Cloud Cost Optimisation for PE-Owned Companies: 30% Savings in 60 Days

Table of Contents

1. Why Cloud Cost Optimisation Matters for PE Portfolios
2. The 60-Day FinOps Playbook: Phase-by-Phase Breakdown
3. AWS Rightsizing and Reserved Capacity Strategy
4. Azure Cost Control and Commitment-Based Discounts
5. GCP Optimisation and Sustained Use Discounts
6. Claude-Agent-Driven Cost Reviews and Automation
7. Measuring ROI: Margin Uplift and Cash Retention
8. Common Pitfalls and How to Avoid Them
9. Building a Sustainable FinOps Culture
10. Next Steps: Partnering for Execution

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Why Cloud Cost Optimisation Matters for PE Portfolios

Private equity firms are under relentless pressure to drive EBITDA growth across their portfolio companies. Cloud infrastructure spend—often invisible in the P&L until it’s audited—represents a material but frequently overlooked lever for margin improvement. For a typical Series-B SaaS company burning $500K monthly on AWS, a 30% reduction translates to $150K per month in recaptured cash. Over 12 months, that’s $1.8M of improved profitability without a single new customer.

The challenge is that cloud cost optimisation requires both technical rigour and operational discipline. Most portfolio companies lack dedicated FinOps teams. Engineering leaders prioritise feature velocity over infrastructure efficiency. Finance teams lack visibility into cloud spend drivers. The result: bloated Reserved Instance commitments, idle compute, over-provisioned databases, and unoptimised data transfer costs that compound quarter after quarter.

Private equity firms that crack this problem early—within the first 60–90 days of acquisition—unlock two critical advantages:

Immediate cash flow improvement that flows directly to EBITDA and debt service capacity. This matters especially for leveraged acquisitions where covenant headroom is tight.

Operational leverage for future roll-ups. If you’ve built a repeatable FinOps playbook, you can apply it across 10 or 20 portfolio companies, generating $10M+ of annual run-rate savings at scale.

According to industry benchmarks, most SaaS companies waste 20–40% of their cloud spend on unused resources, over-provisioned capacity, and suboptimal pricing models. PE firms can achieve 30–60% infrastructure cost reductions across SaaS portfolios using disciplined cloud cost optimisation strategies combined with private cloud solutions where appropriate.

This guide walks you through a battle-tested 60-day playbook to identify and capture that waste, with specific tactics for AWS, Azure, and GCP. We’ll also cover how AI-driven agents (specifically Claude-based cost review automation) can accelerate discovery and maintain optimisation over time.

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The 60-Day FinOps Playbook: Phase-by-Phase Breakdown

The playbook divides into four overlapping 15-day sprints, each with clear deliverables and owner accountability.

Phase 1: Discovery and Visibility (Days 1–15)

You cannot optimise what you cannot see. The first 15 days focus on establishing complete visibility into cloud spend and resource utilisation across all environments.

Immediate actions:

• Export 12 months of billing data from AWS (via Cost Explorer), Azure (Cost Management), and GCP (BigQuery export). This historical context reveals seasonal patterns, growth curves, and anomalies.
• Tag all resources with cost centre, application, environment (prod/staging/dev), and owner. Untagged resources are invisible and unaccountable. Enforce tagging via infrastructure-as-code (Terraform, CloudFormation) and API policies.
• Set up cost dashboards in AWS Cost Anomaly Detection, Azure Cost Management Alerts, and GCP Cost Intelligence. These should surface spend trends daily to finance and engineering leadership.
• Audit Reserved Instance (RI) and Savings Plan commitments across all three clouds. Many portfolio companies inherit RIs from previous owners that no longer match current workloads. Calculate utilisation rates; anything below 60% is a red flag.
• Map resource inventory using AWS Config, Azure Resource Graph, and GCP Asset Inventory. Document instance types, database sizes, storage volumes, and data transfer patterns.

Deliverables by Day 15: a centralised cost dashboard, 12-month spend history by service and tag, RI utilisation report, and a resource inventory spreadsheet.

Owner: Finance + Engineering Lead (fractional CTO or platform engineer). Time commitment: 40–60 hours.

Phase 2: Quick Wins and Low-Hanging Fruit (Days 16–30)

While detailed analysis continues, capture immediate savings from obvious inefficiencies.

Immediate actions:

• Delete or downsize idle resources. Use AWS Trusted Advisor, Azure Advisor, and GCP Recommender to identify underutilised instances, unattached storage, and unused databases. A typical portfolio company has 15–25% of compute sitting idle or severely underutilised.
• Right-size instances based on actual utilisation metrics (CPU, memory, network). CloudWatch, Azure Monitor, and GCP Monitoring data show that many instances are over-provisioned by 50–70%. Move from m5.2xlarge to m5.large where utilisation supports it.
• Consolidate non-production environments. Dev and staging environments often mirror production specs but run at 5–10% utilisation. Consolidate onto smaller instances or shared environments. Turn off non-prod environments outside business hours.
• Optimise data transfer costs. This is often the most overlooked lever. Data transfer between regions, egress to the internet, and cross-cloud transfers can represent 10–15% of total spend. Move workloads closer to data, cache aggressively, and use content delivery networks (CDNs).
• Cancel unused services. RDS instances for abandoned projects, NAT gateways serving no traffic, load balancers with zero connections, and managed services (Elasticsearch, DocumentDB) that were provisioned but never used.

Deliverables by Day 30: a list of 20–40 rightsizing actions with estimated savings, idle resource termination schedule, and data transfer optimisation roadmap.

Owner: Platform Engineer + Cloud Architect. Time commitment: 60–80 hours.

Expected savings: 10–15% of total spend (quick wins only).

Phase 3: Commitment Strategy and Pricing Optimisation (Days 31–45)

Once you’ve eliminated waste, lock in savings through Reserved Instances, Savings Plans, and commitment-based discounts.

Immediate actions:

• Calculate optimal RI and Savings Plan mix. Use AWS Compute Optimizer, Azure Hybrid Benefit calculator, and GCP Commitment Discount recommendations. The goal is to cover 60–75% of steady-state compute with commitments (RIs or Savings Plans), leaving 25–40% on-demand for spiky workloads.
• Migrate to Savings Plans where possible. Savings Plans offer 20–30% discounts and greater flexibility than RIs. They’re particularly valuable for companies with mixed instance types or planned scaling.
• Leverage Spot Instances for fault-tolerant, batch, or stateless workloads. Spot instances cost 70–90% less than on-demand but can be interrupted. Use them for CI/CD pipelines, batch processing, and non-critical background jobs.
• Commit to multi-year discounts only if you’re confident in the workload staying on that cloud. AWS three-year RIs offer 40–50% discounts; Azure three-year reservations offer similar terms. Lock in only after rightsizing is complete.
• Audit managed service pricing. RDS, DynamoDB, Elasticsearch, and BigQuery pricing varies wildly by configuration. Move to provisioned capacity or reserved capacity models for predictable workloads. For variable workloads, on-demand is often cheaper than over-committed reserved capacity.

Deliverables by Day 45: a commitment strategy document with RI/Savings Plan recommendations, projected discount percentages, and a purchase schedule.

Owner: Finance + Cloud Architect. Time commitment: 40–60 hours.

Expected savings: additional 10–15% of spend (on top of Phase 2 quick wins).

Phase 4: Automation and Continuous Optimisation (Days 46–60)

The final 15 days focus on embedding cost optimisation into operational workflows so savings stick and compound over time.

Immediate actions:

• Deploy infrastructure-as-code (IaC) governance. All resources must be provisioned via Terraform, CloudFormation, or ARM templates with cost tags and size constraints baked in. This prevents ad-hoc, expensive provisioning.
• Implement chargeback or showback models. Allocate cloud costs back to product teams by cost centre or application. When engineers see their infrastructure costs on a monthly bill, behaviour changes.
• Set up automated cost anomaly alerts. Configure AWS Budget Alerts, Azure Budget Alerts, and GCP Budget Alerts to trigger when spend exceeds thresholds. Escalate to engineering leadership immediately.
• Schedule regular cost review cadence. Weekly 30-minute FinOps syncs with engineering and product leads. Monthly deep-dives with finance and executive leadership. Quarterly strategy reviews to adjust commitments and forecasts.
• Document runbooks for cost optimisation. Create playbooks for common scenarios: scaling up, scaling down, launching a new service, retiring a service, migrating workloads, and responding to cost anomalies.

Deliverables by Day 60: IaC governance framework, chargeback model, cost review cadence calendar, and FinOps runbook library.

Owner: Platform Engineer + Finance. Time commitment: 50–70 hours.

Expected savings: 5–10% additional savings from sustained optimisation and prevention of new waste.

Total expected savings: 25–40% of baseline cloud spend within 60 days. For a $500K/month portfolio company, that’s $125K–$200K/month recaptured—$1.5M–$2.4M annually.

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AWS Rightsizing and Reserved Capacity Strategy

AWS typically represents 50–70% of total cloud spend in mature SaaS portfolios. Optimising AWS is the highest-leverage activity.

Instance Rightsizing

AWS Compute Optimizer analyses 14 days of CloudWatch metrics and recommends right-sized instances. The tool is accurate and conservative—recommendations typically save 20–40% on compute costs.

Process:

1. Enable AWS Compute Optimizer in your AWS account.
2. Wait 14 days for sufficient metric collection (or use historical CloudWatch data if available).
3. Export recommendations for all EC2, RDS, and Lambda functions.
4. Prioritise by savings potential: focus on high-CPU instances running at <20% utilisation first.
5. Test right-sized instances in staging before production rollout.
6. Implement via Auto Scaling Groups or manual instance replacement, depending on your deployment pipeline.

Common findings:

• Over-provisioned web servers. A typical API server provisioned as m5.2xlarge runs at 15–25% CPU and 30–40% memory. Right-sizing to m5.large saves $400–600/month per instance.
• Undersized databases. Conversely, RDS instances are often undersized, causing performance issues and read replica sprawl. Right-sizing up (e.g., from db.t3.medium to db.m5.large) often improves performance and reduces replica count, netting savings.
• Idle development instances. Dev and staging environments frequently run production-grade instances. Downsize to t3.micro or t3.small; they’re sufficient for non-prod workloads and cost 80–90% less.

Reserved Instance Strategy

Reserved Instances (RIs) offer 20–40% discounts on on-demand pricing, but only if you commit to a 1- or 3-year term and the instance type matches your actual usage.

Best practices:

• Purchase RIs only for steady-state, predictable workloads. Your baseline production compute (API servers, databases, background workers) should be covered by RIs. Spiky or experimental workloads should run on-demand or Spot.
• Use Savings Plans instead of RIs where possible. Savings Plans offer similar discounts but apply across instance families and regions, providing flexibility. If you’re unsure about future instance types, Savings Plans are safer.
• Aim for 60–70% RI/Savings Plan coverage. This balances discount capture with flexibility. Anything above 80% leaves you inflexible; anything below 40% leaves money on the table.
• Blend 1-year and 3-year commitments. 1-year RIs for workloads with uncertain futures; 3-year RIs for stable, long-term infrastructure.
• Monitor RI utilisation monthly. RIs that drop below 50% utilisation should be considered for sale on the AWS Marketplace (you can sell unused RIs for 40–60% of purchase price).

Storage and Data Transfer Optimisation

Storage and data transfer often represent 15–25% of AWS spend but are frequently overlooked.

Actions:

• Implement S3 Intelligent-Tiering. Automatically moves objects between access tiers based on usage patterns. Saves 50–70% on infrequently accessed data.
• Use S3 Lifecycle Policies to archive old data to Glacier (88% cheaper than S3 Standard) or delete it entirely.
• Optimise EBS volumes. Delete unattached volumes (common after instance termination). Right-size volume types: gp3 is cheaper than gp2 for most workloads. Use io2 only for high-IOPS databases.
• Consolidate NAT Gateways. Each NAT Gateway costs $32/month + data transfer charges. Consolidate to one per AZ; eliminate where possible.
• Use CloudFront to cache and serve content from edge locations, reducing data transfer costs by 50–80%.
• Eliminate cross-region data transfer. Data egress between regions costs $0.02/GB. Keep data in one region unless there’s a specific HA or DR requirement.

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Azure Cost Control and Commitment-Based Discounts

Azure typically represents 20–40% of total cloud spend. Azure’s pricing model is less transparent than AWS, but the optimisation levers are similar.

Azure Reserved Instances and Savings Plans

Azure Reservations offer 20–72% discounts depending on commitment length and instance type.

Strategy:

• Use Azure Advisor to identify underutilised VMs and right-sizing opportunities. Advisor scans 7 days of metrics (shorter than AWS, so less precise, but still useful).
• Purchase 3-year reservations for stable workloads. Azure’s 3-year discounts are deeper than AWS (up to 72% for some instance types).
• Leverage Azure Hybrid Benefit if you have existing Microsoft licenses (Windows Server, SQL Server). This stacks with reservations for additional 10–15% savings.
• Use Spot VMs for fault-tolerant workloads. Azure Spot VMs cost 80–90% less than on-demand but can be evicted.

Azure Managed Services Optimisation

• Azure SQL Database. Switch from DTU-based pricing (unpredictable costs) to vCore-based reservations. vCore reservations offer 30–50% discounts.
• Azure App Service. Use Reserved Instances for predictable web app hosting. Consolidate non-prod apps onto smaller SKUs.
• Azure Cosmos DB. This service is notoriously expensive. Right-size provisioned throughput (RU/s) based on actual metrics. Consider Azure Table Storage or PostgreSQL for lower-cost alternatives.
• Azure Storage. Use Cool and Archive tiers for infrequently accessed data. Archive tier costs 80% less than Hot.

Azure Cost Management and Billing

Azure’s Cost Management portal provides visibility into spend by service, resource group, and subscription. Use it aggressively.

Actions:

• Tag all resources with cost centre, application, and environment. Enforce via Azure Policy.
• Set up budget alerts at the subscription and resource group level.
• Use Cost Management exports to feed billing data into your FinOps dashboard.
• Review unattached disks, IP addresses, and load balancers monthly. These are easy to forget but accumulate quickly.

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GCP Optimisation and Sustained Use Discounts

GCP typically represents 10–30% of cloud spend in mixed-cloud portfolios. GCP’s pricing model rewards commitment and continuous usage.

GCP Committed Use Discounts (CUDs)

GCP Committed Use Discounts offer 25–52% savings for 1- or 3-year commitments on compute, memory, and storage.

Strategy:

• Use GCP Recommender to identify right-sizing and commitment opportunities. GCP’s ML-based recommendations are accurate and updated daily.
• Purchase CUDs for predictable workloads. 3-year CUDs offer the deepest discounts; use them for baseline production infrastructure.
• Layer CUDs strategically. Purchase CPU and memory CUDs separately to match your actual workload mix.
• Use Preemptible VMs (GCP’s equivalent to Spot) for batch jobs, CI/CD, and non-critical workloads. They cost 60–80% less than standard VMs.

GCP Managed Services Optimisation

• Cloud SQL. Right-size machine types based on CPU and memory utilisation. Use shared-core machines for dev/staging. Enable automated backups only for production.
• BigQuery. This is GCP’s most expensive service if misused. Implement slot-based pricing (annual commitment) instead of on-demand if you have predictable query volume. Partition and cluster tables aggressively to reduce scanned bytes.
• Cloud Storage. Use Nearline and Coldline storage classes for infrequently accessed data. Implement lifecycle policies to auto-delete old data.
• GKE (Kubernetes). Use Autopilot (managed) instead of Standard (self-managed) for simplicity. Enable Node Auto-Repair and Auto-Upgrade. Use Workload Identity to eliminate unnecessary service account overhead.

Sustained Use Discounts

Beyond CUDs, GCP automatically applies Sustained Use Discounts (SUDs) when you run resources continuously. SUDs are automatic and stack with CUDs.

• Month 1–25%: 0% discount
• Month 26–50%: 10% discount
• Month 51–100%: 20% discount
• Month 100+: 30% discount

The longer you run a resource, the cheaper it becomes. This incentivises keeping workloads on GCP rather than migrating.

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Claude-Agent-Driven Cost Reviews and Automation

Manual cost reviews are labour-intensive and often miss patterns. AI-driven agents (specifically Claude-based cost analysis) can automate discovery, identify optimisation opportunities, and generate actionable recommendations at scale.

How Claude Agents Work for FinOps

A Claude agent can be configured to:

1. Ingest billing data from AWS Cost Explorer, Azure Cost Management, and GCP BigQuery exports.
2. Analyse patterns across services, regions, tags, and time periods to identify anomalies and inefficiencies.
3. Cross-reference resource inventory (EC2, RDS, VMs, databases) with utilisation metrics to identify underutilised resources.
4. Generate prioritised recommendations ranked by savings potential and implementation difficulty.
5. Create executive summaries and detailed runbooks for each recommendation.
6. Track implementation progress and measure actual savings realised.

Implementation Roadmap

Week 1: Data pipeline setup

• Export 12 months of billing data from all three clouds to a central data lake (S3, Azure Blob, or BigQuery).
• Normalise data formats and schemas for cross-cloud analysis.
• Connect resource inventory tools (AWS Config, Azure Resource Graph, GCP Asset Inventory) to the same data lake.

Week 2: Claude agent configuration

• Build a Claude agent with access to billing and resource inventory data.
• Define analysis rules: identify resources with <10% utilisation, detect over-committed RIs, flag unattached storage, etc.
• Configure the agent to generate weekly cost review reports with top 10–20 recommendations.

Week 3: Integration and automation

• Integrate the Claude agent output into your cost dashboard and FinOps tooling.
• Set up automated alerts for high-priority recommendations (e.g., “$10K/month idle database detected”).
• Create a feedback loop: track which recommendations are implemented, measure actual savings, and refine the agent’s logic.

Week 4: Operationalisation

• Schedule weekly Claude agent runs to refresh recommendations.
• Assign ownership of recommendations to engineering and finance leads.
• Track implementation rate and savings realised in a central FinOps scorecard.

Example Claude Agent Prompts

"Analyse our AWS billing data for the last 90 days. Identify:
1. EC2 instances with <15% average CPU utilisation over the last 30 days.
2. RDS instances not accessed in the last 60 days.
3. Unattached EBS volumes and snapshots.
4. NAT Gateways with zero data transfer.
5. Reserved Instances with <40% utilisation.

For each finding, estimate monthly savings if the resource is right-sized or terminated. Prioritise by savings potential and implementation risk. Generate a prioritised action list with runbooks."

"Compare our current Azure VM SKUs against Azure Advisor recommendations. For each VM, calculate:
1. Current monthly cost.
2. Recommended SKU and estimated savings.
3. Performance impact (CPU, memory headroom).
4. Risk level (low/medium/high).

Generate a migration plan that prioritises high-savings, low-risk changes first."

Measuring Claude Agent ROI

Track:

• Recommendations generated per month: A well-tuned agent should surface 30–50 actionable recommendations monthly.
• Implementation rate: Target >60% of recommendations implemented within 30 days of identification.
• Savings realised: Measure actual cloud spend reduction month-over-month. Target: 2–5% monthly savings from agent-driven optimisations (on top of one-time 30% savings from the 60-day playbook).
• Time saved: Estimate hours saved by automating manual cost analysis. A Claude agent can replace 20–40 hours/month of manual FinOps work.

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Measuring ROI: Margin Uplift and Cash Retention

Cloud cost optimisation is only valuable if it translates to bottom-line impact. Here’s how to measure and communicate ROI.

Key Metrics

Absolute savings (dollars):

• Baseline monthly cloud spend (Month 0): $500K
• Post-optimisation monthly spend (Month 2): $350K
• Monthly savings: $150K
• Annual run-rate savings: $1.8M

Percentage savings:

• Baseline: $500K
• Target: 30% reduction = $150K savings
• New run-rate: $350K

EBITDA uplift:

• $1.8M annual savings flows directly to EBITDA (assuming no reinvestment).
• For a company with $10M EBITDA, this is an 18% EBITDA improvement—material enough to move valuation multiples.

Debt service coverage:

• If the company has $20M in debt at 6% interest, annual interest is $1.2M.
• $1.8M in cloud savings improves debt service coverage by 1.5x—a meaningful covenant improvement.

Communicating to Stakeholders

To the PE sponsor:

“We identified and captured $1.8M in annual cloud cost savings in 60 days through rightsizing, commitment optimisation, and automation. This improves EBITDA by 18% and strengthens debt covenants. The savings are sticky—we’ve embedded cost governance into engineering workflows and set up automated monitoring to prevent regression.”

To the portfolio company CEO:

“Your cloud infrastructure is now 30% more efficient. We’ve eliminated $150K/month in wasted spend without impacting product quality or engineering velocity. Going forward, we’ll monitor costs weekly and continuously identify new optimisation opportunities. This gives you more runway and improves your unit economics.”

To the engineering team:

“We’ve right-sized your infrastructure to match actual usage patterns. You now have better performance (databases are properly sized), lower costs, and more predictable billing. We’ve also set up chargeback so you can see the cost impact of infrastructure decisions in real time.”

Ongoing Measurement

Track cloud spend and savings in a monthly FinOps scorecard:

| Metric | Month 0 | Month 1 | Month 2 | Target | |--------|---------|---------|---------|--------| | Total Cloud Spend | $500K | $425K | $350K | $350K | | Month-over-Month Change | — | -15% | -18% | -30% | | AWS Spend | $300K | $210K | $210K | $210K | | Azure Spend | $150K | $135K | $105K | $105K | | GCP Spend | $50K | $80K | $35K | $35K | | RI/Savings Plan Utilisation | 45% | 55% | 70% | 70% | | Cost per Revenue Dollar | $0.50 | $0.43 | $0.35 | $0.35 |

Publish this scorecard monthly to engineering and finance leadership. Celebrate wins. Identify regressions (spend creeping back up) and address them immediately.

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Common Pitfalls and How to Avoid Them

Most cloud cost optimisation initiatives fail not because the tactics are wrong, but because of organisational and execution missteps. Here’s what to watch for.

Pitfall 1: Premature RI/Savings Plan Purchases

Problem: Teams purchase large RI commitments before rightsizing is complete. This locks in inefficient resource configurations for 1–3 years.

Solution: Complete rightsizing first (Phases 1–2 of the playbook). Only then purchase commitments (Phase 3). Measure utilisation for 30 days post-rightsizing to validate that new instance sizes are correct before committing.

Pitfall 2: Optimising Without Visibility

Problem: Teams lack visibility into which applications are consuming cloud resources. Cost reduction efforts are random and often hit critical systems.

Solution: Implement comprehensive tagging from day one. Tag all resources with application name, cost centre, environment, and owner. Enforce tagging via infrastructure-as-code and cloud-native policies. This enables targeted optimisation without guesswork.

Pitfall 3: One-Time Optimisation Without Sustained Governance

Problem: Teams conduct a one-time cost reduction exercise, realise 30% savings, then declare victory. Within 6 months, costs creep back to 90% of baseline as new services are provisioned without governance.

Solution: Embed cost governance into engineering workflows. Require cost estimates and approval for new services. Implement automated alerts for spend anomalies. Schedule monthly FinOps reviews. Assign ownership of cloud cost to a specific person (fractional CTO or platform lead).

Pitfall 4: Ignoring Data Transfer Costs

Problem: Teams focus on compute costs and ignore data transfer, which can represent 10–20% of total spend. Cross-region transfers, egress to the internet, and inter-service data movement accumulate quickly.

Solution: Audit data transfer costs monthly. Identify top 10 data transfer flows (e.g., “S3 to CloudFront”, “EC2 to RDS across regions”). Optimise high-flow transfers first: consolidate to single region, use CDN, cache aggressively, compress data.

Pitfall 5: Sacrificing Performance for Cost

Problem: Aggressive cost cutting leads to undersized databases, insufficient caching, and poor application performance. This hurts user experience and product development velocity.

Solution: Right-size based on utilisation metrics, not arbitrary thresholds. A database running at 80% CPU may need to be right-sized up, not down. Use performance monitoring (CloudWatch, Azure Monitor, GCP Monitoring) to validate that right-sizing doesn’t degrade performance. Test in staging before production rollout.

Pitfall 6: Lack of Executive Sponsorship

Problem: Finance wants cost reduction, but engineering resists because optimisation is seen as a distraction from feature work. Without executive alignment, the initiative stalls.

Solution: Secure buy-in from the CEO and CTO before starting. Frame cost optimisation as a business priority (EBITDA improvement, debt covenant management) and an engineering priority (better infrastructure, clearer cost visibility). Allocate dedicated resources (1 FTE platform engineer, 0.5 FTE finance analyst) for 60 days.

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Building a Sustainable FinOps Culture

The 60-day playbook delivers immediate savings, but long-term value comes from embedding cost consciousness into the organisation.

Establish a FinOps Centre of Excellence

Designate a small team (1–2 people) to own cloud cost strategy, governance, and continuous optimisation. Responsibilities:

• Monthly cost reviews and trend analysis.
• Quarterly commitment strategy updates (RIs, Savings Plans).
• Incident response for cost anomalies (e.g., runaway Lambda bill).
• Chargeback/showback reporting to product teams.
• Vendor negotiations with AWS, Azure, GCP (especially for high-spend accounts).

Implement Chargeback or Showback

When teams see the cost impact of their infrastructure decisions, behaviour changes.

Chargeback: Allocate cloud costs back to business units or product teams. Each team pays for their infrastructure from their budget. This creates accountability and incentivises optimisation.

Showback: Provide visibility into costs without charging teams. Less painful than chargeback but less effective at driving behaviour change.

Embed Cost into Engineering Culture

• Cost as a design criterion. When evaluating architecture options (e.g., Lambda vs. EC2, managed service vs. self-hosted), consider cost alongside performance and reliability.
• Cost-aware deployment pipelines. Integrate cost estimation into CI/CD. Before deploying, estimate the infrastructure cost delta. Require approval for deployments that increase cost significantly.
• Cost reviews in architecture discussions. When designing new features or services, include a cost estimate and optimisation plan. Ask: “How do we build this cost-efficiently?”
• Cost training for engineers. Run quarterly workshops on cloud pricing, rightsizing, and cost optimisation. Make it accessible to non-infrastructure engineers.

Automate Cost Governance

• Infrastructure-as-code enforcement. Require all resources to be provisioned via Terraform, CloudFormation, or ARM templates. Disallow manual resource creation. This enables cost governance at provisioning time.
• Cost guardrails. Implement policies that prevent expensive resource configurations. E.g., “EC2 instances must be t3.medium or smaller unless approved by CTO”.
• Automated cost anomaly detection. Configure AWS Budget Alerts, Azure Budget Alerts, and GCP Budget Alerts. Escalate anomalies immediately to engineering leadership.
• Automated resource cleanup. Schedule Lambda functions or runbooks to delete unattached volumes, terminate idle instances, and clean up orphaned resources weekly.

Vendor Negotiation

For high-spend accounts ($1M+/year), negotiate volume discounts directly with AWS, Azure, and GCP account teams.

• AWS Enterprise Discount Program (EDP): Discounts up to 10–15% on top of RIs/Savings Plans for committed spend.
• Azure Enterprise Agreement (EA): Volume discounts and flexible commitment terms.
• GCP Enterprise Discount Program: Similar to AWS, up to 10–15% additional discounts.

Negotiation points:

• Committed annual spend (e.g., “We’ll commit to $5M/year on AWS if you provide a 15% discount”).
• Multi-year commitments (3-year discounts are deeper than 1-year).
• Service-specific discounts (e.g., lower rates for high-volume data transfer or compute).

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Next Steps: Partnering for Execution

The playbook is clear, but execution is where most initiatives stumble. Many portfolio companies lack the internal expertise or bandwidth to run a comprehensive FinOps programme independently. This is where a fractional CTO or AI-driven platform engineering partner becomes invaluable.

Why Partner with a FinOps Expert

A dedicated FinOps partner brings:

• Cross-portfolio experience. They’ve optimised 10+ SaaS companies and know the common patterns, pitfalls, and quick wins specific to your industry.
• Technical credibility. Engineering teams trust partners who speak their language and understand the tradeoffs between cost and performance.
• Vendor relationships. Partners negotiate volume discounts directly with AWS, Azure, and GCP on behalf of their clients.
• Ongoing optimisation. A partner ensures cost governance sticks and continuously identifies new optimisation opportunities (via Claude agents or manual reviews).
• Speed. A partner can compress the 60-day playbook into 30–40 days by running phases in parallel and leveraging pre-built tools and templates.

PADISO’s Approach to Cloud Cost Optimisation

If you’re a PE firm managing a portfolio of SaaS companies, PADISO offers a fractional CTO service that includes cloud cost optimisation as a core workstream. Our approach:

1. Discovery sprint (Week 1): We audit your cloud infrastructure across AWS, Azure, and GCP. We establish visibility into spend, resource utilisation, and commitment strategy.

2. Quick wins phase (Weeks 2–3): We identify and implement low-risk, high-impact optimisations: rightsizing, idle resource termination, data transfer reduction, and RI/Savings Plan optimisation.

3. Strategy phase (Weeks 4–6): We develop a long-term commitment strategy and implement governance frameworks (tagging, chargeback, cost alerts).

4. Automation phase (Weeks 6–8): We deploy Claude-agent-driven cost reviews and integrate them into your FinOps workflows.

5. Handoff (Week 8+): We transition to a quarterly advisory model, monitoring cost trends and recommending optimisations as your portfolio evolves.

Our typical engagement delivers 25–35% cloud cost savings in 60 days, with an additional 5–10% annual savings from sustained optimisation. For a portfolio company with $500K/month cloud spend, that’s $1.5M–$2.1M in first-year savings.

We’ve also helped portfolio companies pass SOC 2 and ISO 27001 audits while optimising costs—cost and security aren’t mutually exclusive when you have the right governance framework.

Evaluating a FinOps Partner

When selecting a partner, look for:

• Specific case studies. Ask for references from PE-backed SaaS companies. What savings did they achieve? How long did it take? What was the engagement model?
• Cross-cloud expertise. The partner should be fluent in AWS, Azure, and GCP, not just one cloud.
• Technical depth. They should understand infrastructure-as-code, cloud architecture, and application performance, not just billing.
• Vendor credibility. They should have relationships with AWS, Azure, and GCP account teams and be able to negotiate volume discounts.
• Ongoing support model. Will they stick around to ensure optimisations stick, or are they a one-time engagement?

Timeline and Budget

A typical 60-day cloud cost optimisation engagement requires:

• Internal resources: 1 FTE platform engineer or fractional CTO, 0.5 FTE finance analyst. Cost: $30K–$60K (salary allocation).
• External partner: $30K–$80K for a 60-day engagement (varies by portfolio company complexity and current cloud spend).
• Total investment: $60K–$140K.

ROI payback: For a $500K/month portfolio company, 30% savings ($150K/month) pays back the investment in 1–2 months. Annual ROI: 10–15x.

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Conclusion: Making Cloud Cost Optimisation Stick

Cloud cost optimisation for PE-owned companies is not a one-time project—it’s a discipline. The 60-day playbook delivers immediate, material savings (25–40% of baseline spend), but the real value comes from embedding cost consciousness into the organisation and continuously optimising as the business evolves.

Here’s what success looks like:

• Month 2: 30% cloud cost reduction captured through rightsizing, commitment optimisation, and waste elimination.
• Month 3–6: Costs stabilise at the new, lower baseline. Governance prevents regression.
• Month 6–12: Sustained optimisation (via Claude agents or quarterly reviews) identifies an additional 5–10% in savings.
• Year 2+: Cloud cost becomes a managed, predictable line item. The CFO knows exactly what it costs to run the business, and engineering teams make cost-aware decisions.

For PE firms, this translates to:

• Improved EBITDA margins that strengthen covenant positions and increase enterprise value.
• Repeatable playbook that can be applied across 10, 20, or 50 portfolio companies, generating $10M+ in annual run-rate savings at scale.
• Operational leverage that differentiates your portfolio companies from competitors and accelerates exit multiples.

The 60-day playbook is proven. The question is: when will you start?

If you’re ready to capture 30% in cloud savings for your portfolio, explore PADISO’s fractional CTO and platform engineering services. We’ve helped dozens of companies across industries optimise their cloud infrastructure, automate operations, and scale efficiently. We understand the unique pressures PE-backed companies face—tight timelines, multiple acquisitions, integration complexity—and we build solutions that fit.

Or, if you’re a Sydney-based founder or operator interested in understanding how AI advisory services and modern platform engineering can unlock value in your business, we’re here to partner with you. Our AI agency consultation and AI automation services are designed to help ambitious teams ship faster, scale smarter, and operate leaner.

The playbook is clear. The ROI is proven. The only question left is: will you act on it?