tensorflow-serving-api
TensorFlow Serving Python API.
Decision gist · record as of 2026-08-14
Yes, if you are building a client application that needs to query a TensorFlow Serving deployment. The package is actively maintained, has low install friction, carries a permissive license, and is in the top 5000 by downloads. Install only if you already have or plan to run a TensorFlow Serving instance; it is a client library, not a standalone serving system.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; assumes a TensorFlow Serving instance is already running and accessible.
- Low friction installation with a pure Python wheel.
- Active maintenance status and top-5000 popularity tier suggest ongoing support.
License · maintenance · safety
Apache 2.0 (permissive) — Apache 2.0 permissive license allows commercial and private use with minimal restrictions, typical for machine learning infrastructure.
last release 2026-05-30 (76 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 5,470,578 downloads/mo, #2,092 on PyPI
Alternatives
Verify before relying
pip install tensorflow-serving-api
import protobuf
import grpcio
import tensorflow- What protocol buffer message types and gRPC service stubs are exposed by this package for client applications.
- Whether the package includes usage examples or documentation for common inference patterns.
- How version compatibility is managed between this package and its tensorflow runtime dependency.
What it is and what it does
tensorflow-serving-api is a Python client library for interacting with TensorFlow Serving, a production-grade system for deploying machine learning models. It wraps the gRPC protocol and protocol buffer definitions needed to send inference requests to a running TensorFlow Serving instance and receive predictions. The package depends on grpcio, protobuf, and tensorflow, providing the communication layer between your Python application and a remote or local model server.
Typically used in production pipelines where models are hosted on a separate TensorFlow Serving deployment, the library lets you build client applications that query those models without bundling the model files or inference engine locally. It handles serialization of input data and deserialization of responses, abstracting away low-level protocol details.
Use it for
- Build a Python application that sends inference requests to a TensorFlow Serving instance running on a separate machine or container.
- Integrate model predictions into a web service or microservice that calls a centralized model server.
- Test and validate a deployed TensorFlow model by writing client code that queries it over gRPC.
- Batch inference workloads where a Python script repeatedly calls a remote model server for predictions.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building a client application that needs to query a TensorFlow Serving deployment.
The package is actively maintained, has low install friction, carries a permissive license, and is in the top 5000 by downloads. Install only if you already have or plan to run a TensorFlow Serving instance; it is a client library, not a standalone serving system.
Install
tensorflow-serving-api on PyPI
Before you install
Low friction installation with a pure Python wheel. Active maintenance status and top-5000 popularity tier suggest ongoing support.
Requires Python 3.10 or later; assumes a TensorFlow Serving instance is already running and accessible.
License in practice
Apache 2.0 permissive license allows commercial and private use with minimal restrictions, typical for machine learning infrastructure.
Quickstart
pip install tensorflow-serving-api
import protobuf
import grpcio
import tensorflowVerify before relying
- What protocol buffer message types and gRPC service stubs are exposed by this package for client applications.
- Whether the package includes usage examples or documentation for common inference patterns.
- How version compatibility is managed between this package and its tensorflow runtime dependency.
Package facts
| License | Apache 2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesgrpcioprotobuftensorflow |
| Maintenance | Actively maintained 76 days since the last release |
| First released | |
| Downloads | 5,470,578 / month, #2,092 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: tensorflow_serving_api-2.20.0-py2.py3-none-any.whl
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See also bentoml · jina · sagemaker-serve · tritonclient · truss · mlserver · kserve · cog · smg-grpc-servicer · mlserver-mlflow