Applied LLMOps at scale
P13.llmops.01 · Audience: guest, it-ml, language-pro · Prerequisites: Evaluation infrastructure & experiment tracking
The P13 view of running LLM systems in production: cost/latency routing across a model fleet, eval-in-the-loop, and incident playbooks — the cross-model engineering discipline. This module is mostly a curated synthesis of material already taught elsewhere (serving and KV-cache in P10, latency budgets in P11, LLM evaluation in P5), pulled together around one operational question: how do you serve many requests, cheaply, at a quality you can defend?
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 minRoute 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) |
Try it yourself
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Scratch console
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Output appears here.
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