--- title: "REST vs llm-applications" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/fasterdecoding-rest-vs-ray-project-llm-applications" tools: ["fasterdecoding-rest", "ray-project-llm-applications"] --- # REST vs llm-applications *GraphCanon updated Sep 20, 2026* ## Verdict Pick REST if rEST is a retrieval-based speculative decoding method for enhancing the efficiency of large language model inference, implemented in C under the Apache-2.0 license; pick llm-applications if the llm-applications repository offers focused guidance on deploying RAG-based LLM apps in production environments with an emphasis on using Ray. [REST](https://github.com/FasterDecoding/REST) reports 220 GitHub stars, 17 forks, and 15 open issues, last pushed Mar 5, 2026. [llm-applications](https://github.com/ray-project/llm-applications) has 1.9k stars, 258 forks, and 15 open issues, last pushed Aug 15, 2026. Figures are from public GitHub metadata via [REST's repository](https://github.com/FasterDecoding/REST) and [llm-applications's repository](https://github.com/ray-project/llm-applications). | | [REST](/tools/fasterdecoding-rest.md) | [llm-applications](/tools/ray-project-llm-applications.md) | | --- | --- | --- | | Tagline | REST: Retrieval-Based Speculative Decoding | Comprehensive guide to building RAG-based LLM applications for production | | Stars | 220 | 1,857 | | Forks | 17 | 258 | | Open issues | 15 | 15 | | Language | C | Jupyter Notebook | | Adopt for | REST is a retrieval-based speculative decoding method for enhancing the efficiency of large language model inference, implemented in C under the Apache-2.0 license. | The llm-applications repository offers focused guidance on deploying RAG-based LLM apps in production environments with an emphasis on using Ray. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | CC-BY-4.0 | | Categories | Inference & Serving | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [REST](/tools/fasterdecoding-rest.md) | [llm-applications](/tools/ray-project-llm-applications.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Steady (60%) | | Days since push | 196d | 35d | | Open issues delta | 0 (30d) | +2 (30d) | | Full report | [trust report](/tools/fasterdecoding-rest/trust.md) | [trust report](/tools/ray-project-llm-applications/trust.md) | ## Shared compatibility - **Python**: [REST](/tools/fasterdecoding-rest.md) - Python runtime; [llm-applications](/tools/ray-project-llm-applications.md) - Python runtime ## Decision facts: REST - **Adopt for:** REST is a retrieval-based speculative decoding method for enhancing the efficiency of large language model inference, implemented in C under the Apache-2.0 license. ## Decision facts: llm-applications - **Adopt for:** The llm-applications repository offers focused guidance on deploying RAG-based LLM apps in production environments with an emphasis on using Ray. ## Choose when ### Choose REST if… - REST is primarily C; llm-applications is Jupyter Notebook. - License: REST is Apache-2.0, llm-applications is CC-BY-4.0. - Tags unique to REST: llm-inference, retrieval, speculative-decoding. - Use REST when you need to improve the efficiency of large language model inference through retrieval-based speculative decoding. ### Choose llm-applications if… - llm-applications is primarily Jupyter Notebook; REST is C. - License: llm-applications is CC-BY-4.0, REST is Apache-2.0. - Tags unique to llm-applications: anyscale, fine-tuning, llama2, machin-learning. - Also covers LLM Frameworks. - You require a detailed guide specifically tailored to the development and deployment of RAG-based applications, leveraging Ray for performance and scalability. ## When NOT to use REST - Avoid REST if your project does not require improvements in the efficiency of large language model inference. - Do not use REST if your project is not compatible with the C language or if you prefer a different license than Apache-2.0. ## When NOT to use llm-applications - If you are looking for a more generalized approach to LLM application development that does not specifically cater to RAG-based designs and Ray optimizations. - When your project workflow is incompatible with or cannot support Jupyter Notebook dependencies and the resources assume. ## Common questions ### What is the difference between REST and llm-applications? REST: REST: Retrieval-Based Speculative Decoding. llm-applications: Comprehensive guide to building RAG-based LLM applications for production. See the comparison table for live GitHub stats and shared categories. ### When should I choose REST over llm-applications? Choose REST over llm-applications when REST is primarily C; llm-applications is Jupyter Notebook; License: REST is Apache-2.0, llm-applications is CC-BY-4.0; Tags unique to REST: llm-inference, retrieval, speculative-decoding; Use REST when you need to improve the efficiency of large language model inference through retrieval-based speculative decoding. ### When should I choose llm-applications over REST? Choose llm-applications over REST when llm-applications is primarily Jupyter Notebook; REST is C; License: llm-applications is CC-BY-4.0, REST is Apache-2.0; Tags unique to llm-applications: anyscale, fine-tuning, llama2, machin-learning; Also covers LLM Frameworks; You require a detailed guide specifically tailored to the development and deployment of RAG-based applications, leveraging Ray for performance and scalability. ### When should I avoid REST? Avoid REST if your project does not require improvements in the efficiency of large language model inference. Do not use REST if your project is not compatible with the C language or if you prefer a different license than Apache-2.0. ### When should I avoid llm-applications? If you are looking for a more generalized approach to LLM application development that does not specifically cater to RAG-based designs and Ray optimizations. When your project workflow is incompatible with or cannot support Jupyter Notebook dependencies and the resources assume. ### Is REST or llm-applications more popular on GitHub? llm-applications has more GitHub stars (1,857 vs 220). Stars measure visibility, not whether either tool fits your constraints. ### Are REST and llm-applications open source? Yes - both are open-source projects on GitHub (REST: Apache-2.0, llm-applications: CC-BY-4.0). ### Where can I find alternatives to REST or llm-applications? GraphCanon lists graph-backed alternatives at [REST alternatives](/tools/fasterdecoding-rest/alternatives) and [llm-applications alternatives](/tools/ray-project-llm-applications/alternatives) ([REST markdown twin](/tools/fasterdecoding-rest/alternatives.md), [llm-applications markdown twin](/tools/ray-project-llm-applications/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/fasterdecoding-rest-vs-ray-project-llm-applications.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, REST or llm-applications? REST: Slowing. llm-applications: 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 REST and llm-applications? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [REST trust report](/tools/fasterdecoding-rest/trust); [llm-applications trust report](/tools/ray-project-llm-applications/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=fasterdecoding-rest`](/api/graphcanon/graph?tool=fasterdecoding-rest) - 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/_