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Comparison

llm-app vs awesome-mlops

Verdict

Pick llm-app if llm-app offers cloud templates for RAG, AI pipelines, and enterprise search, supporting integration with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs; pick awesome-mlops if awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling.

Markdown twin · llm-app alternatives · awesome-mlops alternatives

GraphCanon updated Oct 4, 2026

8views this month

llm-app logo

llm-app

pathwaycom/llm-app

★ 59kpushed Jul 5, 2026
vs
awesome-mlops logo

awesome-mlops

visenger/awesome-mlops

★ 14kpushed Nov 21, 2024

Trust & integrity

Signalllm-appawesome-mlops
Maintenance
Steady (74d since push)
As of Sep 18, 2026 · github_public_v1
Dormant (681d since push)
As of Oct 4, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Sep 18, 2026 · github_public_v1
Not a fork · Personal account
As of Oct 4, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
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
llm-app trust reportawesome-mlops trust reportTrust methodology

Tagline

llm-app
Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data
awesome-mlops
A curated list of references for MLOps

Stars

llm-app
59k
awesome-mlops
14k

Forks

llm-app
1.5k
awesome-mlops
2.1k

Open issues

llm-app
8
awesome-mlops
47

Language

llm-app
Jupyter Notebook
awesome-mlops
-

Adopt for

llm-app
llm-app offers cloud templates for RAG, AI pipelines, and enterprise search, supporting integration with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs.
awesome-mlops
awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling.

Persona

llm-app
-
awesome-mlops
-

Runtime

llm-app
-
awesome-mlops
-

License

llm-app
MIT License
awesome-mlops
-

Last pushed

llm-app
Jul 5, 2026
awesome-mlops
Nov 21, 2024

Categories

llm-app
Data & Retrieval, Evaluation & Observability, Inference & Serving, Model Training
awesome-mlops
Inference & Serving, Model Training

Trust and health

Maintenance

llm-app
Steady (60%)
awesome-mlops
Dormant (18%)

Days since push

llm-app
74d
awesome-mlops
681d

Open issues (now)

llm-app
8
awesome-mlops
47

Stars delta

llm-app
-117 (30d)
awesome-mlops
+101 (30d)

Open issues delta

llm-app
0 (30d)
awesome-mlops
+3 (30d)

Owner type

llm-app
Organization
awesome-mlops
User

Full report

llm-app
Trust report
awesome-mlops
Trust report

Choose llm-app if…

  • Pricing: The repository is open-source under the MIT License, but additional services or support might incur costs..
  • Requirements: Min 4 GB RAM; Requires Docker; Requires Docker for running the cloud templates.; Supports integration with a variety of data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs..
  • Tags unique to llm-app: chatbot, hugging-face, llm, llm-local.
  • Also covers Data & Retrieval, Evaluation & Observability.
  • When you need ready-to-run cloud templates for RAG, AI pipelines, and enterprise search that integrate seamlessly with data sources such as Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-ti

When NOT to use llm-app

  • Avoid using llm-app if your project does not require integration with specific data sources like Sharepoint or Google Drive, as the tool's strength lies in its broad data source support.
  • Do not use llm-app if you are looking for a tool that focuses solely on model training or inference without the need for cloud templates or enterprise search capabilities.
GitHubDetails

Choose awesome-mlops if…

  • Tags unique to awesome-mlops: ai, data-science, devops, engineering.
  • If you need references covering online training and inference service architecture patterns, consider awesome-mlops.

When NOT to use awesome-mlops

  • Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list.
  • Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance.
GitHubDetails

Explore

llm-app trust report →awesome-mlops trust report →Data & Retrieval category →Evaluation & Observability category →Inference & Serving category →Model Training category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: llm-app 59k · awesome-mlops 14k (synced Sep 20, 2026).

Common questions

What is the difference between llm-app and awesome-mlops?
llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories.
When should I choose llm-app over awesome-mlops?
Choose llm-app over awesome-mlops when Pricing: The repository is open-source under the MIT License, but additional services or support might incur costs.; Requirements: Min 4 GB RAM; Requires Docker; Requires Docker for running the cloud templates.; Supports integration with a variety of data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs.; Tags unique to llm-app: chatbot, hugging-face, llm, llm-local; Also covers Data & Retrieval, Evaluation & Observability; When you need ready-to-run cloud templates for RAG, AI pipelines, and enterprise search that integrate seamlessly with data sources such as Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-ti.
When should I choose awesome-mlops over llm-app?
Choose awesome-mlops over llm-app when Tags unique to awesome-mlops: ai, data-science, devops, engineering; If you need references covering online training and inference service architecture patterns, consider awesome-mlops.
When should I avoid llm-app?
Avoid using llm-app if your project does not require integration with specific data sources like Sharepoint or Google Drive, as the tool's strength lies in its broad data source support. Do not use llm-app if you are looking for a tool that focuses solely on model training or inference without the need for cloud templates or enterprise search capabilities.
When should I avoid awesome-mlops?
Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list. Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance.
Is llm-app or awesome-mlops more popular on GitHub?
llm-app has more GitHub stars (58,920 vs 14,228). Stars measure visibility, not whether either tool fits your constraints.
Are llm-app and awesome-mlops open source?
Yes - both are open-source projects on GitHub.
Where can I find alternatives to llm-app or awesome-mlops?
GraphCanon lists graph-backed alternatives at llm-app alternatives and awesome-mlops alternatives (llm-app markdown twin, awesome-mlops 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, llm-app or awesome-mlops?
llm-app: Steady. awesome-mlops: Dormant. 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 llm-app and awesome-mlops?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llm-app trust report; awesome-mlops trust report.

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