Comparison
FastChat vs awesome-mlops
Verdict
Pick FastChat when tags unique to FastChat: evaluation system, large-language-models, chatbots, distributed serving; pick awesome-mlops when tags unique to awesome-mlops: engineering, data-science, ml, ai.
Markdown twin · FastChat alternatives · awesome-mlops alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | FastChat | awesome-mlops |
|---|---|---|
| Maintenance | Steady (71d since push) As of today · github_public_v1 | Dormant (597d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of today · none |
Tagline
- FastChat
- An open platform for training, serving, and evaluating large language models
- awesome-mlops
- A curated list of references for MLOps
Stars
- FastChat
- 39k
- awesome-mlops
- 14k
Forks
- FastChat
- 4.8k
- awesome-mlops
- 2.1k
Open issues
- FastChat
- 1.0k
- awesome-mlops
- 42
Language
- FastChat
- Python
- awesome-mlops
- -
Adopt for
- FastChat
- FastChat is a comprehensive open platform for managing large language models (LLMs) that includes capabilities for training, serving, evaluating, and comparing chatbot models via web UIs and RESTful APIs. It powers ChatB
- awesome-mlops
- -
Persona
- FastChat
- -
- awesome-mlops
- -
Runtime
- FastChat
- -
- awesome-mlops
- -
License
- FastChat
- Apache-2.0
- awesome-mlops
- -
Last pushed
- FastChat
- May 1, 2026
- awesome-mlops
- Nov 21, 2024
Categories
- FastChat
- Model Training, LLM Frameworks, Inference & Serving, Evaluation & Observability
- awesome-mlops
- Vector Databases, Model Training, Inference & Serving
Trust and health
Maintenance
- FastChat
- Steady (60%)
- awesome-mlops
- Dormant (18%)
Days since push
- FastChat
- 71d
- awesome-mlops
- 597d
Open issues (now)
- FastChat
- 1.0k
- awesome-mlops
- 42
Owner type
- FastChat
- Organization
- awesome-mlops
- User
Full report
- FastChat
- Trust report
- awesome-mlops
- Trust report
Shared compatibility
- Python · FastChat: Python runtime · awesome-mlops: Python runtime
Choose FastChat if…
- Tags unique to FastChat: evaluation system, large-language-models, chatbots, distributed serving.
- Also covers LLM Frameworks, Evaluation & Observability.
- - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench.
When NOT to use FastChat
- - You require a proprietary or closed-source framework; FastChat is open-source under Apache-2.0 license and its use might be unsuitable for environments requiring proprietary solutions.
- - Your chatbot evaluation needs do not align with the types of data used in FastChat's datasets (e.g., human votes, MT-Bench evaluations).
- - You prefer a more user-friendly setup without the need to clone a repository and manually install dependencies; FastChat requires installation from source with additional steps for Rust and CMake on
- + Mac.
Choose awesome-mlops if…
- Tags unique to awesome-mlops: engineering, data-science, ml, ai.
- Also covers Vector Databases.
- Leaner open-issue backlog (42).
When NOT to use awesome-mlops
- Last GitHub push was 597 days ago (dormant maintenance, Nov 21, 2024). Validate activity before betting a new project on awesome-mlops.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (lm-sys/FastChat) · observed Jul 11, 2026
- GitHub forks (lm-sys/FastChat) · observed Jul 11, 2026
- Last push (lm-sys/FastChat) · observed May 1, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (visenger/awesome-mlops) · observed Jul 11, 2026
- GitHub forks (visenger/awesome-mlops) · observed Jul 11, 2026
- Last push (visenger/awesome-mlops) · observed Nov 21, 2024
- License file (unknown) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: FastChat 39k · awesome-mlops 14k (synced Jul 11, 2026).
Common questions
- What is the difference between FastChat and awesome-mlops?
- FastChat: An open platform for training, serving, and evaluating large language models. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories.
- When should I choose FastChat over awesome-mlops?
- Choose FastChat over awesome-mlops when Tags unique to FastChat: evaluation system, large-language-models, chatbots, distributed serving; Also covers LLM Frameworks, Evaluation & Observability; - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench.
- When should I choose awesome-mlops over FastChat?
- Choose awesome-mlops over FastChat when Tags unique to awesome-mlops: engineering, data-science, ml, ai; Also covers Vector Databases; Leaner open-issue backlog (42).
- When should I avoid FastChat?
- - You require a proprietary or closed-source framework; FastChat is open-source under Apache-2.0 license and its use might be unsuitable for environments requiring proprietary solutions. - Your chatbot evaluation needs do not align with the types of data used in FastChat's datasets (e.g., human votes, MT-Bench evaluations). - You prefer a more user-friendly setup without the need to clone a repository and manually install dependencies; FastChat requires installation from source with additional steps for Rust and CMake on + Mac.
- When should I avoid awesome-mlops?
- Last GitHub push was 597 days ago (dormant maintenance, Nov 21, 2024). Validate activity before betting a new project on awesome-mlops. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
- Is FastChat or awesome-mlops more popular on GitHub?
- FastChat has more GitHub stars (39,490 vs 13,952). Stars measure visibility, not whether either tool fits your constraints.
- Are FastChat and awesome-mlops open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to FastChat or awesome-mlops?
- GraphCanon lists graph-backed alternatives at FastChat alternatives and awesome-mlops alternatives (FastChat 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, FastChat or awesome-mlops?
- FastChat: 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 FastChat and awesome-mlops?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: FastChat trust report; awesome-mlops trust report.