--- title: "kvcached vs blast" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ovg-project-kvcached-vs-stanford-mast-blast" tools: ["ovg-project-kvcached", "stanford-mast-blast"] --- # kvcached vs blast *GraphCanon updated Aug 25, 2026* ## Verdict Pick kvcached if kvcached is designed for optimizing dynamic GPU sharing and multiplexing scenarios, beneficial for LLM inference and serving operations; 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. [kvcached](https://github.com/ovg-project/kvcached) reports 1.1k GitHub stars, 132 forks, and 99 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 [kvcached's repository](https://github.com/ovg-project/kvcached) and [blast's repository](https://github.com/stanford-mast/blast). | | [kvcached](/tools/ovg-project-kvcached.md) | [blast](/tools/stanford-mast-blast.md) | | --- | --- | --- | | Tagline | Virtualized Elastic KV Cache for Dynamic GPU Sharing and Beyond | Open-source VMs-as-a-service | | Stars | 1,142 | 778 | | Forks | 132 | 51 | | Open issues | 99 | 6 | | Language | Python | Python | | Adopt for | Kvcached is designed for optimizing dynamic GPU sharing and multiplexing scenarios, beneficial for LLM inference and serving operations. | 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 | Apache-2.0 | MIT | | Categories | Inference & Serving, LLM Frameworks | AI Agents, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [kvcached](/tools/ovg-project-kvcached.md) | [blast](/tools/stanford-mast-blast.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 1d | 87d | | Open issues (now) | 99 | 6 | | Stars delta | +27 (30d) | +1 (30d) | | Open issues delta | -5 (30d) | 0 (30d) | | Full report | [trust report](/tools/ovg-project-kvcached/trust.md) | [trust report](/tools/stanford-mast-blast/trust.md) | ## Shared compatibility - **Python**: [kvcached](/tools/ovg-project-kvcached.md) - Python runtime; [blast](/tools/stanford-mast-blast.md) - Python runtime ## Decision facts: kvcached - **Adopt for:** Kvcached is designed for optimizing dynamic GPU sharing and multiplexing scenarios, beneficial for LLM inference and serving operations. ## 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 kvcached if… - License: kvcached is Apache-2.0, blast is MIT. - Tags unique to kvcached: elastic-kvcache, gpu-sharing, kvcache. - Also covers LLM Frameworks. - If you are looking to optimize performance in environments that require dynamic allocation of GPUs among multiple processes or tasks. ### Choose blast if… - License: blast is MIT, kvcached is Apache-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, 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 kvcached - For applications where static, predefined resource allocations are sufficient and do not require dynamic adjustments to GPU usage. - In scenarios that prioritize simplicity over sophisticated resource management, as Kvcached may add unnecessary complexity with its advanced features for dynamic GPU sharing. ## 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 kvcached and blast? kvcached: Virtualized Elastic KV Cache for Dynamic GPU Sharing and Beyond. blast: Open-source VMs-as-a-service. See the comparison table for live GitHub stats and shared categories. ### When should I choose kvcached over blast? Choose kvcached over blast when License: kvcached is Apache-2.0, blast is MIT; Tags unique to kvcached: elastic-kvcache, gpu-sharing, kvcache; Also covers LLM Frameworks; If you are looking to optimize performance in environments that require dynamic allocation of GPUs among multiple processes or tasks. ### When should I choose blast over kvcached? Choose blast over kvcached when License: blast is MIT, kvcached is Apache-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, 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 kvcached? For applications where static, predefined resource allocations are sufficient and do not require dynamic adjustments to GPU usage. In scenarios that prioritize simplicity over sophisticated resource management, as Kvcached may add unnecessary complexity with its advanced features for dynamic GPU sharing. ### 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 kvcached or blast more popular on GitHub? kvcached has more GitHub stars (1,142 vs 778). Stars measure visibility, not whether either tool fits your constraints. ### Are kvcached and blast open source? Yes - both are open-source projects on GitHub (kvcached: Apache-2.0, blast: MIT). ### Where can I find alternatives to kvcached or blast? GraphCanon lists graph-backed alternatives at [kvcached alternatives](/tools/ovg-project-kvcached/alternatives) and [blast alternatives](/tools/stanford-mast-blast/alternatives) ([kvcached markdown twin](/tools/ovg-project-kvcached/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/ovg-project-kvcached-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, kvcached or blast? kvcached: 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 kvcached and blast? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [kvcached trust report](/tools/ovg-project-kvcached/trust); [blast trust report](/tools/stanford-mast-blast/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ovg-project-kvcached`](/api/graphcanon/graph?tool=ovg-project-kvcached) - 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/_