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Serving Frameworks: KServe, BentoML, Seldon, Triton

What each tool optimises for and when to pick which.

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Match the tool to the on-call

A pragmatic comparison

ToolStrengthWatch-out
KServeKubernetes-native, model-mesh for many small models, standard InferenceService CRD.Steep curve; needs a working K8s.
BentoMLPython-first DX, batteries-included REST/gRPC, easy to package.Less rich on multi-model serving.
Seldon CoreInference graphs, A/B routing built-in, mature for finance/health.More complex CRDs.
NVIDIA TritonTop GPU throughput, batching, multi-framework.Operationally heavier; best ROI on GPU.
Vendor (SageMaker / Vertex / Azure ML)Managed, autoscaling, monitoring built in.Vendor lock-in; cost at scale.

Don't pick the cool tool — pick the one your platform team can support on-call at 3 AM.

Analogy

Choosing a serving framework is like choosing a car: a Triton-on-GPU rig is a Formula 1 — blistering but needs a pit crew. BentoML is a Toyota — reliable, anyone can drive. KServe is a Range Rover — capable but needs paved (Kubernetes) infrastructure.

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