What should I use if I need canary deployments for models?

If you need canary deployments for models, the best thing to use is usually a model serving platform or inference gateway that supports traffic splitting / shadowing / weighted routing.

Good options

• Kubernetes + service mesh

  • Use tools like Istio, Linkerd, or NGINX Ingress for weighted traffic shifting.
  • Good if you already run models on K8s and want fine-grained control.

• Dedicated model serving platforms

  • KServe on Kubernetes
  • Seldon Core
  • BentoML
  • These often support versioned deployments and gradual rollout patterns.

• Managed ML platforms

  • AWS SageMaker (production variants / weighted routing)
  • Google Vertex AI (traffic splitting)
  • Azure ML online endpoints
  • Best if you want less infrastructure management.

What “canary” usually means for models

You route a small percentage of live traffic to the new model version, monitor:

• latency
• error rate
• prediction quality / business metrics
• drift or safety metrics

Then you gradually increase traffic if it performs well.

If you want a simple recommendation

• Already on Kubernetes? Use KServe + Istio or Seldon
• Want managed cloud? Use Vertex AI / SageMaker / Azure ML
• Need a lightweight self-hosted option? Use BentoML with your own traffic router

If you tell me your stack (Kubernetes, AWS/GCP/Azure, Python, etc.), I can recommend the best specific setup.