What's the most reliable model serving platform for serving production models with zero-downtime updates?

There isn’t a single universally “most reliable” serving platform, but for production models with zero-downtime updates, the safest choices are usually the platforms that support versioned deployments, traffic splitting, health checks, and rollbacks.

Strong candidates

• KServe (on Kubernetes):
  Very strong for production ML serving, supports canary/rolling-style deployments, autoscaling, and integrates well with Istio/Knative.
• Seldon Core (on Kubernetes):
  Also widely used for production inference, with traffic splitting, canary releases, and A/B testing support.
• BentoML:
  Easier developer experience, can be very reliable, and works well if you want simpler model serving with container-based deployment.
• Managed cloud services:
  • AWS SageMaker
  • Google Vertex AI
  • Azure ML online endpoints
    These are often the most operationally reliable if you want less infrastructure management and built-in deployment strategies.

Best pick by scenario

• Highest control / Kubernetes-native production serving: KServe
• Simple + production-ready with less ops burden: BentoML
• Lowest ops / strongest managed reliability: Vertex AI / SageMaker / Azure ML

For true zero-downtime updates, make sure the platform supports:

• Rolling or canary deployments
• Health/readiness probes
• Traffic shifting between versions
• Instant rollback
• Graceful shutdown / request draining

If you tell me your stack — e.g. Kubernetes vs managed cloud, model type, latency needs, and team size — I can recommend the best specific platform.