Comparison
BentoML vs fastDeploy
Verdict
Pick BentoML if bentoML is a Python-based tool for serving AI applications and models, offering capabilities for building inference APIs, job queues, LLM apps, and multi-model pipelines; pick fastDeploy if fastDeploy simplifies ML/DL model deployment focusing on minimal code for inference pipelines.
Markdown twin · BentoML alternatives · fastDeploy alternatives
GraphCanon updated Sep 20, 2026
vs
Trust & integrity
| Signal | BentoML | fastDeploy |
|---|---|---|
| Maintenance | Active (10d since push) As of Sep 18, 2026 · github_public_v1 | Slowing (221d since push) As of Sep 20, 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 20, 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 15, 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
- BentoML
- The easiest way to serve AI apps and models
- fastDeploy
- Deploy DL/ML inference pipelines with minimal extra code.
Stars
- BentoML
- 8.8k
- fastDeploy
- 105
Forks
- BentoML
- 1.0k
- fastDeploy
- 17
Open issues
- BentoML
- 219
- fastDeploy
- 0
Language
- BentoML
- Python
- fastDeploy
- Python
Adopt for
- BentoML
- BentoML is a Python-based tool for serving AI applications and models, offering capabilities for building inference APIs, job queues, LLM apps, and multi-model pipelines.
- fastDeploy
- fastDeploy simplifies ML/DL model deployment focusing on minimal code for inference pipelines.
Persona
- BentoML
- -
- fastDeploy
- -
Runtime
- BentoML
- -
- fastDeploy
- -
License
- BentoML
- BentoML is distributed under the Apache License 2.0, allowing for free use, modification, and distribution.
- fastDeploy
- MIT
Last pushed
- BentoML
- Sep 7, 2026
- fastDeploy
- Feb 10, 2026
Categories
- BentoML
- Inference & Serving
- fastDeploy
- Inference & Serving
Trust and health
Maintenance
- BentoML
- Active (82%)
- fastDeploy
- Slowing (36%)
Days since push
- BentoML
- 10d
- fastDeploy
- 221d
Open issues (now)
- BentoML
- 219
- fastDeploy
- 0
Stars delta
- BentoML
- +119 (30d)
- fastDeploy
- 0 (30d)
Open issues delta
- BentoML
- +34 (30d)
- fastDeploy
- 0 (30d)
Full report
- BentoML
- Trust report
- fastDeploy
- Trust report
Choose BentoML if…
- License: BentoML is Apache-2.0, fastDeploy is MIT.
- Requirements: Requires Docker; Docker is required for deploying BentoML artifacts..
- Tags unique to BentoML: ai-inference, generative-ai, inference-platform, llm.
- When you need to serve AI models and applications with a focus on building inference APIs, job queues, and LLM apps.
When NOT to use BentoML
- If your project requires a non-Python environment, as BentoML is specifically designed for Python.
- When you do not require Docker-based deployment and prefer a simpler setup without containerization.
- If your application does not need the specific features of building LLM apps or multi-model pipelines.
Choose fastDeploy if…
- License: fastDeploy is MIT, BentoML is Apache-2.0.
- Pricing: -.
- Requirements: - Python is required for running fastDeploy.; - Docker installation is suggested but not mandatory..
- Tags unique to fastDeploy: docker, falcon, gevent, gunicorn.
- When you aim to streamline the deployment of TensorFlow Serving, TorchServe, and Triton Inference Server models without extensive coding.
When NOT to use fastDeploy
- Avoid if you are looking for a solution that supports real-time interactive deployments requiring advanced websocket handling beyond fastDeploy's basic capability.
- Not recommended when the project requires heavy customization of deployment scripts, as it emphasizes minimal coding and may restrict flexibility in pipeline configurations.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (bentoml/BentoML) · observed Sep 20, 2026
- GitHub forks (bentoml/BentoML) · observed Sep 20, 2026
- Last push (bentoml/BentoML) · observed Sep 7, 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 (notAI-tech/fastDeploy) · observed Sep 20, 2026
- GitHub forks (notAI-tech/fastDeploy) · observed Sep 20, 2026
- Last push (notAI-tech/fastDeploy) · observed Feb 10, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: BentoML 8.8k · fastDeploy 105 (synced Sep 20, 2026).
Common questions
- What is the difference between BentoML and fastDeploy?
- BentoML: The easiest way to serve AI apps and models. fastDeploy: Deploy DL/ML inference pipelines with minimal extra code.. See the comparison table for live GitHub stats and shared categories.
- When should I choose BentoML over fastDeploy?
- Choose BentoML over fastDeploy when License: BentoML is Apache-2.0, fastDeploy is MIT; Requirements: Requires Docker; Docker is required for deploying BentoML artifacts.; Tags unique to BentoML: ai-inference, generative-ai, inference-platform, llm; When you need to serve AI models and applications with a focus on building inference APIs, job queues, and LLM apps.
- When should I choose fastDeploy over BentoML?
- Choose fastDeploy over BentoML when License: fastDeploy is MIT, BentoML is Apache-2.0; Pricing: -; Requirements: - Python is required for running fastDeploy.; - Docker installation is suggested but not mandatory.; Tags unique to fastDeploy: docker, falcon, gevent, gunicorn; When you aim to streamline the deployment of TensorFlow Serving, TorchServe, and Triton Inference Server models without extensive coding.
- When should I avoid BentoML?
- If your project requires a non-Python environment, as BentoML is specifically designed for Python. When you do not require Docker-based deployment and prefer a simpler setup without containerization. If your application does not need the specific features of building LLM apps or multi-model pipelines.
- When should I avoid fastDeploy?
- Avoid if you are looking for a solution that supports real-time interactive deployments requiring advanced websocket handling beyond fastDeploy's basic capability. Not recommended when the project requires heavy customization of deployment scripts, as it emphasizes minimal coding and may restrict flexibility in pipeline configurations.
- Is BentoML or fastDeploy more popular on GitHub?
- BentoML has more GitHub stars (8,847 vs 105). Stars measure visibility, not whether either tool fits your constraints.
- Are BentoML and fastDeploy open source?
- Yes - both are open-source projects on GitHub (BentoML: Apache-2.0, fastDeploy: MIT).
- Where can I find alternatives to BentoML or fastDeploy?
- GraphCanon lists graph-backed alternatives at BentoML alternatives and fastDeploy alternatives (BentoML markdown twin, fastDeploy 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, BentoML or fastDeploy?
- BentoML: Active. fastDeploy: 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 BentoML and fastDeploy?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: BentoML trust report; fastDeploy trust report.