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Observability

Course Recap: A Production Checklist for AI at Scale

⏱ 12 min

Course 4 closed with one lesson on rate limiting. Course 8 closed with one lesson on performance. This course spent eight lessons on what those two lessons only had room to gesture at: caching AI output correctly and invalidating it safely, tracking and routing around real cost, moving slow calls off the request path, rate limiting at a scale a single counter can’t cover, and monitoring cost and latency as one combined signal instead of two separate ones. None of it was speculative, every mechanism used across this course, transients, the object cache, save_post, Action Scheduler, using_model_preference(), McpObservabilityHandlerInterface, is a real, verifiable part of WordPress or the libraries this track builds on. This lesson closes the course with a single checklist you can actually run against a production deployment, and a sensible order to build it in if you’re starting from nothing.

What you'll learn in this lesson
A single production-readiness checklist
Every control this course built, in one place, to run against a real deployment.
A sensible build order if you're starting from zero
Which of these seven controls to build first, and why that order matters.
What "done" actually looks like for each control
A concrete, checkable condition, not just "we thought about it."
Where to go from here
How this course connects back to Course 4 and Course 8, and what to revisit as your feature grows.
Prerequisites

All seven previous lessons in this course. This lesson doesn’t introduce new mechanisms, it organizes what you’ve already built into something you can check off.

Step 1: The full checklist

Production readiness: AI features at scale
AI responses are cached only where the task shape allows it
Deterministic, unchanging-input tasks are cached by a hash of the real input; conversational or time-sensitive tasks are explicitly not.
Cache invalidation is tied to real content-change hooks, not TTL alone
save_post (or the equivalent for your content type) clears the cache keys a piece of content actually produced.
Every AI call is logged with an estimated token count and cost
Through one tracked wrapper function, into a custom table, never scattered across ad hoc call sites.
A cost summary is reviewable without querying the database by hand
A wp-admin page grouping the last 30 days by feature, at minimum.
Model tier is chosen deliberately per feature, not defaulted to the strongest model everywhere
A complexity check or an escalation-on-validation-failure pattern picks the preference list.
Slow AI calls run outside the request/response cycle
Queued through as_enqueue_async_action() or as_schedule_single_action(), not blocking a save or a page render.
Rate limiting covers both a per-user and a site-wide ceiling
A hundred users individually within limits can still exceed what the site as a whole should be spending or sending.
Over-limit requests degrade gracefully where a fallback exists
A cached response or a queued retry, honestly labeled as such, rather than only a flat rejection.
Cost and latency are tracked on the same event, per ability
So you can find what is both slow and expensive, not just optimize whichever number you happened to look at first.

Step 2: A build order, if you’re starting from nothing

Not every control here has equal urgency on day one. If you’re bringing an existing AI feature up to production standard rather than starting fresh, this order front-loads the changes with the highest cost and reliability impact per unit of effort:

Suggested rollout order
1
Cost logging first
You cannot make a cost decision, model routing, caching priorities, anything, without knowing what is actually being spent today. This has no dependencies on anything else in this course.
2
Caching for the safe, deterministic tasks
Usually the single largest cost reduction available, and it also improves latency immediately, no infrastructure change required.
3
Move slow calls to Action Scheduler
Directly improves perceived reliability (no more request timeouts on slow generations) and unblocks the rate-limiting degradation pattern later.
4
Model routing for cost
Apply once you have real cost data from step 1 to know which features are actually worth routing.
5
Rate limiting, per-user and global
Increasingly urgent as traffic grows; do this before it becomes an incident rather than after.
6
Combined cost/latency observability
The capstone: once every other control exists, this is what tells you which one to revisit next.
This order optimizes for impact, not for a rigid dependency chain

Cache invalidation (Lesson 2) should ship alongside caching itself (Lesson 1), and graceful degradation (Lesson 6) depends on both caching and Action Scheduler already being in place, those are noted as dependencies above. Beyond that, treat this order as a strong default, not a rule, if your site’s actual traffic pattern makes one control more urgent than another, build that one first.

Step 3: What “done” looks like, concretely

A checklist item is only useful if you can tell whether it’s actually satisfied. Vague confidence (“we thought about caching”) isn’t the bar, a specific, checkable condition is:

Concrete completion criteria
ControlDone means
Cost loggingEvery code path that calls generate_text() routes through the tracked wrapper; a grep for direct wp_ai_client_prompt() calls outside that wrapper turns up nothing.
CachingCalling the same feature twice with identical input inside the TTL results in exactly one model call, verified the way Lesson 1’s test did.
InvalidationEditing a post and re-requesting its AI summary immediately returns content reflecting the edit, not the pre-edit cached version.
Async processingThe request that triggers a slow AI call returns before the model call completes, verified by timing the request itself.
Rate limitingBoth a per-user and a site-wide test (Lesson 6) trip their respective limit independently, with distinct, correct error reasons.
Combined observabilityA single query or report can answer “which ability is both above our latency threshold and above our cost threshold,” not just one or the other.

Step 4: Where this leaves you

You now have every control this track’s earlier brief lessons could only introduce: Course 4’s rate limiter is now a two-tier limiter with graceful fallback, and Course 8’s cache-the-query pattern is now a full caching and invalidation strategy for AI-generated content specifically, plus cost tracking, cost-based routing, background processing, and combined cost/latency monitoring that neither of those courses covered at all. If you haven’t read MCP Security & Authentication or Advanced WordPress MCP Architecture in full, both are worth going back to now, this course assumed their broader security and architecture material as background even where it went further on the two specific topics they introduced.

None of this is a one-time project

Model pricing changes, traffic patterns shift, and a feature that was cheap and fast at launch can become neither six months later without a single line of its own code changing, just its usage volume changing. Revisit the cost dashboard and the combined observability report on a real cadence, monthly at minimum for anything with meaningful traffic, rather than treating this checklist as something you complete once.

Recap

Nine concrete, checkable production controls: safe caching, real invalidation, cost logging, a reviewable cost dashboard, deliberate model routing, background processing for slow calls, two-tier rate limiting with graceful degradation, and combined cost/latency observability. Build cost logging and caching first if you’re starting from an existing feature, since they have the highest impact for the least dependency on everything else, then work through the rest in the order traffic and risk actually demand. This is the difference between an AI feature that works in a demo and one that survives contact with real production traffic.

Resources & further reading

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