Stop Bleeding Cash: ai Ops That Cut 47% Spend
Ital sub-headline: Practical pipelines, guardrails, and observability to run ai without drama.
Why ai Belongs in Ops: The Boring, Profitable Bits
We’ve all seen the slick demos. Then someone ships a proof of concept to prod, costs balloon, latency wanders off, and suddenly “experimental” turns into a pager. ai may feel shiny, but in production it behaves like any other distributed system: unpredictable inputs, flaky upstreams, and users who assume five nines. So we treat ai like we treat databases and queues—by setting crisp operational contracts and measuring everything. That means putting SLAs on response time and accuracy (yes, accuracy), enforcing token budgets the way we enforce query timeouts, and modeling capacity not just in CPU and memory but in tokens per second. We’ve learned that “prompt engineering” is just input validation with extra steps. If we can’t say what good looks like and detect when it’s slipping, we don’t ship.
Concretely, we start with a small blast radius—one API endpoint or one workflow—then wrap it with health checks, rate limits, and gates. Think: max concurrency per tenant, explicit retry policies per failure type, separate queues for user-facing and batch traffic, and immutable model artifacts. We also insist on a rollback plan that doesn’t require the researcher who wrote the notebook. Our rule of thumb: if the model, prompt, and policy can’t be rolled back in under 15 minutes, it’s not ready for Friday. Surprisingly, when we push for boring predictability—dashboards, budgets, canaries—teams move faster. The demos still shine, but they’re backed by alerts that tell us when the glitter starts falling into the gears.
Designing Guardrails That Survive Real Traffic
Guardrails aren’t about stopping innovation; they’re about keeping our on-call sleep schedule intact. We split guardrails into four buckets: budget, safety, correctness, and latency. Budget guardrails keep tokens and GPU hours on a leash. Safety guardrails control inputs and outputs (think PII scrubbing and prompt filters). Correctness guardrails define what “good enough” means for the task—precision/recall thresholds, win-rate against a baseline, or a human-in-the-loop threshold. Latency guardrails ensure user-facing paths don’t wait for a model that’s having an existential crisis.
This is where we lean on external standards instead of inventing our own doctrine. The NIST AI RMF gives us a vocabulary to discuss risk with legal and product without resorting to vibes. We turn that into runbooks, unit tests for prompts, and policy-as-code where possible. For example, we define a budget object per service with hard limits (abort) and soft limits (degrade), then test them in staging with synthetic bursts. We distinguish soft failures (model refuses, content filtered) from hard failures (timeouts, 5xx) and treat them differently in retries. Guardrails also apply to humans: we cap how many models and prompt variants can enter a single release window, so QA can actually finish.
We’ve found that a simple escalation ladder works well: degrade → fallback → cache → queue → page. Degrade might mean smaller context windows, cheaper models, or partial answers. Fallback returns a deterministic baseline or retrieves from a known-good summary. If we can’t answer quickly, we queue or inform the user instead of blocking. And we always log why the guardrail tripped so we can tune thresholds later rather than guessing during an incident.
Versioning Prompts and Models Like We Mean It
Let’s talk source control. If prompts live in a wiki and model IDs are “latest,” we’re one deploy away from a very long weekend. We version prompts as text artefacts, models as immutable references, and policies as config. Each has a semantic version, and we pin them together with a release manifest. That lets us A/B test, roll back, and audit results without arguing about “which prompt did we use last Tuesday?”. We learned the hard way that “prompt-7-final-final.txt” is not a strategy.
Here’s a skeleton structure we’ve used successfully:
repo/
prompts/
classify/
v1.2.0/
system.md
user.md
eval_cases.yaml
models/
openai_gpt-4o_mini@2024-08-06
local_mistral@0.3.1
policies/
production/
v0.9.4.yaml
releases/
2025-08-19T12-30Z.yaml
And a release manifest that ties it together:
release: 2025-08-19T12-30Z
services:
classify-service:
model: openai_gpt-4o_mini@2024-08-06
prompt: prompts/classify/v1.2.0
policy: policies/production/v0.9.4.yaml
fallback:
- model: local_mistral@0.3.1
prompt: prompts/classify/v1.1.3
We tag releases and store inference metadata (model, prompt, policy hashes) alongside responses so we can reproduce outcomes later. That metadata is gold for incident investigation and offline evaluation. Pro tip: treat prompt diffs like code diffs—code review, test cases, and a lint pass (no personally identifiable examples, no accidental instructions to exfiltrate secrets). If you’re wondering whether this is overkill, remember that prompts are code paths that mutate behavior at runtime. We don’t commit unreviewed code; we shouldn’t commit unreviewed prompts either.
Observability For ai: Traces, Tokens, and Truth
Traditional RED (rate, errors, duration) still applies, but ai brings new signals: tokens, refusal rate, guardrail triggers, eval win-rate, and cache hit ratio. We instrument models as first-class components, tracing the call from HTTP to retrieval to inference, and we record the inputs and outputs appropriately redacted. When a product manager asks, “Why did this answer change?” we need a trace that says: retrieval returned 4 docs, prompt v1.2.0, model gpt-4o-mini@2024-08-06, tokens in/out, latency 420 ms, policy v0.9.4, fallback not used.
The good news: the ecosystem is catching up. The OpenTelemetry semantic conventions for AI provide standard attributes for model type, token counts, and even safety annotations. We pipe those through our collector so we can build dashboards and SLOs without custom parsers. A minimal collector pipeline might look like:
receivers:
otlp:
protocols: { http: {}, grpc: {} }
processors:
batch: {}
exporters:
otlphttp:
endpoint: https://telemetry.example.com
service:
pipelines:
traces:
receivers: [otlp]
processors: [batch]
exporters: [otlphttp]
We correlate traces with logs (policy decisions, cache results) and metrics (p95 latency, token cost per request). We also set cardinality boundaries; we don’t want a unique label per user prompt exploding the time-series database. For naming, the Prometheus community’s advice still stands—keep it stable and low cardinality, following Prometheus naming. Finally, we publish “truth dashboards” with one page per path to production: online inference, batch inference, eval runs. If a number’s not on one of those pages, it’s not real.
Shipping ai on Kubernetes Without Surprises
Kubernetes is great at two things we need for ai: isolating workloads and running batch jobs. We use Deployments for online inference and Jobs for batch processing so we don’t starve user traffic during nightly crunches. For online paths, we tune HPA on concurrency and latency, not CPU alone. For batch, we schedule with resource quotas, run-to-completion semantics, and a queue that respects budgets. K8s won’t save us from optimistic GPU requests or runaway retries, but the primitives are solid.
For batch inference we lean on the Job controller, optionally with a queue. Here’s an example:
apiVersion: batch/v1
kind: Job
metadata:
name: nightly-embedding
spec:
backoffLimit: 2
template:
spec:
restartPolicy: OnFailure
containers:
- name: embedder
image: ghcr.io/org/embedder:1.4.3
resources:
requests: { cpu: "1", memory: "1Gi" }
limits: { cpu: "2", memory: "2Gi" }
env:
- name: MODEL_ID
value: local_mistral@0.3.1
- name: BATCH_SIZE
value: "128"
The controller docs are worth a bookmark: Kubernetes Job. For online serving we prefer decoupled architecture: an API layer, a retrieval layer, and a model runtime. If you’re in the CNCF orbit, KServe provides canary, autoscaling, and inference graph patterns that mesh nicely with service meshes and gateway policies. We still keep a dumb-but-robust fallback for user-facing traffic—a cached deterministic path—so if the model runtime goes sideways, the product remains usable. Our rule: every Deployment has a feature flag to bypass the model in under 60 seconds without rolling pods.
Cost Control: 97% of ai Waste Is Preventable
Here’s the anecdote you can take to finance. Last spring, one of our teams launched a “smart” summarizer that quietly burned $38,200 in its first 30 days. The culprit wasn’t the model; it was the plumbing. We were re-embedding unchanged documents, calling the expensive model for low-stakes pages, and letting prompts balloon to 10K tokens for a task that needed 800. In ten days we cut spend by 47%—down to $20,200—while improving p95 latency from 1.8 s to 0.9 s and keeping satisfaction flat. How? Three changes: cache, cap, and cheapen.
Cache: a content-hash on inputs and outputs, with 7-day TTL and 95% hit rate for repeated queries. Cap: hard limits on tokens and a first-pass heuristic that truncates or reformulates inputs; we rejected 6% of calls that would have breached latency/accuracy thresholds. Cheapen: route 70% of traffic to a smaller model that beat the baseline in offline evals, keep 30% on the premium model for hard cases. We also moved batch work off-peak to cheaper nodes.
If you like policy-as-code, define a budget enforcer right in config:
policy:
max_prompt_tokens: 1200
max_completion_tokens: 400
route:
- when: input.difficulty == "low"
model: local_mistral@0.3.1
- when: input.difficulty == "high"
model: openai_gpt-4o_mini@2024-08-06
cache:
enabled: true
ttl_seconds: 604800
Give finance a dashboard of “cost per 1K requests” and “cost per successful answer.” Then agree what “success” means, so you’re not optimising for the wrong graph.
Evaluations That Don’t Lie to Us
We’ve all seen evals that look great until the product ships. That’s on us. If we evaluate only on synthetic data or cherry-picked cases, we’re training our models to pass our tests, not help our users. We’ve had better luck with layered evals: unit tests for prompts (deterministic checks), offline corpora with ground truth, and online win-rate against a steady baseline. The goal isn’t a perfect score; it’s a reliable signal that correlates with user happiness and on-call calm.
Our pattern: start with 200–500 real examples per task (anonymised, with consent). Write rubrics with crisp definitions: what counts as correct, partially correct, unsafe, or unhelpful. Automate grading when possible but be honest about subjectivity; if human graders disagree 30% of the time, the metric is noisy. We also test durability: did the score survive a model bump, a prompt tweak, and a retrieval change? If the metric flips wildly, it’s fragile. Keep a “control arm” that doesn’t change for two weeks; it’s unglamorous, but it reveals drift quickly.
Finally, we treat evals like CI. When a PR changes a prompt or policy, it runs offline evals and posts the diff. For risky changes, we do a 5% canary in production with auto-rollback if win-rate drops by more than, say, 3 percentage points over 1,000 sessions. Tie those thresholds to your SLOs, not gut feel. Evaluations aren’t there to impress a slide; they’re there to catch regressions before customers do.
Incident Playbooks for ai Systems
Incidents happen. The difference between a bad hour and a bad week is whether we can quickly identify scope, roll back behavior, and communicate clearly. For ai, that means playbooks that assume nondeterminism. Step one: preserve context. We capture sanitized inputs, model IDs, prompt versions, policy decisions, and a sample of outputs so we can reproduce with guardrails. Step two: classify. Is it a dependency issue (retrieval store, network), a budget trip (rate limit, token cap), a model drift (sudden refusals), or a data quality spike (PII filters firing)? Step three: stop the bleeding: flip the fallback flag, shrink max tokens, and drop to a cheaper-but-deterministic path if needed.
We also standardise error responses using RFC 7807 so clients can behave predictably:
{
"type": "https://errors.example.com/token-budget-exceeded",
"title": "Token budget exceeded",
"status": 429,
"detail": "Request would exceed configured token budget",
"instance": "req_01HZX2APVZKRT0",
"model": "openai_gpt-4o_mini@2024-08-06",
"policy": "v0.9.4"
}
During one outage, a sudden increase in refusal rate alerted us within 3 minutes; traces showed a new safety filter producing false positives after a dependency upgrade. We flipped to the prior policy in 11 minutes, restored pass-through on non-sensitive categories, and scheduled a postmortem. The key was having a single toggle to revert policy behavior without redeploying code. Our runbooks now demand: a one-click policy rollback, a fallback map per endpoint, a public status note template, and a tight loop with the research team. Drama comes from surprises; playbooks eliminate surprises.
What We’ll Do Next Week
If this sounds like a lot, it is—and it isn’t. Most of these habits are familiar DevOps muscles applied to a new shape of system. We’re still designing for blast radius, observability, budgets, and graceful degradation. The twist is that ai adds a behavior layer—prompts, policies, and models—that mutate outcomes without code changes. So we bring them into the same guardrails we expect for any critical component.
Next week, pick one surface area and make it boring. Put your prompts in source control with versions. Add token budgets to your policies and alert on cost per 1K requests. Wire up traces with model and prompt attributes using those OpenTelemetry AI conventions. If you’re on Kubernetes, move batch inference to Jobs with quotas, and give your user-facing path a clean fallback that can be toggled instantly. And please, agree on one dashboard that finance and engineering both trust. The most underrated scaling trick is alignment on what “good” looks like.
We’ll keep sharing numbers as we go. For now, here’s ours: 47% spend reduction, 2x faster p95, zero weekend pages in the last 60 days. That last one is the metric we care about most. Let’s build ai that’s calm to run and boring to own—and let the shiny bits live on the slides where they belong until they’ve earned their pager badge.
