cloud-finopslisted
Expert FinOps guidance for cloud, AI, and SaaS technology spend. Covers AI cost management, agentic FinOps, GenAI capacity planning, self-hosted vs managed inference, Anthropic billing, open-weight vendor APIs (DeepSeek, Qwen, Kimi, GLM), AWS (EC2, Bedrock, SageMaker, Savings Plans, CUR), Azure (reservations, AHB, OpenAI PTUs), GCP (Vertex AI, BigQuery), OCI, Kubernetes cost allocation, data platforms (Databricks, Fabric F-SKUs, Snowflake), tagging governance, SaaS management (SAM, shadow IT), AI coding tools (Cursor, Claude Code, Copilot, Codex), ITAM, allocation, showback, chargeback, anomaly management, KPIs and benchmarking, waste detection playbooks, workload onboarding and M&A, and GreenOps (cloud carbon, CSRD). Use for any question about technology cost or waste in your estate - expiring commitments or RIs, NAT gateway bills, idle VMs, snapshot sprawl - and for commitment sizing, rightsizing, allocation, SaaS sprawl, AI dev tool spend, or connecting spend to business value. Built by OptimNow.
OptimNow/cloud-finops-skills · ★ 55 · DevOps & Infrastructure · score 73
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, AI business case, quantifying AI business value, cost displacement vs revenue uplift vs retention vs premium monet