Comparison
serve vs FastChat
Verdict
Pick serve if serve enables developers to create and deploy multimodal AI services in cloud-native environments with Python; pick FastChat if 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.
Markdown twin · serve alternatives · FastChat alternatives
GraphCanon updated 2w
Trust & integrity
| Signal | serve | FastChat |
|---|---|---|
| Maintenance | Dormant (495d since push) As of 2w · github_public_v1 | Slowing (98d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No published findings from this source as of 2026-07-11 As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- serve
- Build multimodal AI applications with cloud-native stack
- FastChat
- An open platform for training, serving, and evaluating large language models
Stars
- serve
- 22k
- FastChat
- 40k
Forks
- serve
- 2.2k
- FastChat
- 4.8k
Open issues
- serve
- 27
- FastChat
- 1.0k
Language
- serve
- Python
- FastChat
- Python
Adopt for
- serve
- Serve enables developers to create and deploy multimodal AI services in cloud-native environments with Python.
- 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
Persona
- serve
- -
- FastChat
- -
Runtime
- serve
- -
- FastChat
- -
License
- serve
- Apache-2.0
- FastChat
- Apache-2.0
Last pushed
- serve
- Mar 24, 2025
- FastChat
- May 1, 2026
Categories
- serve
- Inference & Serving, Model Training
- FastChat
- Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
Trust and health
Maintenance
- serve
- Dormant (18%)
- FastChat
- Slowing (36%)
Days since push
- serve
- 495d
- FastChat
- 98d
Open issues (now)
- serve
- 27
- FastChat
- 1.0k
OSV dependency advisories
- serve
- No published findings from this source as of 2026-07-11
- FastChat
- No lockfile (source not queried)
Full report
- serve
- Trust report
- FastChat
- Trust report
Shared compatibility
- Python · serve: Python runtime · FastChat: Python runtime
Choose serve if…
- Tags unique to serve: cloud-native, cncf, deep-learning, docker.
- - If your project requires building cloud-native applications that integrate multiple types of data (visual, text, audio) with high scalability
- Leaner open-issue backlog (27).
When NOT to use serve
- - If your project is limited to single-modal AI tasks or does not demand cloud-native deployment capabilities
- - If the team lacks familiarity with Kubernetes or gRPC, since these technologies are integral to Serve's operational model for deploying and managing services
Choose FastChat if…
- Tags unique to FastChat: chatbots, distributed-serving, evaluation system, large language models.
- Also covers Evaluation & Observability, LLM Frameworks.
- - 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (jina-ai/serve) · observed Aug 2, 2026
- GitHub forks (jina-ai/serve) · observed Aug 2, 2026
- Last push (jina-ai/serve) · observed Mar 24, 2025
- License file (Apache-2.0) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (lm-sys/FastChat) · observed Aug 7, 2026
- GitHub forks (lm-sys/FastChat) · observed Aug 7, 2026
- Last push (lm-sys/FastChat) · observed May 1, 2026
- License file (Apache-2.0) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: serve 22k · FastChat 40k (synced Aug 2, 2026).
Common questions
- What is the difference between serve and FastChat?
- serve: Build multimodal AI applications with cloud-native stack. FastChat: An open platform for training, serving, and evaluating large language models. See the comparison table for live GitHub stats and shared categories.
- When should I choose serve over FastChat?
- Choose serve over FastChat when Tags unique to serve: cloud-native, cncf, deep-learning, docker; - If your project requires building cloud-native applications that integrate multiple types of data (visual, text, audio) with high scalability; Leaner open-issue backlog (27).
- When should I choose FastChat over serve?
- Choose FastChat over serve when Tags unique to FastChat: chatbots, distributed-serving, evaluation system, large language models; Also covers Evaluation & Observability, LLM Frameworks; - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench.
- When should I avoid serve?
- - If your project is limited to single-modal AI tasks or does not demand cloud-native deployment capabilities - If the team lacks familiarity with Kubernetes or gRPC, since these technologies are integral to Serve's operational model for deploying and managing services
- 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.
- Is serve or FastChat more popular on GitHub?
- FastChat has more GitHub stars (39,517 vs 21,863). Stars measure visibility, not whether either tool fits your constraints.
- Are serve and FastChat open source?
- Yes - both are open-source projects on GitHub (serve: Apache-2.0, FastChat: Apache-2.0).
- Where can I find alternatives to serve or FastChat?
- GraphCanon lists graph-backed alternatives at serve alternatives and FastChat alternatives (serve markdown twin, FastChat 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, serve or FastChat?
- serve: Dormant. FastChat: Slowing. 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 serve and FastChat?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: serve trust report; FastChat trust report.