Skip to main content

cost-optimization-patterns

Cloud cost optimization, token/API cost tracking, build time optimization, and FinOps patterns

Informations de source

Dépôt
vibeeval/vibecosystem
Dernière activité de la source
12 avril 2026 à 23:35
Langue détectée de SKILL.md
turc
Étoiles
538
Forks
47

Options d'installation

Le prompt qui vérifie d'abord la source est sélectionné par défaut. Vous pouvez passer à une commande directe ou télécharger une copie locale.

Vérifiez les fichiers source

Lisez SKILL.md et les fichiers associés affichés par SkillsMP avant de décider de l'installer.

Affichage de SKILL.md

SKILL.md
Instructions source · Aperçu en lecture seule
name
cost-optimization-patterns
description
Cloud cost optimization, token/API cost tracking, build time optimization, and FinOps patterns
# Cost Optimization Patterns ## Cloud Cost Optimization ### Right-Sizing ```bash # AWS - Compute Optimizer önerileri aws compute-optimizer get-ec2-instance-recommendations # CPU/Memory utilization < 20% → downsize # CPU/Memory utilization > 80% → upsize veya auto-scale ``` ### Instance Strategy | Strateji | Tasarruf | Risk | Use Case | |----------|---------|------|----------| | On-Demand | 0% | Düşük | Değişken workload | | Reserved (1yr) | %30-40 | Orta | Steady-state | | Reserved (3yr) | %50-60 | Yüksek | Uzun vadeli | | Spot/Preemptible | %60-90 | Yüksek | Batch, CI/CD | | Savings Plans | %30-50 | Düşük | Flexible commitment | ### Serverless Cost ``` Lambda cost = (requests × $0.20/1M) + (GB-seconds × $0.0000166) Optimizasyon: 1. Memory right-sizing (power tuning) 2. Provisioned concurrency (cold start vs cost tradeoff) 3. ARM architecture (%20 ucuz) 4. Batch processing (SQS batch size) ``` ## LLM/API Token Cost ```typescript // Token maliyet takibi const MODEL_COSTS = { 'claude-opus-4-6': { input: 15.0, output: 75.0 }, // per 1M tokens 'claude-sonnet-4-6': { input: 3.0, output: 15.0 }, 'claude-haiku-4-5': { input: 0.80, output: 4.0 } } as const function estimateCost(model: string, inputTokens: number, outputTokens: number) { const costs = MODEL_COSTS[model] return (inputTokens * costs.input + outputTokens * costs.output) / 1_000_000 } ``` ## Build Time Optimization | Teknik | Tasarruf | Karmaşıklık | |--------|---------|-------------| | Dependency caching | %30-50 | Düşük | | Parallel test execution | %40-60 | Düşük | | Incremental builds | %50-70 | Orta | | Docker layer caching | %20-40 | Düşük | | Affected-only (monorepo) | %60-80 | Orta | ## Database Cost ```sql -- Pahalı query'leri bul SELECT query, calls, mean_exec_time, total_exec_time FROM pg_stat_statements ORDER BY total_exec_time DESC LIMIT 20; -- Unused index'leri bul (gereksiz storage + write overhead) SELECT indexrelname, idx_scan FROM pg_stat_user_indexes WHERE idx_scan = 0 AND indexrelname NOT LIKE '%pkey%'; ``` ## Checklist - [ ] Cloud cost alerting aktif (budget alarm) - [ ] Unused resources temizlenmiş - [ ] Right-sizing review (aylık) - [ ] Reserved/Spot instance kullanımı - [ ] CDN cache hit ratio >90% - [ ] Database query optimization - [ ] Build cache aktif - [ ] Tag-based cost allocation ## Anti-Patterns - Over-provisioned "just in case" resources - Unused EBS volumes, idle load balancers - Data transfer between regions (pahalı) - CloudWatch log retention unlimited - Her environment production-grade
Voir sur GitHub