Comparison
REST vs llm-applications
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.
Markdown twin · REST alternatives · llm-applications alternatives
GraphCanon updated Sep 20, 2026
7views this month
vs
Trust & integrity
| Signal | REST | llm-applications |
|---|---|---|
| Maintenance | Slowing (196d since push) As of Sep 18, 2026 · github_public_v1 | Steady (35d since push) As of Sep 19, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 19, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- REST
- REST: Retrieval-Based Speculative Decoding
- llm-applications
- Comprehensive guide to building RAG-based LLM applications for production
Stars
- REST
- 220
- llm-applications
- 1.9k
Forks
- REST
- 17
- llm-applications
- 258
Open issues
- REST
- 15
- llm-applications
- 15
Language
- REST
- C
- llm-applications
- Jupyter Notebook
Adopt for
- REST
- 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.
- llm-applications
- The llm-applications repository offers focused guidance on deploying RAG-based LLM apps in production environments with an emphasis on using Ray.
Persona
- REST
- -
- llm-applications
- -
Runtime
- REST
- -
- llm-applications
- -
License
- REST
- Apache-2.0
- llm-applications
- CC-BY-4.0
Last pushed
- REST
- Mar 5, 2026
- llm-applications
- Aug 15, 2026
Categories
- REST
- Inference & Serving
- llm-applications
- Inference & Serving, LLM Frameworks
Trust and health
Maintenance
- REST
- Slowing (36%)
- llm-applications
- Steady (60%)
Days since push
- REST
- 196d
- llm-applications
- 35d
Open issues delta
- REST
- 0 (30d)
- llm-applications
- +2 (30d)
OSV dependency advisories
- REST
- Published findings
- llm-applications
- No lockfile (source not queried)
Full report
- REST
- Trust report
- llm-applications
- Trust report
Shared compatibility
- Python · REST: Python runtime · llm-applications: Python runtime
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (FasterDecoding/REST) · observed Sep 20, 2026
- GitHub forks (FasterDecoding/REST) · observed Sep 20, 2026
- Last push (FasterDecoding/REST) · observed Mar 5, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (ray-project/llm-applications) · observed Sep 20, 2026
- GitHub forks (ray-project/llm-applications) · observed Sep 20, 2026
- Last push (ray-project/llm-applications) · observed Aug 15, 2026
- License file (CC-BY-4.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: REST 220 · llm-applications 1.9k (synced Sep 20, 2026).
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 and llm-applications alternatives (REST markdown twin, llm-applications markdown twin), 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 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; llm-applications trust report.