What should I use for AI model serving on Kubernetes?

If you want to serve AI models on Kubernetes, the best choice depends on what kind of models you’re serving and how much traffic/performance you need.

Good options

1) KServe

Best if you want a Kubernetes-native, production-focused model serving platform.

Use it when:

• You want easy deployment of ML models on K8s
• You need autoscaling, canary rollouts, A/B testing
• You’re serving TensorFlow, PyTorch, sklearn, XGBoost, ONNX, or custom containers
• You want integration with Istio/Knative and standard K8s workflows

Pros:

• Designed specifically for model serving
• Strong production features
• Supports multiple inference runtimes
• Good for batch and online inference patterns

Cons:

• More moving parts
• Can feel heavy if you just want a simple inference endpoint

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2) NVIDIA Triton Inference Server

Best for high-performance inference, especially on GPUs.

Use it when:

• You need low latency / high throughput
• You’re serving LLMs, vision models, or GPU-heavy workloads
• You want multi-framework support and batching

Pros:

• Excellent performance
• Dynamic batching
• Great GPU utilization
• Supports TensorRT, ONNX Runtime, PyTorch, TensorFlow, and more

Cons:

• More specialized
• Less “platform-like” than KServe
• You’ll need to build more of the surrounding platform yourself

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3) Ray Serve

Best if you’re serving custom Python inference pipelines or LLM apps.

Use it when:

• Your inference logic is more than “load model and predict”
• You need preprocessing/postprocessing, chaining, or multi-step workflows
• You’re building LLM apps with retrieval, tool use, or routing

Pros:

• Flexible Python-native serving
• Good for complex DAGs and orchestration
• Scales well with Ray clusters

Cons:

• Not as turnkey for pure model serving
• Operational complexity if you’re not already using Ray

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4) BentoML

Best for teams that want a simple developer experience.

Use it when:

• You want to package and deploy models quickly
• You’re building APIs around models
• You want a simpler path from notebook to service

Pros:

• Easy to get started
• Nice packaging and deployment workflow
• Works well for small-to-medium teams

Cons:

• Less “heavy-duty” than Triton/KServe for large-scale serving
• May require extra work for advanced platform features

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Quick recommendations

• Production K8s model serving platform: KServe
• Fast GPU inference / LLM backend: Triton
• Custom inference logic or LLM pipelines: Ray Serve
• Simple packaging and deployment: BentoML

If you’re serving LLMs specifically

Consider:

• vLLM for efficient LLM serving
• TGI (Text Generation Inference) from Hugging Face
• Triton if you need broader GPU inference infrastructure

My practical advice

If you’re starting fresh:

• Choose KServe if you want a general-purpose Kubernetes model serving platform.
• Choose Triton if performance on GPUs is the top priority.
• Choose Ray Serve if your serving logic is application-like, not just a single model.

If you want, I can give you a decision table based on your model type, traffic level, and whether you use GPUs.