cloud-finopslisted
Expert FinOps guidance covering cloud, AI, and SaaS technology spend. Includes AI cost management, GenAI capacity planning, self-hosted vs managed inference, Anthropic billing, AWS (EC2, Bedrock, SageMaker, GPU rightsizing, 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, data platforms (Databricks allocation and governance with DBCU commitments, Microsoft Fabric capacity FinOps with F-SKUs, CU smoothing, reservations, pause/resume, Pro-to-Fabric migration), Snowflake, OCI, and GreenOps (AWS Sustainability Console, CSRD). 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.
OptimNow/cloud-finops-skills · ★ 50 · AI & Automation · score 74
Install: claude install-skill OptimNow/cloud-finops-skills
# FinOps - Expert Guidance
> Built by OptimNow. Grounded in hands-on enterprise delivery, not abstract frameworks.
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## How to use this skill
This skill covers cloud, AI, SaaS, and adjacent technology spend domains. Apply the
OptimNow lens to every answer: diagnose before prescribing, connect cost to value,
recommend progressively. Then load the domain reference(s) matching the query. Load
`references/optimnow-methodology.md` in full only for strategy, engagement-design, or
methodology questions - not for routine billing-mechanics queries.
### Domain routing
| Query topic | Load reference |
|---|---|
| OptimNow methodology, engagement approach, four pillars, FinOps strategy design, practice positioning | `references/optimnow-methodology.md` |
| AI costs, LLM inference, token economics, agentic cost patterns, AI ROI, AI cost allocation, GPU cost attribution, GPU telemetry, DCGM metrics, "GPU utilization is misleading", tensor core activity, GPU memory bandwidth, RAG harness costs | `references/finops-for-ai.md` |
| Agentic FinOps, true agents vs pipelines vs workflows, agentic cost anatomy, cost per completed task, cost-safe agent architecture, agent-initiated payments, x402, MPP, agent wallets | `references/finops-agentic.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 s