Why GenAI Observability Breaks in Production

#genai #llm #rag #architecture

GenAI systems usually look fine in development and staging.

Latency is predictable.
Token usage looks reasonable.
Costs seem under control.

Then the system moves to production — and something changes.

Costs creep up quietly.
Latency becomes inconsistent.
Retries and fallbacks increase.

But when teams look at their dashboards, nothing obvious is “broken”.

The problem: infrastructure metrics don’t explain AI behavior
Traditional observability answers questions like:

Is the service up?
Are requests failing?
Is CPU or memory saturated?

Those signals are useful — but they don’t explain how AI behavior changes over time.

In GenAI systems, cost and reliability are driven by things infra tools don’t model well:

token expansion across prompts
retries and partial failures
fallback model usage
routing changes
temperature and sampling effects
subtle execution drift without code changes

From the outside, the system looks healthy.
From the inside, behavior is changing.

That’s the gap most teams hit after production rollout.

The GenAI production blind spot
This is what teams usually miss:

GenAI systems don’t fail loudly — they drift quietly.

Costs don’t spike in a single incident.
Latency doesn’t collapse across the board.

Instead:

average cost per request slowly rises
tail latency worsens
retries become more frequent
fallback paths get exercised more often

And because prompts and responses are sensitive, many teams avoid collecting anything beyond coarse metrics.

So the very signals that explain why things are changing never get captured.

Why this is hard to spot early
Two reasons:

Behavioral signals aren’t first-class metrics
Tokens, retries, routing decisions, and execution paths aren’t treated like CPU or memory.
Prompt-level data is sensitive
Storing raw prompts or outputs creates privacy, compliance, and security concerns.

As a result, teams either:

collect too little and fly blind, or
collect too much and create risk.

A short visual explanation
I put together a short video that explains this production blind spot visually — why teams lose visibility once GenAI systems go live, and what kind of signals actually matter.

🎥 The GenAI Production Blind Spot
https://youtu.be/8O61U5EQpS0

It’s not a demo or a tutorial — just a clear explanation of the gap that appears in production.