Home/Compare/FastChat vs ort

Comparison

FastChat vs ort

Verdict

Pick FastChat when fastChat is primarily Python; ort is Rust; pick ort when ort is primarily Rust; FastChat is Python.

Markdown twin · FastChat alternatives · ort alternatives

GraphCanon updated today

FastChat logo

FastChat

lm-sys/FastChat

39kpushed May 1, 2026
vs
ort logo

ort

pykeio/ort

2.4kpushed Jul 11, 2026

Trust & integrity

SignalFastChatort
Maintenance
Steady (71d since push)
As of today · github_public_v1
Very active (0d since push)
As of today · github_public_v1
Provenance
Not a fork · Organization account
As of today · github_public_v1
Not a fork · Organization 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
ort
Fast ML inference & training for ONNX models in Rust

Stars

FastChat
39k
ort
2.4k

Forks

FastChat
4.8k
ort
255

Open issues

FastChat
1.0k
ort
1

Language

FastChat
Python
ort
Rust

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
ort
-

Persona

FastChat
-
ort
-

Runtime

FastChat
-
ort
-

License

FastChat
Apache-2.0
ort
Apache-2.0

Last pushed

FastChat
May 1, 2026
ort
Jul 11, 2026

Categories

FastChat
Model Training, LLM Frameworks, Inference & Serving, Evaluation & Observability
ort
Model Training, Inference & Serving

Trust and health

Maintenance

FastChat
Steady (60%)
ort
Very active (96%)

Days since push

FastChat
71d
ort
0d

Open issues (now)

FastChat
1.0k
ort
1

Full report

FastChat
Trust report

Choose FastChat if…

  • FastChat is primarily Python; ort is Rust.
  • 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 ort if…

  • ort is primarily Rust; FastChat is Python.
  • Tags unique to ort: fine-tuning, ai, machine-learning, onnxruntime.
  • More recently updated (last pushed Jul 11, 2026).

When NOT to use ort

  • 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 on cards: FastChat 39k · ort 2.4k (synced Jul 11, 2026).

Common questions

What is the difference between FastChat and ort?
FastChat: An open platform for training, serving, and evaluating large language models. ort: Fast ML inference & training for ONNX models in Rust. See the comparison table for live GitHub stats and shared categories.
When should I choose FastChat over ort?
Choose FastChat over ort when FastChat is primarily Python; ort is Rust; 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 ort over FastChat?
Choose ort over FastChat when ort is primarily Rust; FastChat is Python; Tags unique to ort: fine-tuning, ai, machine-learning, onnxruntime; More recently updated (last pushed Jul 11, 2026).
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 ort?
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 ort more popular on GitHub?
FastChat has more GitHub stars (39,490 vs 2,392). Stars measure visibility, not whether either tool fits your constraints.
Are FastChat and ort open source?
Yes - both are open-source projects on GitHub (FastChat: Apache-2.0, ort: Apache-2.0).
Where can I find alternatives to FastChat or ort?
GraphCanon lists graph-backed alternatives at FastChat alternatives and ort alternatives (FastChat markdown twin, ort 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 ort?
FastChat: Steady. ort: Very active. 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 ort?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: FastChat trust report; ort trust report.