SRE Playbook: Instrumenting Sites for Campaign-Driven Traffic and Cost Efficiency

Hook: When marketing runs a sprint, SREs run a marathon — and budgets can blow up fast

Campaign windows amplify every weakness in your stack: sudden RPS spikes, cache stampedes, database connection storms and surprise cloud bills. As an SRE, you're tasked not only with keeping the site up, but with keeping costs predictable and aligned to marketing budgets. This playbook gives you an operational runbook for 2026 campaign ops: the metrics to instrument, concrete alert thresholds, robust autoscaling policies, and practical cost-control techniques tied directly to campaign budgets.

Executive summary — what this runbook delivers

• Pre-campaign checklist to align engineering, marketing and finance.
• Essential observability signals and dashboards to monitor campaign health.
• Actionable alert thresholds and SLO-based escalation guidelines.
• Autoscaling patterns (Kubernetes, serverless, VM-based) with sample policies.
• Cost-control levers linked to a campaign's total budget (including formulas).
• During-campaign play-by-play and post-campaign reconciliation steps.

2026 context: why this matters now

Marketing tooling and cloud platforms are moving fast. In January 2026 Google publicly expanded total campaign budgets to Search and Shopping — signaling that marketers expect predictable, time-boxed spend and less manual budget tuning. At the same time, cloud vendors and third-party tools shipped stronger predictive autoscaling, predictive autoscaling, edge compute for content delivery, and higher-fidelity telemetry (OpenTelemetry & eBPF becoming default). That means SRE teams can, and must, build a closer contract with marketing: guarantee availability and performance for campaign windows while keeping cloud spend inside agreed limits.

Google's total campaign budgets shift expectations: campaigns will aim to fully use a finite budget over a window rather than depend on day-to-day tweaks. — Jan 2026

Pre-campaign: align goals, budgets and risk

1. Run a kickoff with three owners

• Marketing: campaign start/end, expected conversion and budget (total and CPA targets).
• Finance: budget allocation, approval thresholds and escalation contacts.
• SRE/Engineering: capacity plan, rollback plan, and cost-control knobs.

2. Translate marketing budget into infra capacity (example)

Convert a campaign budget into a max-allowed compute spend for the campaign window. Use simple math:

Inputs: baseline_cost_per_hour (current infra cost), campaign_hours, marketing_budget, infra_alloc_pct (percentage of marketing budget you’ll spend on infra).

Max infra budget = marketing_budget * infra_alloc_pct

Max average infra_cost_per_hour = Max infra budget / campaign_hours

Example: 72-hour sale, marketing budget $90,000, infra_alloc_pct 15% => max infra budget $13,500 => max average infra cost per hour = $187.50.

Use that target to back-calculate maximum instance counts, provisioned concurrency and cache sizes.

3. Model traffic and capacity

Use historical campaign lifts, channel mix, and peak concurrency to estimate RPS. Basic formula:

expected_peak_RPS = baseline_RPS * (1 + expected_lift) * peak_multiplier

Where peak_multiplier accounts for concurrent users vs. average (e.g., 1.8–3x). Validate with A/B trial or smaller test campaign.

Instrumentation: metrics every SRE must have in 2026

Instrument to answer two questions in real time: is the site healthy, and are costs on track?

Application & user-facing metrics

• RPS / requests per second (global and by region)
• p50 / p95 / p99 latency for key endpoints
• Error rate (4xx, 5xx) and critical-transaction failures
• Conversion funnel metrics (cart adds, checkout starts, purchases) to correlate traffic to revenue
• Cache hit ratio (CDN and app cache)

Infrastructure & backend signals

• Pod/VM CPU and mem, but favor request-based metrics for scaling
• DB active connections, queue lengths, replica lag
• External API latencies (3rd-party payment/gateway)
• Autoscaler events (scale up/down times and throttles)

Cost & billing metrics

• Spend per minute and projected spend to campaign end
• Cost per 1k requests and cost per conversion
• Spot/Preemptible usage and fallback costs

Tracing & logs

Distributed tracing (OpenTelemetry) to find hot paths, and high-cardinality logging for campaign UTM tags to attribute traffic to the correct campaign.

Dashboards: one pane for SRE + one for Marketing

• SRE dashboard: RPS, p95/p99 latency, error rate, DB connections, cache hit ratio, autoscaler state, projected spend.
• Marketing dashboard: real-time conversions, spend-to-date, CTR by channel and site availability.

Alerting & SLOs: concrete thresholds and escalation

Move from raw thresholds to SLO-based alerts. Define SLOs for availability and latency, then monitor the error budget burn rate.

Sample SLOs

• Availability SLO: 99.9% successful requests (excluding planned maintenance) per campaign window.
• Latency SLO: p95 latency < 350ms for checkout endpoints.

Alert levels & thresholds (example)

• P1 - Page (Immediate): p99 latency > 1s AND error rate > 1% sustained for 5 minutes OR SLO error budget burn rate > 4 over a 1-hour window.
• P2 - Investigate (Watch): error rate > 0.5% for 15 minutes OR p95 latency > SLO target for 15 minutes.
• P3 - Notify: Cache hit ratio < 75% for 30 minutes OR projected spend > 90% of infra budget.

Billing alerting

Set synthetic billing forecasts to fire at 60%, 80% and 95% of the allocated infra budget. At 95% trigger an immediate cost-mitigation plan (see Cost Controls below).

Autoscaling policies: patterns and sample configs

Shift from CPU-only autoscaling to request- and queue-based scaling. The policies below assume you have a custom metric exporter or KEDA in Kubernetes, or equivalent for serverless/VMs.

Policy A — Request-driven HPA for stateless web frontends

• Metric: requests_per_second per pod (use a custom metric)
• Target: 800–1200 RPS per pod depending on instance type
• minReplicas: baseline_replicas + safety buffer (e.g., baseline + 20%)
• maxReplicas: derived from marketing budget (see formula below)
• cooldown: 60–120 seconds; aggressive scale-up, conservative scale-down

Policy B — Queue-depth scaling for background workers

• Metric: queue_length or backlog_seconds
• Target: process backlog to zero within X minutes (campaign SLA)
• Max concurrency limited by DB or upstream quotas

Policy C — Serverless (Lambda/Cloud Functions) with provisioned concurrency

• Pre-provision concurrency equal to expected baseline peak + 20% for warm starts.
• Use provider predictive scaling if available; otherwise schedule increases before expected spikes.

Tie maxReplicas to budget (concrete)

Given max infra budget per campaign and per-hour cost per replica, calculate:

max_replicas = floor( max_average_infra_cost_per_hour / cost_per_replica_per_hour )

Enforce hard caps at the autoscaler and in the cloud account to avoid runaway spend.

Cost-control levers: keep marketing spend and infra spend aligned

Implement technical and process levers to stay within the marketing budget while protecting availability and revenue.

1. Pre-approved cost caps and escalation

• Set cloud billing alerts at 60/80/95% and require manual approval for autoscaler max increases.
• Define an emergency SLA: if spend > 100% projected, SRE has permission to change traffic routing to tiered experience.

2. Progressive degradation feature flags

• Tier A (full experience): 100% of users under budget
• Tier B (degraded): reduce images, disable video, simplify recommendations (applies when spend > threshold)
• Tier C (preserve checkout only): read-only catalog with checkout paths prioritized

3. CDN and edge caching tactics

• Increase CDN TTL for campaign assets during peak windows.
• Use stale-while-revalidate to reduce origin hits for expensive endpoints.
• Pre-warm CDNs by sending synthetic requests or staging URLs to ensure edge nodes cache campaign assets before the spike.

4. Spot & reserved capacity mix

Use spot instances for non-critical work with automated fallback to on-demand instances. Reserve a minimum guaranteed capacity for critical frontends to avoid preemption during windows.

5. Rate-limiting and bot controls

• Enforce per-IP rate limits and per-API key quotas.
• Apply stricter limits to endpoints that cause backend storms (search, recommendations).

During the campaign: runbook for the SRE on call

1. Start-of-window: validate baseline met, clear pre-warm logs, confirm CDN warmed, and record starting spend and SLOs.
2. First 15 minutes: watch RPS, p95/p99, autoscaler events and billing projection. Confirm no large discrepancy between marketing-reported clicks and site RPS.
3. If p99 > 1s or error rate spikes: trigger P1 and execute mitigation steps (scale-up, enable degraded mode, increase cache TTLs).
4. If projected spend > 90% budget: enter cost-control plan (reduce feature set, apply rate-limiting, inform marketing of scope change).
5. Keep a running incident log (timestamped) that links decisions to conversions lost/saved and cost impact.

Post-campaign: reconcile, learn, and update

• Collect a telemetry package: RPS, errors, p95/p99, cache hit ratio, instance-hours, total infra spend, conversions and revenue.
• Run a cost-to-revenue analysis: cost per conversion vs target CPA.
• Postmortem: what worked, what failed, any feature-degradation triggers that fired and their business impact. See postmortems for incident responder playbooks.
• Update templates: traffic model coefficients, autoscaling targets, and budget allocations for the next campaign.

Advanced strategies & 2026 trends to adopt

• Predictive autoscaling: use ML-driven scaling (cloud provider or in-house) that consumes historical campaign signals and marketing schedules to pre-scale before traffic arrives.
• Edge compute for personalization: run personalization at the edge to reduce origin load and lower per-request cost.
• UTM-aware observability: instrument telemetry to capture UTM params so traffic, errors and cost are directly attributable to campaign channels and ads.
• Cost-aware orchestration: autoscalers that consider price signals (spot vs on-demand) when scaling up.
• eBPF + high-cardinality tracing for microsecond-level hotspots: essential when third-party integrations cause subtle latency spikes during campaigns.

Concrete example: 72-hour flash sale

Baseline site: 500 RPS, average instance handles 1000 RPS (due to service composition), baseline infra cost $50/hr.

Marketing expects a 3x lift at peak hours (expected_peak_RPS = 1500). Campaign window: 72 hours. Marketing budget: $90,000; infra_alloc_pct: 15% => max infra budget $13,500 => max hourly infra spend $187.50.

Compute cost per instance: $10/hr (fully loaded). That implies a budgeted cap of 18 instances on average (floor(187.5/10) = 18). Given a pod can handle 1000 RPS, 2 pods suffice for baseline but peaks might require 2-3 pods. Set minReplicas = 2, maxReplicas = 18; target 800 RPS/pod for safety.

Pre-warm: schedule 5 minutes of synthetic traffic at 50% expected peak 15 minutes before start to warm caches and scale quickly.

Alerts: P1 fires if p99 > 1.2s or error rate > 1% for 5 min. Billing watch at 80% of infra budget triggers marketing notification; 95% triggers fallbacks (degraded experience) and finance alert.

Checklist: quick operational tasks

• Create campaign tag ingestion into telemetry (UTM to traces).
• Preload CDN and raise TTL on static campaign assets.
• Set autoscaler min/max per budget calculations.
• Define degradation flags and ensure they can be flipped by SRE quickly.
• Configure billing alerts and projected spend forecasting.
• Run a 15-minute pre-campaign smoke test that simulates peak traffic.

Final takeaways — the operational contract between Marketing and SRE

Campaign windows require a formal contract: marketing provides clear budgets and expected lifts; finance signs off on infra allocation; SRE guarantees an agreed level of availability and a cost-control plan. Use telemetry and UTM-aware tracing to close the feedback loop — nothing is actionable without attribution. In 2026, with total campaign budgets and predictive scaling widely available, teams that embed these processes into their launch routines will win both uptime and predictable costs.

Call to action

Use this playbook to build your campaign runbook today: implement the metric set, set SLOs, and run a pre-campaign simulation. If you want a ready-made template and automation scripts for predictive scaling, UTM instrumentation and billing integration, theplanet.cloud offers a Campaign Ops Toolkit and consulting services to implement these steps in your stack. Book a workshop with our SRE consultants to turn this playbook into production-ready runbooks that align with your marketing budgets and SLAs.

Related Reading

• Micro‑Regions & the New Economics of Edge‑First Hosting in 2026
• AI Training Pipelines That Minimize Memory Footprint: Techniques & Tools
• Edge‑First Live Production Playbook (2026)
• ClickHouse for Scraped Data: Architecture and Best Practices
• What Beauty Brands Should Know About Platform Shifts: From Bluesky Badges to Paywall-Free Communities
• From Tarot to Triumph: Using Narrative Campaigns to Elevate Employee Awards (Lessons from Netflix)
• Vehicle Interior Gadgets from CES That Make Longer Drives Cozier (and Safer)
• Micro-Apps for Non-Developers: A Step-by-Step No-Code Build Using Claude/GPT
• Protecting Traveler Data When Using Third-party AI for Personalization