Applied LLMOps at scale

P13.llmops.01 · Audience: guest, it-ml, language-pro · Prerequisites: Evaluation infrastructure & experiment tracking

Monitoring & drift Production Case — The Income Model, Packaged to Monitored

LLM grading (this page):Real gradingOff — no LLM calls

One LLM call is easy; a production system makes millions of them, and at that scale every request faces the same three-way tension between latency, cost, and answer quality — pay for the biggest model everywhere and the bill is indefensible, route everything to the cheapest and quality quietly rots. This module is the P13 view of running LLM systems, and it is deliberately a synthesis: the serving and caching economics come from P10, the latency budgets from P11, and the evaluation methods from P5, pulled together here around one operational question — how do you serve many requests, cheaply, at a quality you can defend? What is new is the discipline that binds them, meaning a router that sends each request class to the cheapest model clearing its quality floor, routing decisions fed by offline evaluation, and incident playbooks ready for the day a guardrail trips or a provider goes down.

Step 1 / 3 — The LLMOps problem — serve many requests, cheaply, at defensible quality

A single model call is a solved problem. LLMOps is the discipline of running many calls, from manyrequest classes, under a budget — where the levers are which model serves which request, how you cache, how you batch, and how you fall back when something degrades.

Three forces pull against each other on every request:

Force	Where it was taught	The LLMOps lever
Latency (p95)	P11.applied-llm-systems.03 — latency budgets, batching	model size, batch window, cache
Cost	P10.llm-systems-frontier.01 — serving & KV cache	route to the cheapest model that meets the SLA
Quality	P5.evaluation.03 — LLM eval & judges	eval-in-the-loop; escalate hard requests

None of these is new material — LLMOps is the engineering synthesis that trades them off deliberately rather than one at a time.

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🎓 Practice ladder

2 graded rungs · ~25 min

Route a request to the cheapest model that meets its SLA, then design a routing policy over a mixed request stream and defend the cost/quality trade-off.

Rung 1 — cost/latency model routing

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Rung 2 — design a routing policy (senior, LLM-graded)

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⚡ Interview Ref — the quick-scan Reference face

LLMOps = running many LLM calls under a budget — levers: model choice (routing), cache, batch window, fallback. Trades latency (P11.applied-llm-systems.03) × cost (P10.llm-systems-frontier.01) × quality (P5.evaluation.03).
Routing policy = cost-first within an SLA: cheapest model whose p95 fits the budget and whose quality clears the class floor. Cache + batch before you upsize (cheapest token = one never generated).
Eval-in-the-loop: log per request (class, model, latency, cost, retrieval hit, groundedness, cache hit); batch offline eval (P13.ml-lifecycle.01) decides whether a cheaper route holds quality; the regression gate blocks a route that breaks a protected slice.
Incident playbook: retry on a peer → fall back a tier (down for latency, up for triage) → abstain on weak retrieval → alert when fallback/guardrail-trip rate crosses SLO (a drift signal).
Health-data caveat: filter the candidate model set by data-residency / self-hosting compliance before cost — residency can override price.
The trade-off, stated: fund large-model spend on the small slice of quality-critical traffic by serving the high-volume slice on the cheapest SLA-meeting model.

📚 Go Further

The material this synthesis draws together.

Type	Resource
In-app	P10.llm-systems-frontier.01 — inference & serving, KV cache (the cost/latency substrate)
In-app	P11.applied-llm-systems.03 — latency budgets & batching
In-app	P5.evaluation.03 — LLM & generation evaluation, LLM-as-judge
In-app	P13.ml-lifecycle.01 — evaluation infrastructure (the eval-in-the-loop this routing depends on)

✅ Key takeaways

LLMOps is the engineering synthesis of serving (P10), latency budgets (P11), and LLM evaluation (P5): serve many requests, cheaply, at a quality you can defend — trading the three forces deliberately, not one at a time.
A routing policy defaults to cost-first within an SLA: the cheapest model whose p95 fits the budget and whose quality clears the class floor; cache and batch before you upsize to a bigger model.
Eval-in-the-loop makes routing adaptive: log per request, batch-eval on the frozen set, demote to cheaper routes that hold quality, and let the offline-eval regression gate (P13.ml-lifecycle.01) block a route that breaks a protected slice.
Incident playbooks (retry → fall back a tier → abstain → alert) and the data-residency filter make the router robust and compliant; the explicit trade-off funds large-model spend on quality-critical traffic by serving high-volume traffic on the cheapest SLA-meeting model.

Try it yourself

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Scratch console

A scratch console with the scientific stack (pandas, numpy, scikit-learn). Runs on the server — no network, resource-limited and measured.

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