Cost Optimization

Why Cloud Bills Keep Growing

Every growth-stage company hits the same inflection point: the cloud bill goes from a rounding error to the third-largest line item on the P&L. Usually this happens gradually — a forgotten staging environment here, an oversized database there, auto-scaling policies that scale up fast and never scale down.

By the time someone notices, the monthly bill has doubled and nobody can explain why.

We've audited cloud environments spending $20K to $500K per month and consistently find 30-50% in addressable waste. Not by degrading performance — by fixing the architecture, right-sizing resources, and implementing the financial guardrails that should have been there from the start.

Where the Money Goes

Compute Over-Provisioning

The most common waste pattern. Teams provision for peak load and run at peak capacity 24/7, even when actual utilization averages 15-20%. We see this across:

• Application servers — instances sized for launch-day traffic that now handle 10% of that volume
• Databases — production RDS instances with 64GB RAM running at 8GB utilization because someone picked the "recommended" size two years ago
• Kubernetes clusters — pod resource requests that bear no relationship to actual usage, leading to nodes running at 20% CPU while the cluster reports "no capacity"

Storage Accumulation

Data hoarding is expensive in the cloud. Common patterns:

• Unattached EBS volumes from terminated instances that nobody cleaned up
• S3 buckets with terabytes of processing artifacts that are never accessed again
• Database snapshots retained indefinitely with no lifecycle policy
• Log retention at full resolution forever, when 90% of queries hit the last 30 days

Network Costs

The cloud bills you for data transfer, and the costs add up fast:

• Cross-region traffic between services that don't need multi-region
• NAT gateway fees that dwarf the actual compute costs in some architectures
• CDN misconfigurations that serve cache misses for content that should have been cached

Our Optimization Process

Phase 1: Visibility (Week 1-2)

You can't optimize what you can't see. We set up:

• Cost allocation tags — every resource tagged by team, environment, and project so you can slice the bill by business dimension
• Usage dashboards — actual utilization vs. provisioned capacity for every major resource type
• Anomaly detection — alerts when spending patterns deviate from baseline

Most teams are surprised by what they find. The staging environment that costs $4K/month. The ML training job that runs daily even though the model hasn't been retrained in weeks. The load balancer serving zero traffic.

Phase 2: Quick Wins (Week 2-4)

Low-risk changes that deliver immediate savings:

• Right-sizing — matching instance types to actual resource requirements
• Reserved capacity — committing to 1-year or 3-year terms for predictable workloads (typically 30-40% savings over on-demand)
• Spot/preemptible instances — for fault-tolerant workloads like batch processing, CI/CD, and development environments
• Storage lifecycle policies — moving cold data to cheaper tiers, deleting artifacts past their retention period
• Scheduling — shutting down non-production environments outside business hours

These changes typically capture 20-30% of the total savings opportunity with minimal engineering effort.

Phase 3: Architecture Optimization (Week 4-8)

Deeper changes that require engineering work but deliver the largest long-term savings:

• Serverless migration — moving bursty or low-traffic workloads to pay-per-use compute
• Caching layers — reducing database and API costs by serving repeated queries from cache
• Database optimization — query tuning, connection pooling, read replicas instead of scaling up the primary
• Network architecture — reducing cross-AZ and cross-region traffic through smarter service placement

Phase 4: Guardrails (Ongoing)

Savings from a one-time optimization erode within 6 months without guardrails:

• Budget alerts — per-team and per-project budgets with alerts at 80% and 100%
• Provisioning policies — infrastructure-as-code templates that enforce right-sized defaults
• Regular reviews — monthly cost reviews that catch drift before it compounds
• FinOps culture — making cost a first-class engineering metric alongside performance and reliability

What This Looks Like

A typical engagement starts with a 2-week assessment that produces a prioritized list of optimization opportunities with estimated savings and implementation effort. From there, we work with your team to execute — starting with quick wins and progressing to architectural changes based on your appetite for change.

Most teams see measurable savings within the first month and reach their target run rate within 8-12 weeks.