FinOps - Expert Guidance
Built by OptimNow from hands-on enterprise FinOps delivery.
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 per outcome (CPO), 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 monetisation, realisation rate, ROI sensitivity analysis, payback, break-even volume, total cost of AI (TCA), labour claims (capacity gain vs augmentation vs automation), booking AI value (spend removed vs spend avoided, capital freed), cost per outcome (CPO) | references/finops-ai-value-management.md |
| GenAI capacity planning, provisioned vs shared capacity, traffic shape, spillover, throughput units | references/finops-genai-capacity.md |
| Self-hosted vs managed AI inference, build vs buy LLM, vLLM, SGLang, llama.cpp, GPU rental, RunPod, CoreWeave, Lambda, GPU / inference vendor selection, hidden cost surface, ML-Ops maturity rubric, hybrid routing (LiteLLM, Portkey) | references/finops-ai-self-hosted-vs-managed.md |
| Open-weight model vendors on their own hosted APIs, DeepSeek, Qwen, Kimi, Moonshot, GLM, Z.ai, Chinese model APIs, open-weight pricing, time-based pricing, peak and off-peak token rates, vendor API vs third-party host channel choice, open-weight model licensing, GLM Coding Plan | references/finops-open-weight-vendors.md |
| AWS usage optimisation: EC2, container, Lambda and GPU rightsizing, Compute Optimizer, SageMaker operational FinOps, database cost drivers, CloudFront flat-rate plans, S3 Files | references/finops-aws.md |
| AWS billing data, CUR, Data Exports for FOCUS 1.2, Cost Explorer, Cost Anomaly Detection, cost allocation, Cost Categories, multi-org billing, AWS billing hierarchy, separate invoices per BU, Invoice Configuration, invoice units, Billing Conductor, pro forma billing, Cost Categories vs invoice, expensive IAM actions, cost-preventive SCPs, deny high-cost actions, sandbox account guardrails, AWS Budgets, AWS spend limits, AWS Settings projects, hard spend cap, early cost controls, AWS FinOps Agent, Extended Support audits | references/finops-aws-billing.md |
| AWS Savings Plans, Reserved Instances, Spot, commitment decision tree, commitment portfolio liquidity, phased purchasing, EDP discount order, Convertible RI exchange | references/finops-aws-commitments.md |
| AWS per-service inefficiency catalogue: compute patterns (EC2, Lambda, EKS, ECS / Fargate, EMR, Athena, AppStream, WorkSpaces) | references/finops-aws-patterns-compute.md |
| AWS storage inefficiency patterns (S3, EBS, EFS, snapshots, AWS Backup) | references/finops-aws-patterns-storage.md |
| AWS database inefficiency patterns (RDS, Aurora, DynamoDB, ElastiCache, OpenSearch), RDS cost strategy, database commitment decision tree (Database Savings Plan, RDS and Redshift reservations) | references/finops-aws-patterns-databases.md |
| AWS networking inefficiency patterns (NAT gateways, VPC endpoints, load balancers, cross-AZ transfer, CloudFront, Elastic IPs) | references/finops-aws-patterns-networking.md |
| AWS logging and governance-service patterns (CloudWatch Logs, AWS Config, CloudTrail, EventBridge), EDP double counting, Marketplace spend | references/finops-aws-patterns-other.md |
| AWS Bedrock billing, Bedrock provisioned throughput, model unit pricing, Bedrock batch inference, Application Inference Profiles, Bedrock product attributes in Cost Explorer (model, provider, inference type), Bedrock model attributes in CUR 2.0, Bedrock Projects, prompt caching, IAM Principal Cost Allocation | references/finops-bedrock.md |
| Azure usage optimisation: compute rightsizing and Advisor calibration, Log Analytics cost control, backup and snapshots, AKS, Azure SQL / Cosmos DB, storage tiering, networking cost | references/finops-azure.md |
| Azure billing data: Cost Management exports, FOCUS exports, Retail Prices API, FinOps Toolkit and hubs, MCA billing scopes, cost allocation, tagging and Azure Policy, budgets, EA-to-MCA transition, Azure MCP servers for cost data | references/finops-azure-billing.md |
| Azure Reservations, Savings Plans, Azure Hybrid Benefit, AHB, Spot VMs, compute and database commitment decision trees, commitment portfolio liquidity, 1 February 2027 exchange retirement, phased purchasing, MACC, storage reserved capacity, blob reservation, ADLS Gen2 reservation, Azure Files reservation, Premium SSD disk reservation, database reservation sizing, SQL serverless not reservable, Cosmos DB reserved capacity, Savings Plan for Databases, MACC net discount, ACD, break-even utilisation, reservation recommendations API | references/finops-azure-commitments.md |
| Azure per-service inefficiency catalogue, enumerated Azure optimisation patterns | references/finops-azure-patterns.md |
| Azure OpenAI Service, Azure AI Foundry, PTU reservations, locality constraint, 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, Managed Agents | references/finops-anthropic.md |
| GCP billing, Compute Engine, Cloud SQL, GCS, BigQuery billing export, BigQuery optimisation, FOCUS export, Sustained Use Discounts, SUDs, Committed Use Discounts, CUDs, Flexible CUDs, Flexible Savings Plans, Spot VMs, Cloud Carbon Footprint | references/finops-gcp.md |
| GCP Vertex AI billing, Gemini Enterprise Agent Platform, Vertex provisioned throughput, Gemini pricing, AI Studio, Gemini Developer API, Flexible Savings Plans, FSP, Priority / Flex tiers, Vertex batch prediction, default PAYG spillover | references/finops-vertexai.md |
| Tagging strategy, naming conventions, IaC enforcement, MCP governance | references/finops-tagging.md |
| FinOps framework 2026, 4 domains, 22 capabilities including Executive Strategy Alignment, Usage Optimization, Architecting & Workload Placement, Sustainability, KPIs & Benchmarking, Governance Policy & Risk, Automation Tools & Services, maturity model, phases, personas, shift-left FinOps, prevention rate, shift-left enforcement ladder | references/finops-framework.md |
| Databricks clusters, jobs, Spark optimisation, Unity Catalog costs, allocation and governance, DBU executor attribution, DBCU commitments, Photon multiplier, serverless premium, amortised vs PAYG split, Azure VM RI vs DBU clarification | references/finops-databricks.md |
| Microsoft Fabric capacity FinOps, F-SKUs, Capacity Units, CU smoothing window, throttling, pause/resume, Fabric reservation (CU-based, 1 or 3 years), pause vs reservation, Pro/PPU to Fabric migration governance, Capacity Metrics app, shared-capacity allocation | references/finops-fabric.md |
| Snowflake warehouses, query optimisation, storage, credits, QUERY_ATTRIBUTION_HISTORY, Budgets, Cortex governance, Cortex AI usage views, CORTEX_AISQL_USAGE_HISTORY, Cortex Agents orchestration budget, Cortex Analyst per-message billing, resource monitor scope | 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, Cost Reports, FOCUS Reports, cost-tracking tags, Budgets, Universal Credits | 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 |
| Cloud contract negotiation, enterprise agreement, EDP, PPA, Private Pricing, MACC, ACD, EA or MCA-E renewal, Google Cloud commitment, Minimum Commitment, Marketplace drawdown, migration credits (MAP, Azure Accelerate, RaMP), term sheet review, shortfall exposure | references/finops-cloud-contracts.md |
| Cost anomaly management, anomaly detection, masked anomalies, layered detection, threshold tuning, AWS Cost Anomaly Detection config, Azure anomaly detection, GCP Cost Anomaly Detection, new-region detection, security integration | references/finops-anomaly-management.md |
| FinOps KPIs, KPI portfolio by maturity, unit economics denominators, cost per customer / order / transaction, realised vs potential savings reporting, forecast variance, benchmarking (internal and external, data-quality caveats), executive reporting, CFO / CIO narrative, executive strategy alignment, maturity scorecard | references/finops-kpis-benchmarking.md |
| FinOps forecasting, forecast vs budget vs estimate, Forecast Accuracy Rate, Forecast Drift Rate, forecast variance bands by maturity, trend-based vs driver-based forecasts, rolling forecast, re-forecast cadence, living forecast, AI forecast scenarios (base, expected, aggressive), forecasting on amortised cost, forecast miss classification | references/finops-forecasting.md |
| Cost allocation methodology, showback, EffectiveCost vs BilledCost (FOCUS), amortised vs unblended (AWS legacy), blended-cost trap, defensible allocation keys, shared-services allocation (network, observability, security, ingress), InvoiceId reconciliation, unallocated spend signal, showback report design and routing | references/finops-allocation-showback.md |
| Chargeback, soft chargeback, hard chargeback, financial accountability, Finance and accounting prerequisites for chargeback, ERP readiness (SAP CO, Oracle, Workday, NetSuite), inter-BU P&L impact, CFO sponsorship, transfer pricing for intercompany cloud recharge, cross-border tax (VAT, withholding, permanent establishment, Pillar 2, GILTI / FDII / BEAT), SOX-equivalent controls, separate provider invoices per business unit, invoice units vs internal recharge, methodology dispute process, chargeback-revolt anti-pattern | references/finops-chargeback.md |
| FinOps practice operations, FinOps operating model, FinOps team structure (central, virtual, hub and spoke, distributed), FinOps champions, executive sponsor, RACI / responsibility matrix, steering committee, governance forums and meeting cadence, decision rights and escalation, how FinOps works with Engineering, Product, Finance, Procurement and Leadership, FinOps and ITFM / TBM, intersecting disciplines, allied personas, savings recognition handoff, chargeback design vs execution, pre-close forecast handoff, variance decomposition (price vs volume), shared identifiers / join keys with ITFM, AI cost classification at contract time | references/finops-practice-operations.md |
| Onboarding workloads, migration-time cost hygiene, intake gate, mandatory tags at go-live, 60-90 day forecast-then-commit rule, double-bubble cost (parallel-run source and target), migration cost estimate vs actuals, network-cost trap (data-centre to cloud), M&A integration patterns, divestiture and corporate separation (disposition tags, TSA costs, commitments that cannot be divided, optimisation debt), FOCUS-during-migration, architecture review integration, post-migration FinOps owner | references/finops-onboarding-workloads.md |
| Kubernetes FinOps, K8s cost allocation, OpenCost, Kubecost, GKE Cost Allocation, EKS Split Cost Allocation, AKS Cost Analysis, FOCUS-emitting K8s allocation, container rightsizing (VPA, p95/p99 with safety margins), node-level autoscaling (Karpenter, Cluster Autoscaler), Pod Disruption Budgets, Spot diversification, idle node cost, node efficiency KPI | references/finops-kubernetes.md |
| On-premises and hybrid FinOps, data centre internal bill, private capacity chargeback, service owners and meters, internal rate card in practice, CPU and memory meter split, idle capacity line, peak-based internal pricing, on-premises usage ingestion, cost score instead of dollars, AWS Outposts billing, Azure Local per-core billing, Azure Arc pricing, Exadata Cloud@Customer term, VMware Cloud Foundation core licensing (16-core minimum), repatriation, cloud exit data transfer waivers, EU Data Act switching charges | references/finops-on-prem-hybrid.md |
| FOCUS, FinOps Open Cost and Usage Specification, multi-cloud cost data normalisation, FOCUS versions 1.0 to 1.4, FOCUS export differences across AWS / Azure / GCP (Tags column shape, numeric precision, x_ provider columns, column drift under one version), billing data ingestion pipeline, pull-based ETL, workload identity federation for cost exports, month-end finalisation, invoice reconciliation datasets, correction handling, minimum viable FOCUS schema, parallel-run migration to FOCUS, FOCUS for SaaS vendors and on-premises sources, FOCUS in contracts and renewals, CID FOCUS dashboard, FinOps hubs | references/finops-focus.md |
| Waste detection playbooks, orphaned resources, idle resources, overprovisioned resources, commitment mismatches, schedule blindness, modernization opportunities, AI/ML inefficiency, egress / data transfer waste, cross-AZ egress cost, two-signal classification, classification confidence (obvious / likely / possible), realised vs potential savings, WasteLine appliance, OptimNow waste taxonomy | references/finops-waste-detection-playbooks.md |
| Named waste pattern (e.g. zombie NAT, snapshot sprawl, idle ELB, cross-AZ egress, orphan Azure disks, Log Analytics ingestion sprawl, idle GKE Autopilot, idle SageMaker endpoint, oversized GPU instance, agent-loop flat-line burn, expiring commitment without a renewal decision, unused Azure reservation, GCP CUD mismatch, S3 lifecycle gaps - incomplete multipart uploads, noncurrent version sprawl, cold data in Standard) | playbooks/<slug>.md (see playbooks/README.md for the full pattern list) |
| What does model X / instance Y cost right now - a current price figure rather than a billing mechanic | Not a reference. Use a live pricing tool if one is connected in the session; otherwise point the user at a live source such as https://optimtoken.optimnow.io. A figure remembered from a reference file is illustrative and dated, not a current price. |
| Multi-domain query | Load all relevant references, synthesise |
Reasoning sequence (apply to every response)
- Apply the OptimNow lens - diagnose before prescribing, connect cost to value, recommend progressively (load
references/optimnow-methodology.mdin full only for strategy or engagement questions) - 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 open-source FinOps tools (FinOps Toolkit, OpenCost, Kubecost, Infracost, etc.) where they fit the problem
- Never quote an undated price - see "Price figures" below
Price figures (apply whenever a number is quoted)
Billing mechanics are durable and are what these references are for. Price figures are volatile and go stale here within weeks. Treat the two differently.
- Prefer a live source. If a pricing tool is connected in the session, use it before quoting any token or instance price. If none is connected, point the user at a live source such as https://optimtoken.optimnow.io, or at the provider's own pricing page, rather than quoting from memory. Either way the figure carries its source and date (rule 2).
- Every figure carries its as-of date and source. Write
$X per 1M input tokens (list price, <source>, <date>), not$X per 1M input tokens. A figure with no date cannot be used in a client deliverable. - Figures inside these references are illustrative, not authoritative. They exist to make a worked example concrete. The durable, quotable part is the mechanics: batch and cache-read multipliers, commitment term structure, the shape of a break-even calculation. Quote those with confidence; date the absolute numbers.
- Never interpolate. If the live source has no figure for the model, SKU, or region asked about, say so. Do not derive one from a neighbouring model, a previous generation, or another region.
- When the tool returns provenance, read it before quoting. The AI Pricing Hub
tools return a
provenanceblock.tier: 1means the figure was fetched live;tier: 2means it came from a dated static snapshot because the upstream was unreachable, andprovenance.noticesays so explicitly.upstreamTimestampis the date to put next to a price;eloAsOfdates the quality scores, which move on a different cadence. A tier-2 figure is usable as a dated snapshot, never as a current price. On tier 2 the compute catalogue is also a subset with no region dimension, so a region filter silently does not apply and an empty result can mean degraded data rather than no match - say which. - A figure quoted from an OptimToken guide has a live copy. Some references quote
a guide figure with the day it was read ("measured on the 2026-10-25 OptimToken
catalogue") and name the guide's slug. When the cloud-finops MCP is connected,
get_live_figures(<slug>)is the live source for that figure: quote the current value with thesnapshotDateit returns, and say whether it is a median or a range. Without the MCP, the reference gives the guide'sfigures.jsonlink. If the call fails, the dated figure in the reference stands, quoted with its date.
Core FinOps principles (always apply)
These six principles from the FinOps Foundation (2026 framework) 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: Inform (visibility, allocation,
anomaly detection), Optimize (commitment discounts, rightsizing, unit economics),
Operate (governance and automation embedded in engineering and finance workflows).
See references/finops-framework.md for the full phase model.
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.
Cloud FinOps Skill by OptimNow - licensed under CC BY-SA 4.0.
