TensorFlow Serving

license

Apache-2.0(OSI)

source

public(the published repository is the engine)

core features withheld

no — no enterprise path in the repository root and no paid build of the engine in the README

TensorFlow Serving's LICENSE is the Apache-2.0 text with no appended condition, and the public repository is the engine itself rather than a wrapper around closed code. Its root carries no enterprise or commercial directory, and the README describes no license-gated build, so nothing in the published source is held back.

• https://api.github.com/repos/tensorflow/serving recorded 2026-08-31

  Repo metadata for tensorflow/serving - archived false, 6,360 stars.

• https://raw.githubusercontent.com/tensorflow/serving/master/LICENSE recorded 2026-10-07

  LICENSE body, read in full: the stock Apache-2.0 text, appendix filled in for The TensorFlow Serving Authors, with no appended condition.

• https://raw.githubusercontent.com/tensorflow/serving/master/README.md recorded 2026-10-07

  README, read for a paid tier, an enterprise edition or a licence-gated build beside the published source, and for whether any component is stated to be private.

GitHub stars are the adoption signal because neither alternative fits: the tensorflow-serving-api PyPI package measures a client library's own use rather than deployments of the server, and the tensorflow/serving Docker pull count is cumulative rather than a trailing monthly figure. A star is not a use, so this is a floor on adoption, not a full account of it.

• https://api.github.com/repos/tensorflow/serving recorded 2026-08-31

  Repo metadata - stargazers_count = 6,360 - for tensorflow/serving.

• https://hub.docker.com/v2/repositories/tensorflow/serving/ recorded 2026-08-31

  Docker Hub metadata: pull_count 80,659,067, cumulative since publication. Corroborating only.

• https://pypi.org/pypi/tensorflow-serving-api/json recorded 2026-08-31

  PyPI metadata for the client library, cited to record why it is not banded: summary "TensorFlow Serving Python API.", author Google Inc., homepage tensorflow.org/serving.

TensorFlow Serving sits two tiers below vLLM. It is a general model server built before the LLM-specific serving techniques vLLM is judged on, paged attention, continuous batching, tensor parallelism, and it documents none of them.

• https://raw.githubusercontent.com/tensorflow/serving/master/README.md recorded 2026-10-07

  README, read for the engine's feature set: Model lifecycle management with a batching scheduler and gRPC/HTTP inference endpoints; extensible beyond TensorFlow models.