cloud-finops

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.

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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, compute rightsizing, AKS optimisation, database optimisation (Azure SQL, Postgres/MySQL, Cosmos), Log Analytics cost control, backup and snapshot management, storage tiering and lifecycle, networking cost, tagging and Azure Policy governance, FOCUS exports, MACC, 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 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

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Core FinOps principles (always apply)

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

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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

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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
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.

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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, MACC, EA-to-MCA transition, Advisor rightsizing, AKS optimisation, database patterns, Log Analytics cost control, backup/snapshot management, storage tiering, networking cost, tagging and Azure Policy governance, FOCUS exports	~1500
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

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