viktorbezdek 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.