cloud-finops

name

cloud-finops

description

Expert Cloud FinOps guidance covering AI cost management, GenAI capacity planning, Anthropic billing, AWS (EC2, Bedrock, Savings Plans, CUR), Azure (reservations, OpenAI PTUs, Cost Management), GCP (Vertex AI, Compute Engine, BigQuery), tagging governance, SaaS management (SAM, license optimization, SMPs, shadow IT), Databricks, Snowflake, OCI, GreenOps, and FinOps framework implementation. Use for any query about cloud cost, AI workload economics, commitment discounts, rightsizing, cost allocation, SaaS sprawl, or connecting cloud spend to business value. Built by OptimNow.

Cloud 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 multiple cloud 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, CUR, Cost Explorer, EDP negotiation, RDS cost management	references/finops-aws.md
AWS Bedrock billing, Bedrock provisioned throughput, model unit pricing, Bedrock batch inference	references/finops-bedrock.md
Azure cost management, reservations, Azure Advisor, Cost Management, EA-to-MCA transition	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 optimization	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 optimization, Unity Catalog costs	references/finops-databricks.md
Snowflake warehouses, query optimization, storage, credits	references/finops-snowflake.md
OCI compute, storage, networking optimization	references/finops-oci.md
GreenOps, cloud carbon, sustainability, carbon-aware workloads	references/greenops-cloud-carbon.md
SaaS management, license optimization, shadow IT, SaaS sprawl, renewal governance, SMP, SAM	references/finops-sam.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 organization'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

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Core FinOps principles (always apply)

These six principles from the FinOps Foundation underpin every recommendation:

1. Teams need to collaborate
2. Business value drives technology decisions
3. Everyone takes ownership for their cloud 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

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The three phases (Inform → Optimize → Operate)

FinOps is an iterative cycle, not a linear progression. Organizations move through phases continuously as their cloud 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

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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
Optimization	One-off projects	Documented process	Self-executing policies

Always assess maturity before recommending solutions. A Crawl organization needs visibility before optimization. Recommending commitment discounts to a team with 40% cost allocation is premature - they will commit to waste.

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Reference files

File	Contents	Lines
optimnow-methodology.md	OptimNow reasoning philosophy, 4 pillars, engagement principles, tools	~150
finops-for-ai.md	AI cost management, LLM economics, agentic patterns, ROI framework	~400
finops-ai-value-management.md	AI investment governance: AI Investment Council, stage gates, incremental funding, practice operations, value metrics	~265
finops-genai-capacity.md	GenAI capacity models: provisioned vs shared, traffic shape, spillover, waste types, cross-provider comparison	~220
finops-aws.md	AWS FinOps + 128 optimization patterns: CUR, Cost Explorer, EC2, RIs, Savings Plans, EDP negotiation, RDS strategy, waste detection	~1730
finops-bedrock.md	AWS Bedrock billing: model pricing, provisioned throughput, batch inference, CloudWatch metrics, cost allocation	~200
finops-azure.md	Azure FinOps + 48 optimization patterns: reservations, Advisor, AHB, EA-to-MCA transition, waste detection	~1070
finops-azure-openai.md	Azure OpenAI Service: PTU reservations, spillover, GPT model pricing, prompt caching, fine-tuning costs	~260
finops-anthropic.md	Anthropic billing: Claude Opus/Sonnet/Haiku pricing, Fast mode, long-context cliffs, prompt caching, Batch API, governance	~175
finops-gcp.md	GCP optimization: 26 patterns across Compute Engine, Cloud SQL, GCS, networking	~260
finops-vertexai.md	GCP Vertex AI billing: Gemini pricing, provisioned throughput, batch prediction, Cloud Monitoring metrics	~215
finops-tagging.md	Tagging strategy, IaC enforcement, virtual tagging, MCP automation	~300
finops-framework.md	Full FinOps Foundation framework: 22 capabilities, personas, domains	~350
finops-databricks.md

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Cloud FinOps Skill by OptimNow - licensed under CC BY-SA 4.0.