I'm building an AI app on Kubernetes — what do I need for serving and observability?

For an AI app on Kubernetes, think in two layers:

1. Serving: how you expose and scale model inference.
2. Observability: how you know it’s fast, correct, reliable, and cost-effective.

1) Serving: what you typically need

Core Kubernetes pieces

• Deployments / StatefulSets
  • Deployment for stateless model servers.
  • StatefulSet if you need local persistent caches, shards, or tightly coupled replicas.
• Services
  • ClusterIP for internal routing.
  • LoadBalancer or Ingress for external access.
• Ingress / Gateway API
  • For routing, TLS termination, auth integration, and traffic splitting.
• HPA / VPA / KEDA
  • HPA for scaling on CPU/GPU/custom metrics.
  • KEDA if you want event-driven scaling or queue-based autoscaling.
• Node pools
  • Separate CPU and GPU node pools.
  • Taints/tolerations and node affinity to keep workloads on the right hardware.
• Resource requests/limits
  • Essential for scheduling and avoiding noisy-neighbor issues.
  • For GPUs, request extended resources like nvidia.com/gpu.

Model-serving components

You usually want a dedicated inference layer rather than a generic web app:

• Model server
  • Examples: KServe, Seldon, Triton Inference Server, Ray Serve, BentoML, TorchServe, vLLM, TGI.
• Model artifact storage
  • Object storage like S3/GCS/Azure Blob or PVC-backed storage.
• Model loading strategy
  • Warm startup vs lazy loading.
  • Support for model versioning and canary rollout.
• Request batching
  • Critical for throughput on GPUs.
• Streaming support
  • Useful for LLMs and token-by-token output.
• Caching
  • Prompt/prefix caching, embedding cache, response cache where appropriate.
• Queueing/backpressure
  • Protects the system under load and helps avoid timeouts and OOMs.

LLM-specific serving concerns

If you’re serving LLMs:

• GPU-aware scheduling
  • Bin packing and fragmentation matter a lot.
• KV cache management
  • Often the biggest memory pressure point.
• Token streaming
  • SSE/WebSocket/gRPC streaming is common.
• Concurrency controls
  • Max in-flight requests, max tokens per batch, admission control.
• Quantization
  • Can dramatically reduce cost and increase throughput.
• Sharding / tensor parallelism
  • Needed for larger models.
• Cold start mitigation
  • Pre-pull images, keep warm replicas, preload weights.

Operational features to include

• Health probes
  • Liveness, readiness, and startup probes.
• Rollouts
  • Blue/green or canary deployments.
• Autoscaling policy
  • Scale on queue depth, request latency, GPU utilization, or tokens/sec.
• Timeouts and retries
  • Carefully tuned to avoid retry storms.
• Authentication/authorization
  • API keys, OAuth, mTLS, or service mesh policies.
• Rate limiting
  • Per tenant/user/app to control cost and protect capacity.

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2) Observability: what you need to measure

You want observability across three signals:

• Metrics
• Logs
• Traces

And for AI systems, add:

• Model-specific quality/performance metrics

A) Infrastructure metrics

Use Prometheus/Grafana or a managed equivalent.

Track:

• Pod CPU/memory
• GPU utilization
• GPU memory usage
• GPU power/temperature if available
• Network throughput and packet loss
• Disk I/O
• Node pressure / evictions
• Replica counts and restarts
• HPA events and scaling decisions

B) Serving metrics

For inference services, track:

• Request rate
• Latency percentiles: p50, p95, p99
• Error rate
• Timeout rate
• Concurrency / in-flight requests
• Queue depth / queue wait time
• Batch size
• Tokens/sec
• Time to first token for streaming LLMs
• Time spent in prefill vs decode
• Cache hit rates
• Model load time
• OOM / restart counts

C) Model quality metrics

These depend on the application, but common ones are:

• Accuracy / precision / recall / F1
• BLEU / ROUGE / exact match for generation tasks
• Hallucination or groundedness scores
• Human feedback / thumbs up/down
• Abstention rate
• Prompt/response length distributions
• Safety / policy violation rate
• Drift metrics
  • Input drift
  • Output drift
  • Embedding drift
• Retrieval metrics if using RAG:
  • Recall@k
  • MRR
  • Context hit rate
  • Citation accuracy

D) Logs

Log:

• Request IDs / correlation IDs
• Model version
• Prompt metadata, not necessarily raw prompt content unless safe/compliant
• Latency breakdown
• Errors and stack traces
• Batch/queue info
• Retry attempts
• Safety filter decisions
• Retrieval source IDs in RAG systems

Be careful with:

• PII
• secrets
• full prompts/completions if sensitive

E) Distributed tracing

Use OpenTelemetry if possible.

Trace the full path:

• API gateway
• auth layer
• retrieval service
• vector DB
• model server
• post-processing

This helps answer:

• Where is latency coming from?
• Is the bottleneck retrieval, serialization, model inference, or downstream IO?
• Which component is failing?

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3) Recommended stack

A practical baseline:

Serving

• KServe if you want Kubernetes-native model serving with inference graphs, canary, autoscaling, and model management.
• Triton for optimized GPU inference and multiple framework support.
• vLLM or TGI for LLM serving.
• Ray Serve if you need flexible Python-native serving and orchestration.

Observability

• Prometheus + Grafana for metrics and dashboards
• OpenTelemetry for traces and unified instrumentation
• Loki or ELK/OpenSearch for logs
• Alertmanager for alerting
• Optional managed APM like Datadog/New Relic/Grafana Cloud if you want less ops overhead

AI-specific monitoring

• A model monitoring tool or custom pipeline for:
  • input/output capture
  • feedback collection
  • drift detection
  • evaluation jobs
  • offline replay testing

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4) Architecture tips that save pain

• Separate control plane and data plane
  • Keep model management/rollout logic separate from inference traffic.
• Use admission control
  • Reject or queue requests when overloaded rather than letting everything time out.
• Precompute where possible
  • Embeddings, retrieval indexes, prompt templates.
• Instrument everything with request IDs
  • Mandatory for debugging.
• Make model version part of every log/metric
  • Essential for rollback and A/B testing.
• Have a fallback path
  • Smaller model, cached response, or graceful degradation.
• Plan for GPU scarcity
  • Prioritize, queue, or tier requests.
• Security and compliance first
  • Secrets management, encryption, audit logs, PII handling.

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5) Minimum viable setup

If you want the shortest “good enough” stack:

Serving

• Kubernetes Deployment
• Ingress/Gateway
• Dedicated CPU/GPU node pools
• HPA
• A model server like vLLM/Triton/BentoML
• S3-compatible storage for weights

Observability

• Prometheus
• Grafana dashboards
• OpenTelemetry tracing
• Centralized logs
• Alerts on latency, errors, restarts, GPU memory, queue depth

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6) Key questions to decide the right design

If you answer these, the design gets much clearer:

1. Are you serving LLMs, vision models, embeddings, or classical ML?
2. Do you need GPU inference?
3. Is traffic interactive or batch?
4. Do you need streaming responses?
5. Are you doing RAG?
6. What are your SLOs for latency and availability?
7. Do you need multi-tenancy and per-tenant quotas?
8. How sensitive is the data?

If you want, I can turn this into:

• a reference architecture diagram, or
• a production-ready Kubernetes stack for your specific model type (LLM, RAG, embeddings, vision, etc.).