Cloud FinOps by OptimNow FinOps knowledge for AI assistants: billing mechanics, commitment decisions and waste runbooks for AWS, Azure, GCP, AI and data platforms. By OptimNow. Both are in the Claude directory. The plugin loads the FinOps library into Claude's context; the connector serves the same library as seven MCP tools that Claude calls when a question needs them. Pick one, or see which to choose. Not using…
Open-source, FOCUS-aligned FinOps knowledge skill and mcp for AI coding assistants. 28 reference files spanning cloud cost (AWS/Azure/GCP/OCI), AI inference economics, Kubernetes, data platforms, allocation, chargeback, anomaly management, waste detection, GreenOps. Installs into 11 AI tools. Refreshed bi-monthly. Built by OptimNow.
FinOps knowledge for AI assistants: billing mechanics, commitment decisions and waste runbooks for AWS, Azure, GCP, AI and data platforms. By OptimNow.
Both are in the Claude directory. The plugin loads the FinOps library into
Claude’s context; the connector serves the same library as seven MCP tools that
Claude calls when a question needs them. Pick one, or see
which to choose.
Not using Claude? The same library works in ChatGPT, Gemini, Cursor, Copilot and other
tools: see Install.
Ask your assistant how to reduce a cloud bill, size a commitment, read a term sheet
or allocate shared AI costs, and it answers from how billing works, with decision
rules and worked examples, instead of general advice. The same files work in Claude,
ChatGPT, Gemini, Cursor and any MCP-compatible tool. Open source, written by
OptimNow
from enterprise FinOps delivery, and aligned with the FinOps Foundation framework.
What you can ask, by role
Each question below is answered by a file that exists in this repository today.
“A third of our bill is unallocated. How do we reach a defensible allocation of shared costs?” (finops-allocation-showback.md)
“We run half our workloads in our own data centres. How do we give app teams a monthly bill for on-prem capacity they will accept?” (finops-on-prem-hybrid.md)
“Who should own commitment purchases, and which meetings do we need to run the practice?” (finops-practice-operations.md)
“We need to start reporting our cloud carbon emissions. Where do we begin?” (greenops-cloud-carbon.md)
Finance and procurement
“Our client wants separate AWS invoices per business unit. Their AWS contact suggested Cost Categories. Is that right?” (finops-aws-billing.md)
“We are migrating from EA to MCA. What FinOps work do we need to do before the switch?” (finops-azure-billing.md)
“What has to be true in Finance and in the ERP before we move from showback to chargeback?” (finops-chargeback.md)
“Our AWS enterprise agreement renews in 6 months. What should we check on the term sheet before signing?” (finops-cloud-contracts.md)
“Finance wants a 12-month cloud forecast with an accuracy target. Which measures do we report, and how tight should the band be at our maturity?” (finops-forecasting.md)
“Our monthly bill jumped from $12K to $38K after a developer enabled Fast mode in Claude Code. How do I get this under control?” (finops-anthropic.md)
“How do we split the cost of a shared Kubernetes cluster between teams?” (finops-kubernetes.md)
“Our Snowflake bill doubled in a quarter. Is it the warehouses, storage or Cortex, and what do we check first?” (finops-snowflake.md)
AI product owner
“Our AI coding tool spend tripled in 6 months. How do we control it without banning the tools?” (finops-ai-dev-tools.md)
“Should we self-host an open-weight model on rented H100s instead of paying per token, and what hidden costs do TCO calculators miss?” (finops-ai-self-hosted-vs-managed.md)
“We spend $40K a month on Bedrock and do not know which features it goes to. Where do we start?” (finops-bedrock.md)
“Who on my leadership team should sponsor FinOps, and what does the sponsor decide?” (finops-practice-operations.md)
“What should I tell the board about our cloud carbon footprint, and which number is defensible?” (greenops-cloud-carbon.md)
“What would it take to cut our cloud bill by 20% without slowing delivery?” (optimnow-methodology.md)
“Where should we start with FinOps, and what should we expect in the first six months?” (optimnow-methodology.md)
Neither the skill nor the connector can see your cloud account. For a question about
your own resources, the answer is a detection query you run yourself, taken from the
matching runbook.
What it covers
The FinOps Framework
The reference library is mapped to the 22 capabilities of the FinOps Foundation
framework, grouped in its 4 domains. A filled square means at least one reference
owns the capability; a grey square means the capability is covered inside another
file; a dashed square is a known gap, listed with its reasoning in
docs/ROADMAP.md.
This map is generated from the frontmatter of every reference file, and the CI
pipeline fails when the committed map no longer matches the files
(fcp-coverage.md).
Providers and business needs
The matrix reads the library by business need. Every cell comes from a named
reference file, listed in the notes under the tables.
Level
Meaning
● deep
A dedicated reference or section, with decision rules, worked examples or detection queries
◐ partial
Covered inside a broader file, or mechanics only
–
Not covered today
n/a
The combination does not exist
Hyperscalers
Business need
AWS
Azure
GCP
Understand and allocate the bill
●
●
●
Budget and forecast
●
●
●
Commitments and discounts
●
●
●
Contract negotiation and renewal
●
●
●
Reduce waste
●
●
●
AI and GenAI spend
●
●
●
Data platforms
◐
●
◐
SaaS and licences
◐
◐
◐
Carbon footprint
●
◐
◐
Other estates
Business need
OCI
On-prem
Understand and allocate the bill
◐
●
Budget and forecast
◐
◐
Commitments and discounts
◐
◐
Contract negotiation and renewal
–
–
Reduce waste
◐
◐
AI and GenAI spend
–
◐
Data platforms
–
–
SaaS and licences
–
◐
Carbon footprint
–
–
Platforms
Business need
AI
Data
SaaS
Understand and allocate the bill
●
●
◐
Budget and forecast
●
◐
◐
Commitments and discounts
●
●
◐
Contract negotiation and renewal
–
◐
◐
Reduce waste
●
●
●
AI and GenAI spend
●
◐
●
Data platforms
n/a
●
n/a
SaaS and licences
◐
◐
●
Carbon footprint
–
–
–
On-prem covers owned data centres and the hybrid platforms (Outposts, Azure Local,
Arc). AI covers the AI platforms: Anthropic, AWS Bedrock, Azure OpenAI, GCP Vertex AI
and the open-weight vendor APIs. Data covers Databricks, Snowflake and Microsoft
Fabric. SaaS covers SaaS applications, AI coding tools and licences.
Where each cell comes from
All paths are under plugins/cloud-finops/skills/cloud-finops/.
Understand and allocate the bill. AWS: references/finops-aws-billing.md, finops-allocation-showback.md, finops-focus.md, finops-tagging.md. Azure: finops-azure-billing.md, finops-focus.md, finops-tagging.md. GCP: finops-gcp.md (cost data foundation), finops-focus.md, finops-kubernetes.md. OCI, partial: finops-oci.md (cost reports, cost-tracking tags), finops-focus.md. On-prem: finops-on-prem-hybrid.md (internal bill, usage ingestion). AI platforms: the “Cost visibility and allocation” sections of finops-bedrock.md, finops-azure-openai.md, finops-vertexai.md, finops-anthropic.md; finops-for-ai.md. Data platforms: finops-databricks.md (system tables, allocation), finops-fabric.md (shared-capacity allocation), finops-snowflake.md (query attribution). SaaS, partial: finops-sam.md (discovery), finops-focus.md (FOCUS for SaaS vendors), finops-ai-dev-tools.md (attribution patterns).
Budget and forecast. AWS and Azure: finops-forecasting.md, finops-anomaly-management.md, and the Budgets sections of finops-aws-billing.md and finops-azure-billing.md. GCP: finops-gcp.md (budgets, spend caps), finops-anomaly-management.md. OCI, partial: finops-oci.md (Budgets). On-prem, partial: finops-on-prem-hybrid.md (rate setting); capacity planning is on the roadmap. AI platforms: finops-ai-value-management.md (AI investment budgets, stage gates), finops-forecasting.md (AI forecast scenarios), finops-anthropic.md (spend limits). Data platforms, partial: budget controls in finops-snowflake.md, finops-databricks.md, finops-fabric.md. SaaS, partial: finops-sam.md (renewal calendar, metrics), finops-ai-dev-tools.md (spending caps).
Contract negotiation and renewal. AWS, Azure, GCP: finops-cloud-contracts.md, plus finops-azure-billing.md (EA-to-MCA). Data platforms, partial: the data platform section of finops-cloud-contracts.md, finops-databricks.md, finops-snowflake.md. SaaS, partial: finops-sam.md (renewal governance), finops-itam.md (pre-renewal checkpoint, joint negotiation). OCI, on-prem and AI platforms: no file. See “Contract negotiation and renewal” below.
Reduce waste. AWS: finops-aws.md, the 5 finops-aws-patterns-*.md files, 22 runbooks. Azure: finops-azure.md, finops-azure-patterns.md, 10 runbooks. GCP: the pattern catalogue inside finops-gcp.md, 7 runbooks. OCI, partial: finops-oci.md (6 patterns, no runbook). On-prem, partial: finops-on-prem-hybrid.md (idle capacity line, cost score). AI platforms: finops-for-ai.md (anti-patterns), finops-genai-capacity.md (provisioned-capacity waste), the optimisation sections of the 4 AI platform files, 2 cross-cloud runbooks. Data platforms: the pattern sections of finops-databricks.md, finops-snowflake.md, finops-fabric.md. SaaS: finops-sam.md (sprawl patterns), finops-ai-dev-tools.md (optimisation levers).
AI and GenAI spend. AWS: finops-bedrock.md, finops-aws.md (SageMaker), the GPU and SageMaker runbooks. Azure: finops-azure-openai.md. GCP: finops-vertexai.md. On-prem, partial: finops-ai-self-hosted-vs-managed.md. AI platforms: finops-for-ai.md, finops-agentic.md, finops-ai-value-management.md, finops-genai-capacity.md, finops-anthropic.md, finops-open-weight-vendors.md. Data platforms, partial: Cortex in finops-snowflake.md, model serving in finops-databricks.md. SaaS: finops-ai-dev-tools.md, playbooks/cross-cloud-coding-agent-token-waste.md.
Data platforms. AWS, partial: finops-databricks.md on AWS, Redshift commitments in finops-aws-patterns-databases.md. Azure: finops-fabric.md, finops-databricks.md. GCP, partial: BigQuery in finops-gcp.md. Data platforms: finops-databricks.md, finops-snowflake.md, finops-fabric.md.
SaaS and licences. AWS, Azure, GCP, partial: marketplace channel governance in finops-itam.md, Marketplace private offers in finops-cloud-contracts.md, Azure Hybrid Benefit in finops-azure-commitments.md. On-prem, partial: VMware core licensing in finops-on-prem-hybrid.md, BYOL in finops-itam.md. AI platforms, partial: licensing in finops-open-weight-vendors.md. Data platforms, partial: Pro, PPU and F-SKU licences in finops-fabric.md. SaaS: finops-sam.md, finops-itam.md, finops-ai-dev-tools.md.
Carbon footprint. AWS: greenops-cloud-carbon.md (Sustainability Console, Well-Architected pillar). Azure and GCP, partial: the same file, with the Azure and GCP depth pass on the roadmap; finops-gcp.md (Cloud Carbon Footprint).
Contract negotiation and renewal
The reference finops-cloud-contracts.md
covers negotiated spend commitments with the 3 hyperscalers: AWS Private Pricing
(EDP and PPA), Azure MACC and the Azure Commitment Discount, and Google Cloud Minimum
Commitment. It describes the 12 clauses of an enterprise agreement, where the discount
shows in billing data, migration funding (MAP, Azure Accelerate, RaMP), Marketplace
private offers as a commitment lever, the reseller channel, 9 intake questions to ask
before advising, a 15-point review grid for a term sheet, the usual pitfalls and the
timing of a renewal. A closing section covers Databricks and Snowflake commitments
inside a cloud contract. Benchmarks come from public sources named in the file; the
library carries no observed contract figure.
Also covered: SaaS renewal governance (renewal calendar, auto-renewal review) in
finops-sam.md and
the pre-renewal checkpoint and marketplace channel governance in
finops-itam.md.
Not covered today: negotiation with AI providers (Anthropic, OpenAI and the hosted
model vendors; the library covers their reservation mechanics, not their enterprise
agreements), OCI Universal Credits beyond their mechanics, and colocation or hosting
contracts.
Waste runbooks
A runbook is a step-by-step procedure for one specific waste pattern: the symptoms, a
detection query you run in your own account, the fix in safest-first order, and the
mistake to avoid. The library files them by provider and by waste category, and each
one carries a confidence level that says whether a single signal is enough to act.
What it does not do
Live prices. The files carry billing mechanics, which stay true for years. Price
figures go stale inside a packaged skill within weeks, so they are left out. For a
current price the skill points to OptimToken,
OptimNow’s free comparison of LLM token rates and compute instance rates, each
figure with its as-of date.
Your cloud account. Nothing here reads a bill or an API. For “which of my
resources” questions, the deliverable is the runbook’s detection query.
Legal, tax or accounting advice. The contract and chargeback references describe
mechanics and the questions to ask; your counsel, tax adviser and auditors decide.
Topics still open. Accounting for owned capacity (depreciation, lease treatment),
AI provider and OCI contract negotiation, and the FinOps Education & Enablement
capability. Each is listed in docs/ROADMAP.md with the trigger
that would reopen it.
Guaranteed routing. The model decides which file to read. When an answer arrives
without a visible file read or tool call, ask for the library by name (“check the
playbook library”). See docs/CONCEPTS.md.
curl -sL https://raw.githubusercontent.com/OptimNow/cloud-finops-skills/main/install.sh | bash -s -- --tool <name> (drop -s -- --tool <name> to auto-detect the tool)
Any MCP client
Hosted: add an HTTP MCP server at https://mcp.optimnow.io/mcp, exactly that form. Local: pip install cloud-finops-mcp, config snippets from ./install.sh --tool mcp
ChatGPT, Gemini (web)
Self-host for now: ./install.sh --tool chatgpt --grouped or ./install.sh --tool gemini
Full options, troubleshooting, and the model-agnostic API loader: see
INSTALLATION.md. A version-tagged zip
(cloud-finops-vX.Y.Z.zip) is attached to every
GitHub release.
How it works
The skill is a folder of markdown files: an entry point that routes a question
to the right reference, the reference library and the runbooks. Installed in Claude,
Kiro or through the installer for other tools, the guidance sits in the model’s
context before it answers. No code runs, no data leaves your machine.
The MCP connector serves the same library the other way round, as 7 read-only
tools the model calls when it decides to: list, search by FinOps capability or
waste category or by a question in your own words, fetch one reference or runbook,
and read the current figures of an OptimToken pricing guide. One click from its
Claude directory listing, or one URL to paste
(https://mcp.optimnow.io/mcp), nothing to install. Tool calls go to a server
OptimNow runs in Paris; see “Data handling” below.
The plugin in the Claude directory is the skill only. The connector is a
separate listing, so that the same content is not loaded twice in one session.
Live prices come from OptimToken, queried at
answer time, never copied into these files.
The 2 delivery shapes behave differently: a loaded skill grounds advisory and
symptom questions without a decision to make, while the connector depends on the
model choosing to call a tool. What that means in practice, and when to use both,
is in docs/CONCEPTS.md. Using a non-Claude model through an API,
add the response contract from INSTALLATION.md to your system
prompt so answers stay structured and billing-grounded.
Every reference and runbook declares the questions it answers in its frontmatter
(questions:), in English and French. The role list above is generated from the ones
tagged with a persona and checked in CI. Ask in your own words: the connector’s query
parameter and the skill’s routing both search those lists.
Design principles
Visibility before optimisation. Establish what you spend and what drives it,
then act. The skill does not recommend commitments to a team that cannot allocate
its bill.
Provider mechanics first, vendor claims second. Guidance is grounded in how
billing works (CUR columns, Azure cost-management semantics, BigQuery export, FOCUS
conformance), with primary sources cited. Vendor savings and sustainability claims
are read critically.
Maturity is contextual. Where cloud is a cost centre, Crawl plus selective Walk
is often the right state; where cloud is the product, Run pays for itself in gross
margin. The skill assesses maturity before recommending.
Cost connects to business value. Every recommendation answers the CFO question:
which business outcome does this protect or create.
Mechanics live here, price figures do not. Any figure a reference quotes carries
its date and source; current prices route to OptimToken.
AI cost management is a first-class domain, with a reference per AI platform
and a discipline file for agents, capacity and investment governance.
Data handling
What the skill and the hosted connector run, send and fetch, so you can decide where
each is appropriate. Full policy in PRIVACY.md.
The skill is static text. The entry point, the references and the runbooks are
markdown files read into the model’s context. They run no code, call no network
endpoint and carry no credentials. The runbooks contain detection queries (billing
export SQL, CLI commands) that you run in your own cloud account; nothing in this
repository reads a cloud account.
The hosted MCP connector, if you add it, sends tool calls to mcp.optimnow.io.
When the model calls one of the 7 tools, the tool arguments (a reference or runbook
name, a section phrase, facet filters, a guide slug) travel over HTTPS to that server and the
matching content comes back. The server requires no account and no authentication,
holds no database and no per-user state, and serves the same public files as this
repository. Its application log writes one line per request (method, path, status
and request type, never the arguments); when a lookup matches nothing, it also
writes the tool name and the arguments that missed, which are short strings the
model writes and can include a word copied from your question. The server runs on
Fly.io, primary region Paris, which holds that log stream, possibly with the client
IP address, under its default retention. OptimNow does not sell, share or profile
from it.
Price lookups route to a live tool, not to this repo. Whether the model opens
OptimToken is its decision in the conversation.
One tool makes an outbound request.get_live_figures sends one HTTPS GET to
optimtoken.optimnow.io (an OptimNow site) for a guide’s figures.json, carrying the
guide slug and a cloud-finops-mcp User-Agent, nothing from the conversation. On the
hosted connector the request leaves the server; with the PyPI package it leaves your
machine. No other tool touches the network.
Nothing else. No telemetry, no analytics beacon, no update check, no package
launcher, no credential read from your environment.
Maintenance
The library is updated weekly. Every Saturday an automated scan checks around 35
sources: cloud and AI provider release notes, billing changelogs and pricing pages,
plus FinOps community publications. Each change is confirmed on the provider’s own
documentation and reviewed by a practitioner before it is applied. Every merge to
main becomes a new plugin version in the Claude directory, and a version-tagged
release ships the same content to PyPI and the MCP Registry. Watch or star the
repository to be notified, and read CHANGELOG.md for what each
version changes for you, in plain words.
Contributing
Practitioner experience is the highest-value contribution. Frameworks and vendor docs
are already public; what is rare is “we tried X in production, this is what it
billed”. Corrections to billing mechanics, new or improved runbooks, real-world
counter-examples and adversarial review of the recommendations are all welcome: the
repository is opinionated, and it should also be falsifiable.
The full guide, including the repository structure, contribution types, process and
conventions, is in CONTRIBUTING.md. If your change names another
OptimNow tool (an MCP tool name, an endpoint URL, a provenance field), read
DEPENDENCIES.md first.
Adapting this skill for your organisation
Two paths, both first-class.
An overlay is a second, private skill folder beside this one. It cites the public
sections it builds on by heading and adds what this repository deliberately leaves
out: your negotiated rates, your tag taxonomy, your coverage targets, the decisions
your committee already took, one client account’s profile. It copies nothing, so a
public update never breaks it, and a check tells you when a heading it cites has
moved. Start from docs/OVERLAYS.md and the template in
docs/overlay-template/.
A fork is the right tool when you want to change the public text itself: a
different methodology, your own house style, a provider this repository does not
cover. A fork gives you a stable base you can pull upstream updates into at your own
pace. Typical customisations:
Adding organisation-specific tag requirements to finops-tagging.md
Adding reference files for internal tools or platforms not covered here
Adjusting the methodology file to your team’s own approach
About OptimNow
OptimNow is a boutique FinOps consultancy that helps organisations connect cloud and
AI spend to measurable business value. Based in France, with European reach.
Compare what 250+ models cost per request, with caching and batch factored in, plus compute instance rates across 7 clouds. Also an MCP connector; this skill routes price questions here
This skill incorporates content derived from the following sources:
FinOps Foundation: framework definitions, capability
descriptions and maturity model structure are based on the FinOps Framework.
Tokenomics Foundation: the token complexity
classes in the agentic FinOps reference are adapted from Big-T Notation by
Dan Neff (Adobe), published by the Tokenomics Foundation under
CC BY 4.0. The AI cost and value
references also draw on the Foundation’s five-layer tokenomics stack paper, its
proposed definition of the total cost of AI, and the drafts of its consumption and value
working groups, each cited where it is used.
Every other source is credited inline, in the file that uses it. Billing mechanics
cite the provider’s own documentation with the date it was read; figures and
opinions from practitioner talks (FinOps X 2026 and others) are attributed to the
speaker and organisation where they appear.
All referenced content has been adapted with additional context from OptimNow’s
consulting delivery experience. Any errors or opinionated interpretations are our own.
This skill is independently maintained and is not affiliated with or endorsed by the
FinOps Foundation.
You are free to use, adapt and redistribute this skill, including for commercial
purposes, as long as you credit OptimNow and share any derivatives under the same
licence. When redistributing the material, please include this attribution:
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