TFServing

Overview

TFServing is a high-performance serving system for machine learning models, designed for production environments. It handles model versioning, multiple model management, and provides a gRPC/REST API for inference requests. The system is built in C++ and integrates tightly with TensorFlow models.

Best for

Best for
Teams deploying TensorFlow models at scale in production

Use cases

• Deploying TensorFlow models to production with version management
• Serving multiple models simultaneously with dynamic loading
• Running low-latency inference via gRPC or REST endpoints

Notes

TFServing is a high-performance serving system for machine learning models, designed for production environments. It handles model versioning, multiple model management, and provides a gRPC/REST API for inference requests. The system is built in C++ and integrates tightly with TensorFlow models.

6,353 stars on GitHub. Last updated 2026-05-28. Licensed Apache-2.0.

Use cases

• Deploying TensorFlow models to production with version management
• Serving multiple models simultaneously with dynamic loading
• Running low-latency inference via gRPC or REST endpoints

Pros

• Optimized for high throughput and low latency in C++
• Supports model versioning and seamless rollback
• Mature and widely adopted in production ML pipelines

Cons

• Primarily designed for TensorFlow models, limited support for other frameworks
• Requires significant infrastructure setup and tuning for optimal performance
• Documentation can be sparse for advanced configurations

Indexed from awesome-llmops and enriched against its public facts.