Systems administration and infrastructure engineering for PPC, affiliate marketing, and lead generation operations — where a slow database query isn't a technical problem, it's a revenue problem, and where downtime at 11pm on a Friday has a measurable dollar value before anyone wakes up to fix it.
The vertical that shaped the infrastructure
Most infrastructure consultants understand that downtime is bad. In performance marketing, you understand exactly how bad — per hour, per campaign, per traffic source — because the revenue reporting tells you immediately.
The infrastructure approach documented throughout this site was shaped significantly by years of operating performance marketing platforms at scale. A price comparison and search monetization platform responsible for over $30 million in annual revenue — running on a fleet of 115–135 servers at peak, handling bought pixel traffic across multiple sources, managing hourly traffic monitoring via Redis clusters, with backend processing covering bid management, RPC tracking, and ETL pipelines into BigQuery. That context is different from general systems administration. It changes how you think about every infrastructure decision.
When the MariaDB replica is lagging, the bid management system is making decisions on stale data. When the PHP-FPM pool is saturated, click traffic is being lost. When the SMS delivery queue exceeds its latency bounds, the entire affiliate pipeline halts. The monitoring, the HA design, the database tuning, the custom instrumentation documented on this site — all of it was developed in an environment where the feedback loop between infrastructure state and business outcome is measured in minutes, not days.
That background is why the infrastructure approach here is different from a generic managed services provider. The design decisions are made by someone who has run these platforms at the scale where they break, diagnosed the failures that aren't visible in conventional monitoring, and understands the specific points in the stack where performance degradation translates directly into revenue loss.
Platform experience
Bought traffic to landers monetized via blended search feeds — Google AFS, Yahoo! Search, Bing, Taboola, Pricegrabber, and others. Domain feed management, XML feed infrastructure, and the operational discipline of managing traffic quality signals that affect monetization partner relationships. Bid management systems that consume hourly traffic data from Redis clusters and adjust spend based on RPC tracking. Infrastructure that handles traffic spikes from campaign launches without degrading the click-through experience that feed partners measure.
Large-scale price comparison infrastructure handling continuous traffic across hundreds of domains, pixel traffic from multiple acquisition sources, and backend systems for data aggregation, feed normalization, and real-time bidding decisions. The $30M platform operated in this space — FreeBSD-based, Redis cluster traffic monitoring on hourly cycles, ETL pipelines moving performance data into BigQuery for reporting and bid optimization.
Both sides of the affiliate relationship. Publisher infrastructure for traffic monetization: landers, trackers, redirect chains, and the monitoring that catches when a monetization partner's API is slow before the queue backlog becomes visible in reporting. Provider infrastructure for campaign management, postback tracking, and the delivery pipelines — SMS via Twilio, email via Mailchimp and similar — with custom latency monitoring that halts queuing automatically when delivery services degrade.
Custom lead generation platforms for regulated verticals where data quality, delivery confirmation, and compliance infrastructure matter as much as volume. Click PPC feeding lead capture funnels, with delivery pipelines to buyers and the tracking infrastructure to verify and report on delivery quality. Platforms where a slow API response from a lead buyer isn't just a latency issue — it's a compliance and relationship management problem.
The problems we recognise
These are the failure modes that are specific to performance marketing infrastructure — not generic "the site is down" problems, but the ones that cost money quietly before anyone notices.
Traffic monetization
Feed partner quality signals degrading
Infrastructure performance that affects click quality — page load time, redirect latency, lander response time — feeds directly into monetization partner quality scores. A database that is periodically stalling produces lander responses that are intermittently slow. The feed partner sees the quality signal. The RPC drops. The connection between the infrastructure problem and the revenue impact takes weeks to identify without the right monitoring.
Bid management
Stale data driving spend decisions
Bid management systems that consume hourly Redis traffic data make spend decisions on whatever data is in the cluster. If Redis is evicting keys under memory pressure — or if the ETL pipeline writing performance data to BigQuery is lagging — bid decisions are made on stale signals. The spend continues at the wrong level until the next reporting cycle reveals the gap. Custom monitoring on Redis eviction rates and pipeline lag catches this before the reporting cycle.
Delivery pipeline
Queue backlog from slow third-party APIs
SMS and email delivery pipelines that depend on third-party APIs — Twilio, Mailchimp, hygiene services — are vulnerable to API degradation that backs up the queue without producing errors. The application is working. The queue is growing. The delivery latency is climbing. Without latency monitoring on the specific API endpoints that sit in the critical path, the first visible symptom is a queue that has grown beyond catch-up capacity.
Infrastructure scaling
Traffic spikes without capacity signals
Campaign launches, viral traffic events, and seasonal peaks create traffic spikes that are predictable in type but not in timing. PHP-FPM pool busyness metrics that trend toward saturation before the pool is actually exhausted provide the signal to add capacity before requests start queuing. Without that signal, the first indication of insufficient capacity is error rates — which means the traffic spike has already started damaging quality scores before the response begins.
Database performance
Query performance under concurrent load
Performance marketing databases handle concurrent writes from multiple traffic sources, reads from bid management and reporting systems, and ETL operations — simultaneously. Query optimizer decisions that look correct in isolation can be catastrophic under concurrent load. FORCE INDEX hints, proper index design for the actual access patterns, and replication topology that separates read and write load are the difference between a database that performs at scale and one that doesn't.
Cost efficiency
Infrastructure cost vs. revenue margin
At the scale where infrastructure cost is a meaningful percentage of revenue margin, every instance type decision, every over-provisioned server, and every idle standby has a real cost. AWS Reserved Instance optimization, right-sizing based on actual workload profiles, and the discipline of building for the workload rather than for worst-case theoretical peaks — all of it matters when the infrastructure bill is measured against the revenue it enables.
Technical capabilities
Traffic serving
High-throughput lander and redirect infrastructure
nginx serving static lander content with PHP-FPM handling dynamic elements, HAProxy load balancing across multiple application nodes, Redis session store for stateless application tier scaling. Tuned for low-latency response under high concurrency — the infrastructure that feed partners measure when they assign quality scores.
Traffic intelligence
Redis cluster hourly monitoring
Redis cluster infrastructure for real-time and hourly traffic data — click counts, conversion signals, RPC by source and campaign. Custom Graphite metrics tracking cluster health, eviction rates, and hit ratios. The data layer that bid management systems consume for spend decisions, instrumented to detect when the data is stale before the decisions go wrong.
Data pipeline
ETL from MariaDB to BigQuery
ETL pipeline architecture moving performance data from production MariaDB into Google BigQuery for reporting, bid optimization, and historical analysis. Pipeline lag monitoring ensures that the analytics layer reflects current performance rather than lagging behind production state. Large table management on the MariaDB side — non-blocking operations, careful index design for the specific query patterns of reporting workloads.
Delivery infrastructure
SMS and email pipeline monitoring
Integration with Twilio for SMS delivery and Mailchimp and similar platforms for email — with custom latency monitoring on each third-party endpoint. Queue depth tracking, delivery rate monitoring, and circuit breaker behavior that halts queuing when delivery services degrade beyond acceptable latency bounds. The monitoring documented in the observability page was built specifically for this failure mode.
AWS management
Cost-aware cloud infrastructure
AWS IAM infrastructure, Reserved Instance optimization, and the discipline of right-sizing based on actual workload profiles rather than theoretical peaks. At 115–135 servers, instance type decisions compound quickly. The experience of operating at that scale informs cost efficiency recommendations at any scale — including recognizing when something is wastefully overbuilt and when it's dangerously underprovisioned.
Remarketing
Tracker and content infrastructure
Remarketing infrastructure: traffic source to tracker to article/lander with Adsense, SMS and email flows for re-engagement. Tracker infrastructure with pixel and postback handling, redirect chain optimization for speed, and the content serving layer sized for remarketing traffic patterns which differ significantly from cold acquisition traffic in concurrency and session behavior.
Confidentiality
Performance marketing is a competitive vertical. Operators in the same space are often running similar traffic sources, similar monetization partners, and similar technical approaches — and most would prefer that their infrastructure decisions, campaign strategies, and technical methods remain private.
We work with multiple clients in this space, including some who compete directly with each other. We do not disclose client identities, discuss one client's methods with another, or replicate specific proprietary approaches from one engagement to the next. The infrastructure patterns we bring to each engagement are derived from broad experience across the vertical — not from the specific implementation details of any individual client.
If you are concerned about confidentiality before engaging — that concern is appropriate, and you are welcome to ask directly about how we handle it. We would rather have that conversation upfront than have it be a reason you don't reach out.
Infrastructure problems in this vertical have specific causes. We know what they are.