ChatGPT: We need to run multiple model versions, do canary releases, and monitor latency in production. What stack would you suggest?

A solid, practical stack for multiple model versions, canary releases, and latency monitoring in production would look like this:

Recommended stack

1) Model serving

• Kubernetes for orchestration
• KServe or Seldon Core for model deployment, versioning, and traffic splitting
• NVIDIA Triton Inference Server if you need high-performance GPU inference for deep learning models
• vLLM or TGI (Text Generation Inference) for LLM serving

2) Canary / traffic management

• Istio or Linkerd for service mesh traffic splitting and gradual rollouts
• Or Kubernetes-native ingress + progressive delivery tooling like:
  • Argo Rollouts
  • Flagger

These make it easy to do:

• 90/10, 50/50, etc. traffic splits
• automated promotion/rollback based on metrics
• A/B testing if needed

3) Monitoring / observability

Use the standard observability trio:

• Prometheus for metrics
• Grafana for dashboards
• Loki or ELK/OpenSearch for logs
• OpenTelemetry for traces and unified instrumentation

For latency specifically, track:

• p50 / p95 / p99 inference latency
• queue time
• model load time
• GPU utilization / memory
• request rate, error rate, timeout rate

4) Experiment tracking / model registry

• MLflow for model registry, versioning, and promotion workflows
• Alternatives: Weights & Biases, SageMaker Model Registry, Vertex AI Model Registry

5) CI/CD

• GitHub Actions or GitLab CI for build/test/deploy
• Argo CD for GitOps-based deployment
• Argo Rollouts for progressive delivery

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A good default architecture

If you want a fairly standard, production-ready setup:

• Kubernetes
• KServe for model deployment
• Istio + Argo Rollouts for canary releases
• Prometheus + Grafana + OpenTelemetry for latency monitoring
• MLflow for registry/versioning
• vLLM/Triton depending on the model type

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If you want the simplest viable option

If you don’t want a lot of moving parts:

• Kubernetes
• FastAPI model service
• Argo Rollouts for canary
• Prometheus + Grafana
• MLflow

This is simpler, but you’ll manage more serving logic yourself.

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My recommendation by use case

For LLMs

• vLLM
• Kubernetes
• Istio or Argo Rollouts
• Prometheus + Grafana + OpenTelemetry
• MLflow

For classical ML or CV models

• KServe
• Triton if GPU acceleration matters
• Argo Rollouts
• Prometheus + Grafana

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Key design tips

• Route traffic by model version, not just service version
• Log request metadata + prediction + latency + model version
• Use automated rollback if:
  • p95 latency increases beyond threshold
  • error rate spikes
  • output quality/regression metrics degrade
• Separate online serving metrics from offline evaluation metrics

If you want, I can also give you:

1. a minimal stack,
2. a best-in-class enterprise stack, or
3. a reference architecture diagram for this setup.