cloud-finops

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Atualizado27 de abril de 2026 às 10:12

Expert FinOps guidance covering cloud, AI, SaaS, and adjacent technology spend. Includes AI cost management, GenAI capacity planning, AI-powered FinOps automation, 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), Kubernetes and container FinOps (OpenCost, Kubecost), serverless FinOps (Lambda, Functions, Cloud Run), data platforms (Kafka/MSK, Elasticsearch/OpenSearch, Redis/Valkey), multi-cloud normalization (FOCUS specification), 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, container cost attribution, serverless optimization, multi-cloud strategy, or connecting spend to business va

Instalação

Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.

Fonte

viktorbezdek

viktorbezdek/skillstack

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Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Analistas financeiros e de investimentosNegócios e Operações Financeiras·SOC 13-2051

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name

cloud-finops

description

FinOps - Expert Guidance

Built by OptimNow (James Barney) and Viktor Bezdek. 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 & GenAI
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`
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`
AI-powered FinOps, FinOps automation, agentic FinOps tools, anomaly detection with AI, natural language cost querying	`references/finops-ai-automation.md`
Cloud Providers
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`
OCI compute, storage, networking optimisation	`references/finops-oci.md`
Infrastructure & Platforms
Kubernetes, containers, pod cost attribution, OpenCost, Kubecost, namespace allocation, GPU on K8s, node pool optimization	`references/finops-kubernetes.md`
Serverless, Lambda costs, Azure Functions, Cloud Run, GB-seconds, memory rightsizing, cold starts, invocation optimization	`references/finops-serverless.md`
Kafka, MSK, Elasticsearch, OpenSearch, Redis, Valkey, event streaming costs, search cluster costs, in-memory data store costs	`references/finops-data-platforms.md`
Databricks clusters, jobs, Spark optimisation, Unity Catalog costs	`references/finops-databricks.md`
Snowflake warehouses, query optimisation, storage, credits	`references/finops-snowflake.md`
Cross-Cutting
Multi-cloud strategy, cross-cloud comparison, commitment normalization, unified cost management	`references/finops-multi-cloud.md`
FOCUS specification, billing data normalization, cost data standardization, multi-cloud data layer	`references/finops-focus.md`
Tagging strategy, naming conventions, IaC enforcement, MCP governance	`references/finops-tagging.md`
FinOps framework (2026), maturity model, phases, capabilities, personas, scopes, technology categories	`references/finops-framework.md`
GreenOps, cloud carbon, sustainability, carbon-aware workloads	`references/greenops-cloud-carbon.md`
SaaS & Licensing
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)

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

Core FinOps principles (always apply)

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

Teams need to collaborate
Business value drives technology decisions
Everyone takes ownership for their technology usage
FinOps data should be accessible, timely, and accurate
FinOps should be enabled centrally
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
Methodology
`optimnow-methodology.md`	OptimNow reasoning philosophy, 4 pillars, engagement principles, tools	~155
`finops-framework.md`	Full FinOps Foundation framework (2026): capabilities, personas, domains, scopes, technology categories	~360
AI & GenAI
`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-ai-dev-tools.md`	AI coding tools: Cursor, Claude Code, Copilot, Windsurf, Codex billing models, cost attribution, optimisation levers	~400
`finops-ai-automation.md`	AI-powered FinOps: anomaly detection, automated rightsizing, NL cost querying, AI FinOps tool landscape, guardrails	~230
Cloud Providers
`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-oci.md`	OCI optimisation: 6 patterns for compute, storage, networking	~75
Infrastructure & Platforms
`finops-kubernetes.md`	Kubernetes/container FinOps: cost model, OpenCost, Kubecost, attribution patterns, pod rightsizing, GPU optimization	~400
`finops-serverless.md`	Serverless FinOps: Lambda/Functions/Cloud Run billing, memory rightsizing, cold starts, hidden costs, ARM migration	~230
`finops-data-platforms.md`	Data platform FinOps: Kafka/MSK cross-AZ costs, Elasticsearch/OpenSearch tiering, Redis-to-Valkey migration	~190
`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
Cross-Cutting
`finops-multi-cloud.md`	Multi-cloud FinOps: terminology normalization, cross-cloud commitment strategy, unified cost allocation, platform comparison	~285
`finops-focus.md`	FOCUS specification (v1.3): billing data normalization, core columns, provider support matrix, adoption guidance	~260
`finops-tagging.md`	Tagging strategy, IaC enforcement, virtual tagging, MCP automation	~250
`greenops-cloud-carbon.md`	GreenOps: carbon measurement, carbon-aware workloads, region selection, GHG Protocol	~330
SaaS & Licensing
`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

Anti-Patterns

Anti-Pattern	Problem	Solution
Optimizing before allocating	Committing to discounts on unattributed spend	Get to 80%+ allocation before buying commitments
Chasing unit savings over coverage gaps	Saving $0.02/hour on 10 instances while 200 run on-demand	Prioritize commitment coverage over per-unit optimization
Ignoring spillover costs	Provisioned capacity with unchecked spillover to pay-per-token	Model total cost including spillover; set alerts
Tagging as afterthought	<60% of resources tagged, can't attribute spend	Enforce tagging via IaC; block untagged deployments
Annual commitment on new workloads	Committing before usage patterns stabilize	Start with pay-per-use; commit after 3 months of data
Single-cloud cost view	Multi-cloud spend unnormalized	Adopt FOCUS spec for cross-cloud normalization
Ignoring SaaS sprawl	Shadow IT SaaS spend exceeds infrastructure	Implement SMP; discover and rationalize SaaS portfolio
FinOps as finance-only	Engineering excluded from cost decisions	Embed FinOps in engineering workflows; distribute accountability

FinOps Skill by OptimNow (James Barney) and Viktor Bezdek - licensed under CC BY-SA 4.0.