstackai-dstack.md) | [blast](/tools/stanford-mast-blast.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 87d | | Open issues (now) | 66 | 6 | | Stars delta | +27 (30d) | +1 (30d) | | Open issues delta | +5 (30d) | 0 (30d) | | Full report | [trust report](/tools/dstackai-dstack/trust.md) | [trust report](/tools/stanford-mast-blast/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: blast - **Requirements:** Requires Docker; Ensure you have Docker installed to create and manage virtual machine instances effectively with Blast.; Python environment setup is necessary for leveraging all the features offered by this project. - **Adopt for:** Blast provides open-source VMs-as-a-service for deploying AI agents and facilitating large-language-model inference, with support for Python. ## Choose when ### Choose dstack if… - License: dstack is MPL-2.0, blast is MIT. - Tags unique to dstack: agent-skills, agentic-orchestration, amd, cloud. - Also covers Model Training. - If your project requires support for multiple hardware vendors such as NVIDIA, AMD, TPU, or Tenstorrent ### Choose blast if… - License: blast is MIT, dstack is MPL-2.0. - Requirements: Requires Docker; Ensure you have Docker installed to create and manage virtual machine instances effectively with Blast.; Python environment setup is necessary for leveraging all the features offered by this project.. - Tags unique to blast: ai-agents, browser-automation, llm-inference, python. - Use Blast if you need an open-source solution for virtual machines as a service specifically tailored to artificial intelligence agent deployment and large-language-model inference processes. ## 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 blast - Avoid Blast if your project requires proprietary or commercial-only solutions because it is an open-source tool governed by the MIT License. - Do not use Blast for applications where browser-automation support alone is needed as its primary focus is on deploying AI agents and not solely on automating browsers. ## Common questions ### What is the difference between dstack and blast? dstack: Vendor-agnostic orchestration for AI workloads. blast: Open-source VMs-as-a-service. See the comparison table for live GitHub stats and shared categories. ### When should I choose dstack over blast? Choose dstack over blast when License: dstack is MPL-2.0, blast is MIT; Tags unique to dstack: agent-skills, agentic-orchestration, amd, cloud; Also covers Model Training; If your project requires support for multiple hardware vendors such as NVIDIA, AMD, TPU, or Tenstorrent. ### When should I choose blast over dstack? Choose blast over dstack when License: blast is MIT, dstack is MPL-2.0; Requirements: Requires Docker; Ensure you have Docker installed to create and manage virtual machine instances effectively with Blast.; Python environment setup is necessary for leveraging all the features offered by this project.; Tags unique to blast: ai-agents, browser-automation, llm-inference, python; Use Blast if you need an open-source solution for virtual machines as a service specifically tailored to artificial intelligence agent deployment and large-language-model inference processes. ### 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 blast? Avoid Blast if your project requires proprietary or commercial-only solutions because it is an open-source tool governed by the MIT License. Do not use Blast for applications where browser-automation support alone is needed as its primary focus is on deploying AI agents and not solely on automating browsers. ### Is dstack or blast more popular on GitHub? dstack has more GitHub stars (2,219 vs 778). Stars measure visibility, not whether either tool fits your constraints. ### Are dstack and blast open source? Yes - both are open-source projects on GitHub (dstack: MPL-2.0, blast: MIT). ### Where can I find alternatives to dstack or blast? GraphCanon lists graph-backed alternatives at [dstack alternatives](/tools/dstackai-dstack/alternatives) and [blast alternatives](/tools/stanford-mast-blast/alternatives) ([dstack markdown twin](/tools/dstackai-dstack/alternatives.md), [blast markdown twin](/tools/stanford-mast-blast/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-stanford-mast-blast.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, dstack or blast? dstack: Very active. blast: 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 blast? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [dstack trust report](/tools/dstackai-dstack/trust); [blast trust report](/tools/stanford-mast-blast/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/_