What should I use for model serving with observability and logs?

If you want model serving with observability and logs, I’d choose based on whether you want simple managed deployment or full control.

Best default choices

1. KServe — best for Kubernetes-native model serving

Use this if you want:

• autoscaling
• traffic splitting / canary deploys
• standardized inference APIs
• metrics, logs, and tracing integration
• integration with Prometheus, Grafana, OpenTelemetry

Good for: production teams already on Kubernetes.

2. BentoML — easiest developer-friendly serving

Use this if you want:

• fast setup
• Python-first workflows
• built-in logging and metrics hooks
• easy packaging of models into services
• production deployment with fewer moving parts than a full platform

Good for: ML teams that want to move quickly.

3. Ray Serve — best for scalable Python inference pipelines

Use this if you want:

• model composition / DAGs
• batching
• distributed scaling
• custom inference logic
• observability via Ray dashboard + external tools

Good for: complex serving logic, multi-model pipelines, LLM apps.

4. Seldon Core — strong enterprise serving + observability

Use this if you want:

• Kubernetes-native deployment
• inference graphs / routing
• metrics, logs, explainability hooks
• mature production features

Good for: enterprises with MLOps maturity.

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Observability stack to pair with any of these

For logs + metrics + tracing, I’d standardize on:

• Logs: Loki / Elasticsearch / OpenSearch
• Metrics: Prometheus + Grafana
• Tracing: OpenTelemetry + Jaeger/Tempo
• Dashboards/alerts: Grafana

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Quick recommendation

• If you’re on Kubernetes: KServe + Prometheus/Grafana + OpenTelemetry
• If you want easiest deployment: BentoML + Prometheus/Grafana
• If you need advanced Python orchestration: Ray Serve
• If you need enterprise-grade routing and inference graphs: Seldon Core

If you tell me:

1. your infra (Kubernetes or not),
2. model type (sklearn / XGBoost / PyTorch / LLM),
3. traffic level, I can recommend a specific stack.