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Here’s a curated list of top model serving frameworks—including your suggestions and a few other best-in-class options—plus a side-by-side comparison so you can see where each one shines.

Top Model Serving Frameworks (2025)

1. KFServing / KServe

Kubernetes-native, multi-framework model serving.
Advanced features: autoscaling, canary rollouts, versioning, pre/post processing, scale to zero.
Supports: TensorFlow, PyTorch, scikit-learn, XGBoost, ONNX, HuggingFace, and custom containers.

2. Seldon Core

Flexible, Kubernetes-native serving for any ML framework.
Build complex inference graphs (ensembles, A/B testing, custom pre/post processors).
Enterprise features: explainability, drift/outlier detection, monitoring.

3. TorchServe

Official model server for PyTorch (by AWS & Meta).
REST/gRPC APIs, batch inference, model versioning, multi-model serving, metrics.

4. FastAPI

High-performance Python web framework.
Not “model server” out of the box but very popular for serving ML models as REST APIs.
Async, automatic docs, great developer experience.

5. Knative

Kubernetes-based serverless platform for running containerized apps (including ML models).
Autoscale to zero, event-driven, traffic splitting. Often used as a backend for KServe or custom FastAPI model servers.

6. TensorFlow Serving

Official serving system for TensorFlow models.
Production-grade, optimized for TF, supports versioning, REST/gRPC.

7. BentoML

Flexible, easy-to-use framework for model packaging and serving (supports any Python ML framework).
One-command deploy to REST/gRPC API, great for both local and cloud.
Integrates with Docker, Lambda, K8s, and cloud providers.

8. Triton Inference Server (NVIDIA)

High-performance, multi-framework server for deep learning and ML models.
Supports TensorFlow, PyTorch, ONNX, TensorRT, and more.
GPU acceleration, concurrent model execution, dynamic batching.

9. MLflow Models

Simple model serving using MLflow’s model registry; supports multiple flavors (Python, R, Java, H2O, PyTorch, etc.).
REST API out of the box, but limited to single-model-per-process.

Comparison Table: Model Serving Frameworks

Framework	K8s Native	Multi-Framework	REST/gRPC	Autoscaling	Model Versioning	Pre/Post Processing	Advanced Routing (A/B/Canary)	Monitoring/Explain	Scale to Zero	GPU Support	Typical Use Cases
KFServing/KServe	✅	✅	✅	✅	✅	✅	✅ (Canary)	✅	✅	✅	Enterprise, multi-ML, CI/CD
Seldon Core	✅	✅	✅	✅	✅	✅ (Inference Graph)	✅ (A/B, Ensembles)	✅	Partial	✅	Custom pipelines, ensembles
TorchServe	🚫	🚫 (PyTorch)	✅	Via K8s	✅	✅ (Custom Handler)	🚫	✅	🚫	✅	PyTorch production serving
FastAPI	🚫	✅ (Python)	✅	Via K8s	Custom	✅ (Python code)	🚫	Via extensions	🚫	🚫	Custom REST APIs, ML demos
Knative	✅	✅ (Any)	✅	✅	Custom	Custom	✅ (Traffic Split)	🚫	✅	✅	Serverless ML, event-driven
TensorFlow Serving	🚫	🚫 (TF only)	✅	Via K8s	✅	🚫	🚫	Basic	🚫	✅	TensorFlow models only
BentoML	🚫	✅	✅	Via K8s	Partial	✅ (Python code)	🚫	Via Prometheus	🚫	✅	ML devs, fast packaging
Triton Inference Server	✅	✅	✅	Via K8s	✅	🚫	🚫	✅	🚫	✅	High-perf, GPU, deep learning
MLflow Models	🚫	✅	✅	🚫	✅ (Registry)	🚫	🚫	🚫	🚫	🚫	Model registry/testing

Legend:
✅ = Native/built-in | 🚫 = Not native or not included | Partial = Possible but not full feature

Framework Recommendations by Use Case

All-purpose, production-ready on Kubernetes:
KServe/KFServing, Seldon Core, Triton Inference Server
PyTorch-only production serving:
TorchServe
Lightweight, developer-friendly Python APIs:
FastAPI, BentoML
Serverless, event-driven, scale to zero:
Knative (often with KServe or FastAPI)
TensorFlow-only, high-performance:
TensorFlow Serving
Easy packaging and deploy for any ML framework:
BentoML
GPU-heavy, deep learning inference at scale:
Triton Inference Server
Simple model serving for quick testing:
MLflow Models

Rajesh Kumar

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