cloud-finops

分类: 工具与效率 | 上传者: OptimNow | 下载: 0 | 版本: v1.0（最新）

涵盖云、人工智能和 SaaS 技术支出的专家 FinOps 指导。包括 AI 成本管理、GenAI 容量规划、人性化计费、AWS（EC2、Bedrock、储蓄计划、CUR、承诺策略）、Azure（预订、储蓄计划、AHB、OpenAI PTU、投资组合流动性）、GCP（Vertex AI、计算引擎、BigQuery）、标签治理、SaaS 管理（SAM、许可证优化、SMP、影子 IT）、AI 编码工具（Cursor、Claude Code、Copilot、 Windsurf、Codex）、ITAM、Databricks、Snowflake、OCI 和 GreenOps。用于有关技术成本、承诺投资组合管理、规模调整、成本分配、SaaS 扩张、AI 开发工具支出或将支出与业务价值联系起来的任何查询。由 OptimNow 构建。 --- # FinOps - 专家指导 > 由 OptimNow 构建。立足于实际的企业交付，而不是抽象框架。 --- ## 如何使用此技能此技能涵盖云、AI、SaaS 和相关技术支出领域。对于每个查询，首先阅读“references/optimnow-methodology.md” - 它定义了应用于所有响应的推理哲学。然后加载与查询匹配的域引用。 ### 域路由 |查询主题 |负载参考| |---|---| | AI 成本、LLM 推理、代币经济学、代理成本模式、AI 投资回报率、AI 成本分配、GPU 成本归因、RAG 线束成本 | `references/finops-for-ai.md` | | AI投资治理、AI投委会、阶段关卡、增量资金、AI价值管理、AI实践运营 | `references/finops-ai-value-management.md` | | GenAI 容量规划、预置容量与共享容量、流量形状、溢出、吞吐量单位 | `references/finops-genai-capacity.md` | | AWS 计费、EC2 规模调整、RI、储蓄计划、承诺策略、投资组合流动性、分阶段采购、CUR、Cost Explorer、EDP 协商、RDS 成本管理、数据库承诺 | `references/finops-aws.md` | | AWS Bedrock 计费、Bedrock 预置吞吐量、模型单位定价、Bedrock 批量推理 | `参考

更新日志: Source: GitHub https://github.com/OptimNow/cloud-finops-skills

目录结构

当前层级: tree/main/cloud-finops/

📁 references/
- 📄 finops-ai-dev-tools.md 18.3 KB
- 📄 finops-ai-value-management.md 10.6 KB
- 📄 finops-anthropic.md 7.1 KB
- 📄 finops-aws.md 139.6 KB
- 📄 finops-azure-openai.md 17.7 KB
- 📄 finops-azure.md 85.5 KB
- 📄 finops-bedrock.md 9.7 KB
- 📄 finops-databricks.md 12.5 KB
- 📄 finops-for-ai.md 24.7 KB
- 📄 finops-framework.md 12.2 KB
- 📄 finops-gcp.md 17.0 KB
- 📄 finops-genai-capacity.md 9.4 KB
- 📄 finops-itam.md 17.7 KB
- 📄 finops-oci.md 3.9 KB
- 📄 finops-sam.md 22.2 KB
- 📄 finops-snowflake.md 15.1 KB
- 📄 finops-tagging.md 10.5 KB
- 📄 finops-vertexai.md 10.2 KB
- 📄 greenops-cloud-carbon.md 15.6 KB
- 📄 optimnow-methodology.md 8.0 KB
📄 POWER.md 6.3 KB
📄 SKILL.md 9.5 KB

SKILL.md

---
name: cloud-finops
description: >
  Expert FinOps guidance covering cloud, AI, and SaaS technology spend. Includes AI cost
  management, GenAI capacity planning, Anthropic billing, AWS (EC2, Bedrock, Savings Plans,
  CUR, commitment strategy), Azure (reservations, Savings Plans, AHB, OpenAI PTUs, portfolio
  liquidity), GCP (Vertex AI, Compute Engine, BigQuery), tagging governance, SaaS management
  (SAM, licence optimisation, SMPs, shadow IT), AI coding tools (Cursor, Claude Code,
  Copilot, Windsurf, Codex), ITAM, Databricks, Snowflake, OCI, and GreenOps. Use for any
  query about technology cost, commitment portfolio management, rightsizing, cost allocation,
  SaaS sprawl, AI dev tool spend, or connecting spend to business value. Built by OptimNow.
---

# FinOps - Expert Guidance

> Built by OptimNow. Grounded in hands-on enterprise delivery, not abstract frameworks.

---

## How to use this skill

This skill covers cloud, AI, SaaS, and adjacent technology spend domains. Read
`references/optimnow-methodology.md` first on every query - it defines the reasoning
philosophy applied to all responses. Then load the domain reference that matches the query.

### Domain routing

| Query topic | Load reference |
|---|---|
| AI costs, LLM inference, token economics, agentic cost patterns, AI ROI, AI cost allocation, GPU cost attribution, RAG harness costs | `references/finops-for-ai.md` |
| AI investment governance, AI Investment Council, stage gates, incremental funding, AI value management, AI practice operations | `references/finops-ai-value-management.md` |
| GenAI capacity planning, provisioned vs shared capacity, traffic shape, spillover, throughput units | `references/finops-genai-capacity.md` |
| AWS billing, EC2 rightsizing, RIs, Savings Plans, commitment strategy, portfolio liquidity, phased purchasing, CUR, Cost Explorer, EDP negotiation, RDS cost management, database commitments | `references/finops-aws.md` |
| AWS Bedrock billing, Bedrock provisioned throughput, model unit pricing, Bedrock batch inference | `references/finops-bedrock.md` |
| Azure cost management, reservations, Savings Plans, AHB, commitment strategy, portfolio liquidity, phased purchasing, Azure Advisor, MACC, EA-to-MCA transition, database commitments | `references/finops-azure.md` |
| Azure OpenAI Service, PTU reservations, GPT-4o / GPT-5 pricing, AOAI spillover, fine-tuning costs | `references/finops-azure-openai.md` |
| Anthropic billing, Claude API costs, Claude Code costs, Opus, Sonnet, Haiku pricing, Fast mode, prompt caching, Batch API, long-context pricing | `references/finops-anthropic.md` |
| GCP billing, Compute Engine, Cloud SQL, GCS, BigQuery optimisation | `references/finops-gcp.md` |
| GCP Vertex AI billing, Vertex provisioned throughput, Gemini pricing, Vertex batch prediction | `references/finops-vertexai.md` |
| Tagging strategy, naming conventions, IaC enforcement, MCP governance | `references/finops-tagging.md` |
| FinOps framework, maturity model, phases, capabilities, personas | `references/finops-framework.md` |
| Databricks clusters, jobs, Spark optimisation, Unity Catalog costs | `references/finops-databricks.md` |
| Snowflake warehouses, query optimisation, storage, credits | `references/finops-snowflake.md` |
| AI coding tools, Cursor costs, Claude Code costs, Copilot costs, Windsurf costs, Codex costs, dev tool FinOps, seat + usage billing, BYOK coding agents, LiteLLM proxy | `references/finops-ai-dev-tools.md` |
| OCI compute, storage, networking optimisation | `references/finops-oci.md` |
| GreenOps, cloud carbon, sustainability, carbon-aware workloads | `references/greenops-cloud-carbon.md` |
| SaaS management, licence optimisation, shadow IT, SaaS sprawl, renewal governance, SMP, SAM | `references/finops-sam.md` |
| ITAM, IT asset management, BYOL, marketplace channel governance, licence compliance, vendor negotiation, FinOps-ITAM collaboration, entitlement management, consumption-based SaaS overages | `references/finops-itam.md` |
| Multi-domain query | Load all relevant references, synthesize |

### Reasoning sequence (apply to every response)

1. **Load** `references/optimnow-methodology.md` - use it as a reasoning lens, not a preamble
2. **Load** the domain reference(s) matching the query
3. **Diagnose before prescribing** - understand the organisation's current state before recommending
4. **Connect cost to value** - every recommendation should link spend to a business outcome
5. **Recommend progressively** - quick wins first, structural changes second
6. **Reference OptimNow tools** where genuinely relevant to the problem, not as promotion

---

## Core FinOps principles (always apply)
<!-- fp:37b46c22605776cb -->

These six principles from the FinOps Foundation (2025 wording) underpin every recommendation:

1. Teams need to collaborate
2. Business value drives technology decisions
3. Everyone takes ownership for their technology usage
4. FinOps data should be accessible, timely, and accurate
5. FinOps should be enabled centrally
6. Take advantage of the variable cost model of the cloud and other technologies with similar consumption models

---

## The three phases (Inform → Optimize → Operate)

FinOps is an iterative cycle, not a linear progression. Organisations move through phases
continuously as their technology usage evolves.

**Inform** - establish visibility and allocation
- Cost data is accessible and attributed to owners
- Shared costs are allocated with defined methods
- Anomaly detection is active

**Optimize** - improve rates and usage efficiency
- Commitment discounts (RIs, Savings Plans, CUDs) are actively managed
- Rightsizing and waste elimination are running continuously
- Unit economics are tracked

**Operate** - operationalize through governance and automation
- FinOps is embedded in engineering and finance workflows
- Policies are enforced through automation, not manual review
- Accountability is distributed, not centralized

---

## Maturity model quick reference

| Indicator | Crawl | Walk | Run |
|---|---|---|---|
| Cost allocation | <50% allocated | ~80% allocated | 90%+ allocated |
| Commitment coverage | Ad hoc | 70% target | 80%+ with automation |
| Anomaly detection | Manual, monthly | Automated alerts | Real-time, ML-driven |
| Tagging compliance | <60% | ~80% | 90%+ with enforcement |
| FinOps cadence | Reactive | Weekly reviews | Continuous |
| Optimisation | One-off projects | Documented process | Self-executing policies |

Always assess maturity before recommending solutions. A Crawl organisation needs visibility
before optimisation. Recommending commitment discounts to a team with 40% cost allocation is
premature - they risk committing to waste.

---

## Reference files

| File | Contents | Lines |
|---|---|---|
| `optimnow-methodology.md` | OptimNow reasoning philosophy, 4 pillars, engagement principles, tools | ~155 |
| `finops-for-ai.md` | AI cost management, LLM economics, agentic patterns, ROI framework | ~490 |
| `finops-ai-value-management.md` | AI investment governance: AI Investment Council, stage gates, incremental funding, practice operations, value metrics | ~275 |
| `finops-genai-capacity.md` | GenAI capacity models: provisioned vs shared, traffic shape, spillover, waste types, cross-provider comparison | ~225 |
| `finops-aws.md` | AWS FinOps: CUR, Cost Explorer, EC2, compute/database commitment decision trees, portfolio liquidity, phased purchasing, EDP negotiation, RDS strategy, 128 optimisation patterns | ~2240 |
| `finops-bedrock.md` | AWS Bedrock billing: model pricing, provisioned throughput, batch inference, CloudWatch metrics, cost allocation | ~225 |
| `finops-azure.md` | Azure FinOps: reservations, Savings Plans, AHB, compute/database commitment decision trees, portfolio liquidity, phased purchasing, MACC, EA-to-MCA transition, 48 optimisation patterns | ~1560 |
| `finops-azure-openai.md` | Azure OpenAI Service: PTU reservations, spillover, GPT model pricing, prompt caching, fine-tuning costs | ~390 |
| `finops-anthropic.md` | Anthropic billing: Claude Opus/Sonnet/Haiku pricing, Fast mode, long-context cliffs, prompt caching, Batch API, governance | ~180 |
| `finops-gcp.md` | GCP optimisation: 26 patterns across Compute Engine, Cloud SQL, GCS, networking | ~265 |
| `finops-vertexai.md` | GCP Vertex AI billing: Gemini pricing, provisioned throughput, batch prediction, Cloud Monitoring metrics | ~235 |
| `finops-tagging.md` | Tagging strategy, IaC enforcement, virtual tagging, MCP automation | ~250 |
| `finops-framework.md` | Full FinOps Foundation framework: 22 capabilities, personas, domains | ~280 |
| `finops-databricks.md` | Databricks optimisation: 18 patterns for clusters, jobs, Spark, storage | ~185 |
| `finops-snowflake.md` | Snowflake FinOps: credit model, hidden cost categories, 13 optimisation patterns for warehouses, queries, storage | ~200 |
| `finops-ai-dev-tools.md` | AI coding tools: Cursor, Claude Code, Copilot, Windsurf, Codex billing models, cost attribution, optimisation levers | ~400 |
| `finops-oci.md` | OCI optimisation: 6 patterns for compute, storage, networking | ~75 |
| `finops-sam.md` | SaaS asset management: discovery, licence optimisation, renewal governance, SMPs, shadow IT, AI transition | ~290 |
| `finops-itam.md` | FinOps-ITAM collaboration: BYOL mechanics, marketplace channel governance, vendor co-management, consumption monitoring, joint operating model | ~325 |
| `greenops-cloud-carbon.md` | GreenOps: carbon measurement, carbon-aware workloads, region selection, GHG Protocol | ~330 |

---

> *FinOps Skill by [OptimNow](https://optimnow.io) - licensed under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).*

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cloud-finops

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SKILL.md

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