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Top Model Serving Frameworks

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

FrameworkK8s NativeMulti-FrameworkREST/gRPCAutoscalingModel VersioningPre/Post ProcessingAdvanced Routing (A/B/Canary)Monitoring/ExplainScale to ZeroGPU SupportTypical Use Cases
KFServing/KServe✅ (Canary)Enterprise, multi-ML, CI/CD
Seldon Core✅ (Inference Graph)✅ (A/B, Ensembles)PartialCustom pipelines, ensembles
TorchServe🚫🚫 (PyTorch)Via K8s✅ (Custom Handler)🚫🚫PyTorch production serving
FastAPI🚫✅ (Python)Via K8sCustom✅ (Python code)🚫Via extensions🚫🚫Custom REST APIs, ML demos
Knative✅ (Any)CustomCustom✅ (Traffic Split)🚫Serverless ML, event-driven
TensorFlow Serving🚫🚫 (TF only)Via K8s🚫🚫Basic🚫TensorFlow models only
BentoML🚫Via K8sPartial✅ (Python code)🚫Via Prometheus🚫ML devs, fast packaging
Triton Inference ServerVia 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

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