TensorFlow Serving
TensorFlow Serving is an application that takes trained Artificial Intelligence models and makes them available for use.
Platform availability
Available on 0 of 6 install platforms
- Unraid (not listed)
- TrueNAS (not listed)
- Umbrel (not listed)
- ZimaOS (not listed)
- Proxmox (not listed)
- Helm (not listed)
Also on Docker Hub and GitHub
Health score
Not enough data yet for a health score. It needs at least 2 of 4 factors: maintenance, popularity, ease of install, resource needs.
- GitHub stars
- 6,364
- Open issues
- 80
- Last commit
- 2026-10-02
- Latest release
- 2.21.0
- License
- Apache-2.0
- Activity
- steady
Checked today - source: GitHub (tensorflow/serving)
Resources & Compatibility
ARM (e.g. Raspberry Pi) is not supported.
Checked 5 days ago - source: Docker Hub image tags and published docs
First-install notes
No first-install notes yet.
Variants
Alternatives
No alternatives collected yet.
Common questions
Can TensorFlow Serving serve models that are not TensorFlow models?
It has out-of-the-box integration with TensorFlow models. It can also be extended to serve other types of models and data.
Does it support different versions of a model for clients?
It manages model lifetimes and provides clients with versioned access to models.
Is TensorFlow Serving used for training or inference?
It handles the inference aspect of machine learning by taking models after training and serving them. It is not described in the source as a training system.
What client-facing API is documented for accessing the server?
The documentation lists a Server API and a REST Client API.
Answers sourced from www.tensorflow.org, github.com
Community
No community discussions collected yet.