Note: This page documents methodologies and frameworks I’ve developed through past consulting engagements. These are shared for educational purposes and knowledge sharing only.

Cloud Cost Model & Guardrails Methodology

Goal: make cloud spend predictable by aligning architecture and autoscaling with p95 latency, not just CPU averages.

What we examine

• Workload patterns: burst vs steady, read/write ratios
• Storage & retention: hot vs cold, partitioning, lifecycle rules
• Network & egress: cross-region flows, CDN strategy
• Autoscaling signals: CPU vs p95, queue depth, custom metrics
• Job scheduling: batch windows, concurrency, backpressure
• Metering & budgets: quotas, alerts, cost anomaly detection

Guardrails we implement

• Right-sizing & SKU selection
• Partition/cluster strategy for BigQuery
• TTLs for logs and data by class
• p95-based HPA policies; queue lag thresholds
• Budget alerts & per-team cost allocation
• “Kill switches”: protect against retry storms / cost runaways

Outcome: typical 20-40% spend reduction without rewrites, often within one quarter (based on past engagements).

Questions about this methodology? Contact me

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Conflict of Interest Disclosure

Current Employment Status: I am currently employed full-time in Germany.

This methodology was developed through past consulting engagements. It is shared for educational purposes and knowledge sharing only.

All content reflects my personal views and does not represent any current or past employer. To ensure transparency and avoid any potential conflicts of interest, any potential conflicts will be disclosed and addressed in accordance with applicable regulations.

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