For a Kubernetes-based model serving platform, the best choice depends on what you need most: simple deployment, high throughput, multi-model management, or full MLOps integration. Here’s the practical breakdown.

Best default choice

KServe

Use KServe if you want:

• Kubernetes-native model serving
• Autoscaling (including scale-to-zero)
• Standardized inference APIs
• Support for multiple runtimes
• Easy integration with Istio/Knative, model storage, and canary rollout patterns

Why it’s a strong default:
KServe is one of the most complete Kubernetes-native model serving frameworks. It’s good for production platforms where you want a clean abstraction over different model backends.

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If you need max GPU inference performance

NVIDIA Triton Inference Server

Use Triton if your priority is:

• High-performance GPU serving
• Dynamic batching
• Concurrent model execution
• TensorRT / PyTorch / TensorFlow / ONNX support
• Low latency and high throughput

Common pattern:
Use KServe as the platform layer and Triton as the model runtime behind it.

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If you want flexible Python-first serving

BentoML

Use BentoML if you want:

• Fast setup
• Python-native model packaging
• Custom preprocessing/postprocessing
• Easy APIs and deployment workflows
• Good developer experience

This is often a good choice for teams moving quickly, especially if they have custom logic around the model.

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If you want lightweight, general-purpose model hosting

Seldon Core

Use Seldon Core if you need:

• Kubernetes-native deployment
• Advanced inference graphs / pipelines
• Model explainability integrations
• Multi-step inference workflows

It’s useful, but in many teams KServe has become the more common default for new Kubernetes model serving platforms.

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If you want a full internal platform with pipelines and experiment tracking

MLflow + serving layer

Use MLflow for:

• Model registry
• Lifecycle management
• Experiment tracking

Then pair it with:

• KServe for serving
• or Triton for high-performance GPU inference

MLflow is not really a serving platform by itself, but it’s very useful as the system of record for models.

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My recommendation by scenario

1. General-purpose production platform

Choose: KServe + MLflow

• KServe for deployment/serving
• MLflow for registry and model lineage
• Prometheus/Grafana for monitoring
• Istio or Knative depending on your traffic patterns

2. GPU-heavy, latency-sensitive serving

Choose: Triton + KServe

• Triton for runtime performance
• KServe for Kubernetes abstraction and traffic management

3. Small team, fastest time to value

Choose: BentoML

• Simpler operational overhead
• Easier app-style model packaging
• Good for custom inference code

4. Complex inference pipelines or graph-based routing

Choose: Seldon Core or custom services

• Especially if you need ensembles, chains, or explainability hooks

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A sensible platform architecture

A common production setup is:

• KServe for deployment and autoscaling
• Triton for GPU model execution
• MLflow for registry/tracking
• Prometheus + Grafana for observability
• Istio/Knative for traffic routing and scale-to-zero
• Argo CD / Flux for GitOps deployment

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Short answer

If you want one recommendation:

Use KServe as your serving platform.
If you’re serving GPU models or care a lot about performance, use Triton behind KServe.

If you want, I can also give you:

1. a reference architecture for a Kubernetes model serving platform,
2. a feature comparison table of KServe vs Triton vs BentoML vs Seldon, or
3. a suggested production stack based on your model types and traffic patterns.