Embedding Pipeline Service  ·  View 18 of 22  ·  Operations

Observability

Five signal families against six pipeline stages — and the one metric that is both the SLI and the autoscaling trigger.

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Capture Prepare Embed Index Serve Contract The SLO signal Feed lag per corpus Oldest unprepared age Oldest unembedded age Version visibility lag Retrieval p99 Oldest unmigrated chunk Cost ratios Events accepted / dropped Chunk reuse rate Cache hit rate GPU batch efficiency Bytes per million chunks Cost per million queries Migration burn vs estimate Correctness Sweep discrepancies Unextractable rate Quarantined chunks Orphaned vectors Results withheld Contract mismatch rejects Quality Chunks per document Vector norm + centroid drift Index recall vs brute force Query distribution drift Answer acceptance Recall@10 by contract Compliance Tombstones received Text retention age Erasure unconfirmed Operator text access Alias flips audited Observability — Signals by Pipeline Stage Oldest-unembedded age is both the freshness SLI and the autoscaling trigger. A queue depth would not distinguish a big backlog from a stuck one. v 1.0 · owner Data & AI Platform Architecture · date 2026-10

The signal that does two jobs

  • Oldest-unembedded age per lane is the freshness SLI and the scaling trigger. Queue depth cannot distinguish a large backlog from a stuck one; an age can.
  • The same shape repeats per stage — oldest unprepared, oldest unmigrated — so a delay can be attributed to a stage rather than to the pipeline.
  • Retrieval p99 is the only latency SLI, because it is the only number a user experiences.

Cost is a monitored quantity

  • Chunk reuse rate and vector cache hit rate are tracked as operational signals, not reports: a drop in either is a cost incident before it is a budget variance.
  • GPU batch efficiency makes the freshness-versus-cost trade measurable rather than asserted.
  • Migration burn against estimate is watched during a migration, so an overrun is caught at day three rather than at the invoice.

Quality is not an afterthought

  • Vector distribution drift and query distribution drift are monitored separately, because they look identical and have different fixes.
  • Index recall against a brute-force sample distinguishes an ANN parameter problem from a model problem.
  • Results-withheld counts sit under correctness, not compliance: a spike usually means the permission authority is unwell, not that policy tightened.