'cost optimization' 검색 결과

Optimize cloud costs through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing cost governance policies.

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Plan and review paid advertising across Google Ads, Meta, LinkedIn, and other platforms. Organize audiences, budgets, and bidding, then assess results using customer acquisition costs and sales data while separating evidence, hypotheses, and missing information.

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• Choose advertising platforms and budget allocations
• Review campaign results and decide next steps
• Find wasted spend in search advertising

Solve optimization problems in Python with one or several competing goals. Define constraints, compare search algorithms, visualize trade-offs, and assess candidate solutions before choosing an option that fits your priorities.

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• Comparing designs with competing cost and performance goals
• Searching for solutions under explicit constraints
• Benchmarking optimization algorithms

Plan AI features for products, covering language model integration, RAG architecture that retrieves information for answers, prompt design, user experiences people can trust, and cost optimization. Use it to guide development decisions for production use.

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• Plan language model integration in a product
• Consider RAG architecture for reference-based answers
• Review prompt design and AI user experience

Guide the design of AI product features with output validation, responses displayed as they arrive, prompt versioning, and testing. Address API costs, failures, and unsafe input when preparing language model integrations for production.

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• Reviewing AI features before production release
• Designing validation for AI-generated output
• Managing prompt versions and regression tests

Plan Azure infrastructure around application needs, scale, and budget. Generate Bicep templates, files that define cloud resources as code, then review estimated costs, configure deployment pipelines, and check security settings before production.

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• Planning Azure infrastructure for a web app
• Choosing services for microservices or event-driven processing
• Generating Bicep templates and deployment pipelines

Improve a measurable target in a working system by tracing workload costs or scoring alternative implementations. Compare results against a baseline, keep confirmed improvements on a dedicated branch, and record experiments for review or resumption.

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• Investigating the cost of a specific workload
• Comparing implementations against a measurable target
• Running improvement experiments within time and spending limits

Assess Google Cloud workloads for environmental sustainability using the Well-Architected Framework. Get recommendations on lower-carbon regions, efficient computing and storage, AI energy use, and ongoing emissions measurement with Carbon Footprint.

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• Reviewing cloud architecture for environmental impact
• Comparing deployment regions by carbon-free energy use
• Reducing energy waste in AI and computing workloads

Design how an AI agent edits its own working conversation history, choosing what to retain, rewrite, or remove. Covers safeguards, capacity monitoring, recovery after overflow, and the computing cost of changing earlier context.

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• Designing agents that edit their own conversation history
• Preserving exact values during long tasks
• Recovering from context overflow

Plan projects that use large language models, from checking whether the task suits a model to designing processing stages. Estimate costs, choose single or multiple agents, and organize prototypes, structured outputs, and iteration.

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• Checking whether a task is suitable for an LLM
• Designing a pipeline for batch analysis
• Estimating processing costs before development

Train Mixture of Experts models, which activate selected specialist networks for each input. The skill guides architecture, input routing, workload balancing, and GPU distribution using DeepSpeed or HuggingFace, with examples for training configuration and inference optimization.

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• Designing a model that activates only selected expert networks
• Balancing workloads across expert networks
• Distributing experts across multiple GPUs

Analyze token usage and AI costs in GitHub Agentic Workflows, identify expensive steps and budget or timeout failures, and develop prioritized improvements checked against the original workflow’s output quality.

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• Investigating runs that exceed their AI budget
• Reducing costs in frequently scheduled workflows
• Reviewing workflows that hit time or turn limits

Optimize existing Azure resources and analyze Reservations or Savings Plans. WHEN: "optimize Azure costs", "reduce cloud spending", "rightsize resources", "find idle resources", "orphaned disk", "deleted VM still charged", "public IP still charging", "reservation utilization", "Savings Plan coverage", "commitment recommendation", "why is pay-as-you-go still charged". DO NOT USE FOR: cost spikes, forecasts, pricing estimates, budgets, or governance.

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Activate when developers have latent caching needs: slow API responses, database read bottlenecks, DynamoDB throttling or cost, RDS/Aurora scaling pressure, Bedrock latency or cost, or adding a cache; activate when working with Redis, Valkey, Memcached, or any in-memory data store, cache-aside patterns, session stores, rate limiting, leaderboards, counters, streams, queues, pub/sub, distributed locks, feature flags, shopping carts, or other caching strategies. Activate for GenAI and ML retrieval: vector similarity search for low-latency retrieval, semantic caching, RAG, LLM response caching, embedding stores, AI agent memory, recommendation, personalization. Activate for ElastiCache lifecycle: provisioning (serverless or node-based), engine selection, CloudFormation/CDK/Terraform IaC, VPC connectivity, TLS, RBAC, IAM auth, Global Datastore, monitoring, troubleshooting, cost optimization, and migration from self-managed Redis. Do not trigger for browser caches, CDN/CloudFront, HTTP Cache-Control, CPU caches.

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Analyze and reduce avoidable Attio integration cost and load by measuring request volume, query complexity, synchronization churn, and retained data before changing architecture. Use when an Attio workload is wasteful or operationally expensive. Trigger with "Attio cost", "reduce Attio API usage", or "Attio sync efficiency".

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Apple Notes cost optimization — it is free, focus on iCloud storage management. Trigger: "apple notes cost".

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Generator economic dispatch and cost optimization for power systems. Use when minimizing generation costs, computing optimal generator setpoints, calculating operating margins, or working with generator cost functions.

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Expert cloud architect specializing in AWS/Azure/GCP multi-cloud infrastructure design, advanced IaC (Terraform/OpenTofu/CDK), FinOps cost optimization, and modern architectural patterns. Masters serverless, microservices, security, compliance, and disaster recovery. Use PROACTIVELY for cloud architecture, cost optimization, migration planning, or multi-cloud strategies.

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Optimize existing Azure resources and analyze Reservations or Savings Plans. WHEN: "optimize Azure costs", "reduce cloud spending", "rightsize resources", "find idle resources", "orphaned disk", "deleted VM still charged", "public IP still charging", "reservation utilization", "Savings Plan coverage", "commitment recommendation", "why is pay-as-you-go still charged". DO NOT USE FOR: cost spikes, forecasts, pricing estimates, budgets, or governance.

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Supply Chain Bottleneck Analyzer for Shopify/DTC stores. Diagnose cash flow, inventory, shipping costs, and customer acquisition efficiency. Includes CAC/LTV analysis, 3PL cost optimization, and ad spend benchmarks. No API key required for basic analysis.

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E-commerce warehouse and inventory optimization advisor. Analyzes inventory health, calculates safety stock and reorder points, performs ABC analysis, evaluates fulfillment costs, and provides actionable recommendations for improving efficiency. Supports all major fulfillment models: Self-fulfillment, Amazon FBA/FBM, Walmart WFS, 3PL, Shopify Fulfillment, TikTok Shop, Dropshipping, and Hybrid setups. No API key required. Use when: (1) reducing stockouts or overstock, (2) calculating safety stock levels, (3) optimizing warehouse costs, (4) improving Amazon IPI score, (5) analyzing inventory KPIs.

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Reduce LLM API and infrastructure costs through model selection, prompt caching, batching, caching, quantization, and self-hosting strategies. Track spend by team and model, set budgets, and implement cost-aware routing.

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Suggest concrete optimizations for Claude Code usage based on historical session data. Covers cost reduction, speed improvement, error prevention, and workflow efficiency. Use for data-driven optimization planning.

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Optimize Kubernetes costs through resource right-sizing, unused resource detection, and cluster efficiency analysis. Use for cost optimization, resource analysis, and capacity planning.

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