New Grafana dashboard (llm-observability) with 27 panels across 5 rows:
TTFT, inter-token latency, batching/scheduling, KV cache, throughput.
Both metric sources are shown side by side because they answer different
questions and neither is complete alone:
vLLM 0.11.0 (reasoning) — engine truth: kv_cache_usage_perc,
num_requests_running/waiting, num_preemptions_total, prefill vs
decode time, prefix cache, per-request histogram quantiles.
TEI 1.8.2 (embeddings, reranker) — te_batch_next_size, te_queue_size,
te_embed_duration, te_embed_queue_duration.
api-gateway — the ONLY per-request latency source covering Ollama
models (ornith:35b, qwen2.5:3b-instruct), which expose no metrics.
It is derived from the SSE byte stream, so it is streaming-only and
counts SSE chunks rather than true tokens. vLLM's own histograms are
authoritative for reasoning.
All 44 panel queries were executed against live Prometheus before commit —
all return success. vLLM/TEI panels have live series; gateway panels show
no data until the netpol fix in homelab-frontend PR #62 merges.
Also fixes a dead alert. alerts/cag-kv-cache-rules.yaml derived
vllm:prefix_cache_hit_rate from vllm:cache_query_hit_total and
vllm:cache_query_miss_total. Those metric names do not exist in vLLM
0.11.0 — the real series are vllm:prefix_cache_hits_total and
vllm:prefix_cache_queries_total. The recording rule produced no data, so
VLLMPrefixCacheHitRateLow could never fire.
Not included: real GPU hardware metrics (SM utilisation, framebuffer,
power, temperature). Only nvidia-device-plugin is deployed, which exposes
none of them; DCGM exporter is required. vllm:kv_cache_usage_perc and
preemption rate are the only GPU-pressure proxies available today.
Both metric sources existed but neither was scraped:
- servicemonitors/vllm.yaml was orphaned (absent from kustomization
resources:) and would not have matched anyway — it used 'port: http'
but KServe-generated Services in llm-serving have UNNAMED ports.
Switched to targetPort: 8080 (prometheus-operator v0.94.1 supports it),
which avoids editing the InferenceServices and therefore avoids a
predictor restart / weights reload.
- api-gw exposes llm_ttft_seconds, llm_itl_seconds, llm_tokens_total,
queue_full_requests_total, warmup_requests_delayed_total and
vllm_prefix_cache_hit_tokens_total, but the Deployment's
prometheus.io/scrape annotation is ignored by kube-prometheus-stack
(no additionalScrapeConfigs). Added a ServiceMonitor for it. This is
the only per-request latency source covering the two Ollama models,
which expose no metrics endpoint at all.
Ollama services are excluded from the vllm ServiceMonitor selector to
avoid permanently-DOWN targets.
Note: alerts/cag-kv-cache-rules.yaml references vllm:prefix_cache_hit_rate
derived from vllm:cache_query_hit_total/miss, which were never scraped.
Those alerts were dead; verify the metric names after the first scrape.
Actual TSDB data is ~2Gi. 100Gi over-provisioned Longhorn storage
contributed to the cluster-wide scheduling pressure; 25Gi is ample
with room for retention growth.
- Increase PVC from 20Gi to 100Gi (talos-cp-1 disk full)
- Move from az-a (talos-cp-1) to worker-1 (648GB available)
- Increase retention from 15d to 30d (95GB limit)
- Fixes Prometheus pod restarts due to disk pressure
Add ServiceMonitor for llm-serving namespace. Wire prometheus.io annotations to all vLLM pods (reasoning, ornith, embeddings, reranker). Scrape /metrics@8080 every 30s with proper relabeling.
Ingress api/api now backs onto api-gateway:8080; the kong Application, its
Helm values, plugins and llm-routes are removed. Gateway image v0.0.0 is in
the Forgejo registry and the pull secret is in the api namespace.
rock
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