Cloud Finops

FinOps - Expert Guidance

> Built by OptimNow (James Barney) and Viktor Bezdek. > 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 & 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)

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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: viktorbezdek
• Source: viktorbezdek/skillstack
• License: MIT

Install and usage instructions live in the source repository linked above.