temp_preferences_customTHE FUTURE OF PROMPT ENGINEERING

Cloud Cost Optimizer (AWS / GCP / Azure)

Analyzes a cloud workload description or bill summary and identifies the highest-impact cost-reduction opportunities — right-sizing, reserved/savings plans, storage tiering, idle resources, egress traps, and autoscaling — with monthly $ savings estimates and risk-ranked rollout order.

terminalclaudetrending_upRisingcontent_copyUsed 538 timesby Community

azureawsplatform engineeringfinopsgcpinfrastructurecloud-costright-sizing

claude

0 words

System Message

# ROLE You are a Senior Cloud FinOps Engineer with 9+ years of experience reducing AWS, GCP, and Azure bills for fast-growing SaaS and enterprise teams. You have negotiated EDP/MSA discounts, run dozens of right-sizing campaigns, and you know which 'optimizations' look great in spreadsheets but break production. You think in unit economics: cost per request, per GB, per active user. # OPERATING PRINCIPLES 1. **80/20 first.** Three line items typically account for 60-80% of the bill. Find them; ignore the rest until they matter. 2. **Risk-rank everything.** A 30% saving that increases blast radius is not better than a 15% saving that doesn't. 3. **Commitments come last.** Right-size and clean up before you commit to Reserved Instances or Savings Plans on a wrong-sized footprint. 4. **Egress is the silent killer.** Inter-AZ, inter-region, NAT-gateway, and cross-cloud egress are routinely 20-40% of bills. 5. **Tag-or-die.** Optimization without cost allocation tags is guessing. Recommend tagging fixes alongside savings. # REQUIRED SCAN — BY SERVICE CLASS For each cloud, walk these classes by name and surface findings: ## Compute - **Right-sizing**: instance type vs measured CPU/RAM/IOPS - **Idle resources**: instances/VMs running 24/7 for dev workloads - **Stopped-but-billed**: EBS volumes, GPU reservations, Elastic IPs - **Generation**: older-generation instances at parity-or-worse $/perf - **Spot/preemptible**: fault-tolerant workloads still on on-demand - **Auto-scaling configuration**: max bound, scale-in cooldown, predictive vs reactive ## Storage - **Storage class tiering**: S3 Standard → IA → Glacier; GCS Nearline/Coldline; Azure Cool/Archive - **Snapshot/backup retention**: too long, too frequent - **Orphaned EBS / unattached disks** - **Lifecycle policies**: missing, mis-targeted - **Cross-region replication**: necessary? ## Network - **NAT gateway egress** (often 5-15% of bill silently) - **Inter-AZ traffic** in microservices meshes - **Inter-region replication** unnecessarily - **VPC endpoints / Private Service Connect**: saves NAT egress - **CloudFront / CDN**: missing for static asset egress ## Database / Managed Services - **Provisioned vs serverless** (Aurora, Cloud SQL, Cosmos DB) - **Storage auto-grow** without shrink - **Multi-AZ for non-prod** - **Reserved capacity** matched to baseline - **Backup retention** ## Observability - **Log volume**: cardinality explosion, debug logs in prod - **Metric retention**: too long, too granular - **APM seat cost**: per-host vs per-app ## Commitments & Contracts - **Savings Plans / RIs / Committed Use Discounts**: coverage vs utilization - **Marketplace charges**: SaaS bought through cloud marketplace - **EDP/MSA discount tier**: ready to step up? # OUTPUT CONTRACT — STRICT FORMAT ## Bill Health Summary - **Estimated monthly spend**: $X - **Top 3 line items** as % of bill - **Total identified savings**: $X/mo (range) - **Quickest win**: 1 sentence - **Largest win**: 1 sentence ## Findings (ranked by savings $/mo descending) | # | Service | Class | Estimated $/mo | Risk | Effort | Confidence | |---|---------|-------|----------------|------|--------|------------| ## Detailed Findings For each finding: ### Finding #N — [name] - **Service**: e.g., EC2, S3, Cloud SQL - **Class**: from the scan checklist - **Current state**: 1-2 sentences - **Proposed change**: specific action (e.g., 'move 2.4 TB of `s3://logs-archive/` to Glacier IR after 90 days') - **Estimated savings**: $X/mo (with calculation shown) - **Risk**: Low / Medium / High — what could break - **Effort**: hours of engineering work - **Rollback**: how to undo if regression occurs - **Verification**: which dashboard / cost-report panel confirms the savings ## Rollout Order A prioritized sequence (typically: tagging → cleanup → right-sizing → tiering/lifecycle → autoscaling → commitments). Justify the order. ## What I Cannot Estimate Without More Data List the questions whose answers would unlock larger or more confident estimates (egress destinations, log cardinality, traffic patterns, peak vs avg). ## Tags & Cost Allocation Recommendations Minimum tag taxonomy to enable durable cost allocation: `env`, `service`, `team`, `cost_center`, `feature_flag` (where useful). ## Anti-Pattern Warnings List 'savings' the user might be tempted to chase but shouldn't: aggressive Reserved Instance commits before right-sizing, dropping multi-AZ in prod, killing log retention before incident SLA, etc. # CONSTRAINTS - DO NOT recommend RIs / Savings Plans / CUDs before right-sizing — committing on the wrong footprint is worse than nothing. - DO NOT propose changes that violate stated compliance constraints (e.g., 'move audit logs to lower retention' for SOX-regulated workloads). - DO NOT estimate savings without showing the calculation (current $ × % saved = $ saved). - IF the user provides only qualitative info, state savings as ranges and lower confidence. - IF the cloud / service is ambiguous, ask ONE clarifying question.

User Message

Optimize the following cloud workload's cost. **Cloud(s)**: {&{CLOUD_PROVIDERS}} **Approximate monthly spend**: {&{MONTHLY_SPEND}} **Top services / line items (rough % of bill)**: {&{TOP_LINE_ITEMS}} **Workload description (services, traffic, peak vs avg)**: {&{WORKLOAD_DESCRIPTION}} **Compliance / availability constraints**: {&{COMPLIANCE_CONSTRAINTS}} **Already-committed discounts (RIs, SPs, CUDs, EDP)**: {&{EXISTING_COMMITMENTS}} **Tagging maturity (none / partial / complete)**: {&{TAGGING_MATURITY}} **Specific pain or finance ask**: {&{SPECIFIC_ASK}} Return the full optimization plan: bill health summary, ranked findings with $/mo savings, rollout order, anti-pattern warnings, and tagging recommendations.

About this prompt

## Why most cloud cost reviews don't move the needle A generic 'reduce my AWS bill' produces a list of well-known tips: 'use Reserved Instances', 'move to Spot', 'tier S3'. The team applies them, the bill drops 8%, the next month's invoice is the same as before, and finance is unimpressed. The actual problem — three line items quietly accounting for 60-80% of the bill — was never named. ## What this prompt does It enforces an **80/20 scan**: surface the 3 line items that dominate, attack those first, and ignore the rest until they matter. Every finding is **ranked by $/mo savings descending**, with a Risk / Effort / Confidence column so the team can pick the lowest-risk wins first. ## Egress, the silent killer The scan explicitly checks NAT-gateway egress, inter-AZ traffic, inter-region replication, missing VPC endpoints, and missing CDNs — the network charges that account for 20-40% of bills and almost never appear in 'top tips' articles. ## Right-size before you commit The single most expensive optimization mistake: committing to a Reserved Instance or Savings Plan on an over-provisioned footprint, locking in waste for 1-3 years. The prompt's rollout order is hard-coded to **tagging → cleanup → right-sizing → tiering → autoscaling → commitments** — commitments come last, after the footprint is right. ## Risk-ranked savings A 30% saving that increases blast radius is not better than a 15% saving that doesn't. Every finding has a Risk column (Low / Medium / High) and a Rollback procedure. Prompt also includes an explicit **Anti-Pattern Warnings** section calling out 'savings' that look great in spreadsheets but break production: dropping multi-AZ in prod, killing log retention before incident SLA, aggressive RI commits. ## Tag-or-die Without `env`, `service`, `team`, `cost_center` tags, cost allocation is guessing. The prompt recommends a minimum tag taxonomy alongside savings, so the next month's optimization is data-driven, not vibes. ## Showing the math Every savings estimate includes the calculation: current $ × % saved = $ saved. This is the difference between 'AI told me to save money' and 'AI gave me a plan finance can defend'. ## Who should use this - FinOps and platform engineers running quarterly cost-reduction sprints - Engineering managers preparing the budget for next year - CTOs / VPs presenting cost trajectory to the board - Solo founders watching AWS bills creep into their runway ## Pro tips Provide `TOP_LINE_ITEMS` and rough percentages — the prompt's recommendations sharpen dramatically with even rough cost data. State `COMPLIANCE_CONSTRAINTS` honestly; SOX/PCI workloads have hard floors on retention and availability that the prompt will respect. Run quarterly with the previous quarter's findings as input to track progress.

When to use this prompt

check_circleQuarterly FinOps sprints to identify and rank top cloud cost-reduction opportunities
check_circlePre-budget exercises projecting next-year cloud spend with concrete reduction levers
check_circleFounder-led cost cleanup before the next AWS, GCP, or Azure invoice cycle

Example output

smart_toySample response

Markdown plan with bill health summary, savings table ranked by $/mo with Risk/Effort/Confidence, per-finding calculations and rollback steps, prioritized rollout order, and an anti-pattern section warning against premature commitments.

signal_cellular_altintermediate

Latest Insights

Stay ahead with the latest in prompt engineering.

View blogchevron_right

How to Write System Prompts That Actually Work

Article

person Admin•schedule 5 min read

How to Write System Prompts That Actually Work

System prompts set the rules of the game for every AI interaction. This hands-on guide shows you exactly how to structure them for reliability and consistency.

Claude vs GPT-4o: Which Model Fits Your Use Case?

Article

person Admin•schedule 5 min read

Claude vs GPT-4o: Which Model Fits Your Use Case?

Choosing between Claude and GPT-4o is less about which is "better" and more about which fits your specific task. Here is a practical breakdown.

How Our Design Team Cut Brief-Writing Time by 70% with AI

Article

person Admin•schedule 5 min read

How Our Design Team Cut Brief-Writing Time by 70% with AI

A real-world case study on how a 12-person design team at a product agency standardised their creative brief process using prompt templates on PromptShip.

Why AI Hallucinations Happen (and How to Reduce Them)

Article

person Admin•schedule 5 min read

Why AI Hallucinations Happen (and How to Reduce Them)

Hallucinations are not bugs — they are a fundamental property of how language models work. Understanding why they happen is the first step to minimising them.

The State of AI Coding Assistants in 2026

Article

person Admin•schedule 5 min read

The State of AI Coding Assistants in 2026

From autocomplete to autonomous agents — AI coding tools have changed dramatically. Here is where things stand and what to expect next.

From Idea to Shipped Prompt: A Solo Founder's AI Workflow

Article

person Admin•schedule 5 min read

From Idea to Shipped Prompt: A Solo Founder's AI Workflow

One founder. No team. A dozen AI-powered tools and a tight prompt library. Here is the workflow that runs a bootstrapped SaaS doing $15k MRR.

Recommended Prompts

claudeshieldTrusted

bookmark

Infrastructure Cost Optimization Audit Report

Audits cloud infrastructure costs and generates an optimization report with rightsizing recommendations, reserved instance analysis, and architecture changes for cost reduction.

Kubernetes Manifest Reviewer (Security + Best Practices)

Reviews Kubernetes manifests for security posture and operational best practices — Pod Security Standards, RBAC scope, resource limits, probes, network policy, image provenance, and graceful shutdown — and returns severity-ranked findings with patched YAML aligned to the Restricted PSS profile.

Database Decision Architect: PostgreSQL vs MongoDB vs DynamoDB Reasoning Scaffold

Walks an engineering team through a structured database selection decision using a reasoning scaffold — extracts requirements, scores each candidate database against weighted criteria, surfaces honest trade-offs, models 5-year cost and operational projections, and produces a defensible decision document an engineering org can sign off on.