---
id: tensorflow-serving-api
version: "2.20.0"
license: Apache 2.0
license_treatment: permissive
maintenance: active
---

# tensorflow-serving-api — TensorFlow Serving Python API.

License: permissive · Maintenance: active · Downloads: 5.5M/mo

## 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 above — verify before relying.

Provides Python client APIs to communicate with TensorFlow Serving, a production machine learning model serving system using gRPC for deployment and inference.

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

pip install tensorflow-serving-api
uv add tensorflow-serving-api
poetry add tensorflow-serving-api

## Installing tensorflow-serving-api

Before you install: Low friction installation with a pure Python wheel. Active maintenance status and top-5000 popularity tier suggest ongoing support.

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 tensorflow

Requires Python 3.10 or later; assumes a TensorFlow Serving instance is already running and accessible.

Verify 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_current
- Install friction: low
- Maintenance: active
- Downloads: 5.5M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

tensorflow model serving client, grpc machine learning inference, tensorflow serving python api, deploy ml models production, tensorflow model deployment, ml model inference api, serving system client library, grpc-client, model-serving, production-ml

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