--- title: "beta9 vs blast" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/beam-cloud-beta9-vs-stanford-mast-blast" tools: ["beam-cloud-beta9", "stanford-mast-blast"] --- # beta9 vs blast *GraphCanon updated Aug 25, 2026* ## Verdict Pick beta9 if beta9 is an ultrafast serverless GPU inference platform with sandbox environments and background job capabilities. Noteworthy features include its focus on large language model inference and environment management; 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. [beta9](https://beam.cloud) reports 1.8k GitHub stars, 158 forks, and 21 open issues, last pushed Aug 19, 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 [beta9's repository](https://github.com/beam-cloud/beta9) and [blast's repository](https://github.com/stanford-mast/blast). | | [beta9](/tools/beam-cloud-beta9.md) | [blast](/tools/stanford-mast-blast.md) | | --- | --- | --- | | Tagline | Ultrafast serverless GPU inference, sandboxes, and background jobs | Open-source VMs-as-a-service | | Stars | 1,753 | 778 | | Forks | 158 | 51 | | Open issues | 21 | 6 | | Language | Go | Python | | Adopt for | beta9 is an ultrafast serverless GPU inference platform with sandbox environments and background job capabilities. Noteworthy features include its focus on large language model inference and environment management. | 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 | AGPL-3.0 | MIT | | Categories | Inference & Serving, LLM Frameworks | AI Agents, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [beta9](/tools/beam-cloud-beta9.md) | [blast](/tools/stanford-mast-blast.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 4d | 87d | | Open issues (now) | 21 | 6 | | Stars delta | +33 (30d) | +1 (30d) | | Open issues delta | +4 (30d) | 0 (30d) | | Full report | [trust report](/tools/beam-cloud-beta9/trust.md) | [trust report](/tools/stanford-mast-blast/trust.md) | ## Shared compatibility - **Python**: [beta9](/tools/beam-cloud-beta9.md) - Python runtime; [blast](/tools/stanford-mast-blast.md) - Python runtime ## Decision facts: beta9 - **Pricing:** unknown - The license type is AGPL-3.0 which may indicate an open-source community model with potential enterprise upgrades. - **Requirements:** Development in Go implies the system leverages specific idiomatic patterns and libraries within this language which might not be portable across others. - **Adopt for:** beta9 is an ultrafast serverless GPU inference platform with sandbox environments and background job capabilities. Noteworthy features include its focus on large language model inference and environment management. ## 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 beta9 if… - beta9 is primarily Go; blast is Python. - License: beta9 is AGPL-3.0, blast is MIT. - Pricing: The license type is AGPL-3.0 which may indicate an open-source community model with potential enterprise upgrades.. - Requirements: Development in Go implies the system leverages specific idiomatic patterns and libraries within this language which might not be portable across others.. - Tags unique to beta9: autoscaler, cloudrun, cuda, distributed-computing. - Also covers LLM Frameworks. - Use beta9 when you specifically need to deploy large language models for ultrafast inference tasks, benefiting from its dedicated support for LLMs. ### Choose blast if… - blast is primarily Python; beta9 is Go. - License: blast is MIT, beta9 is AGPL-3.0. - 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. - Also covers AI Agents. - 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 beta9 - Avoid using beta9 if you need more general-purpose developer tools that don't specialize in large language model inference and related tasks. - Do not use this platform if your project does not benefit from GPU acceleration or serverless computing for background jobs and sandboxes, as these are beta9's key strengths. ## 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 beta9 and blast? beta9: Ultrafast serverless GPU inference, sandboxes, and background jobs. blast: Open-source VMs-as-a-service. See the comparison table for live GitHub stats and shared categories. ### When should I choose beta9 over blast? Choose beta9 over blast when beta9 is primarily Go; blast is Python; License: beta9 is AGPL-3.0, blast is MIT; Pricing: The license type is AGPL-3.0 which may indicate an open-source community model with potential enterprise upgrades.; Requirements: Development in Go implies the system leverages specific idiomatic patterns and libraries within this language which might not be portable across others.; Tags unique to beta9: autoscaler, cloudrun, cuda, distributed-computing; Also covers LLM Frameworks; Use beta9 when you specifically need to deploy large language models for ultrafast inference tasks, benefiting from its dedicated support for LLMs. ### When should I choose blast over beta9? Choose blast over beta9 when blast is primarily Python; beta9 is Go; License: blast is MIT, beta9 is AGPL-3.0; 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; Also covers AI Agents; 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 beta9? Avoid using beta9 if you need more general-purpose developer tools that don't specialize in large language model inference and related tasks. Do not use this platform if your project does not benefit from GPU acceleration or serverless computing for background jobs and sandboxes, as these are beta9's key strengths. ### 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 beta9 or blast more popular on GitHub? beta9 has more GitHub stars (1,753 vs 778). Stars measure visibility, not whether either tool fits your constraints. ### Are beta9 and blast open source? Yes - both are open-source projects on GitHub (beta9: AGPL-3.0, blast: MIT). ### Where can I find alternatives to beta9 or blast? GraphCanon lists graph-backed alternatives at [beta9 alternatives](/tools/beam-cloud-beta9/alternatives) and [blast alternatives](/tools/stanford-mast-blast/alternatives) ([beta9 markdown twin](/tools/beam-cloud-beta9/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/beam-cloud-beta9-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, beta9 or blast? beta9: 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 beta9 and blast? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [beta9 trust report](/tools/beam-cloud-beta9/trust); [blast trust report](/tools/stanford-mast-blast/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=beam-cloud-beta9`](/api/graphcanon/graph?tool=beam-cloud-beta9) - 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/_