Opportunities › Kubernetes cost accounting and optimization › Proposed app
Nodeledger
See what each Kubernetes namespace really costs, overhead included
- Score
- 62
- confidence 51
- Evidence
- 6
- items on the page
- Rivals
- 0
- named in sources
- First version
- 35 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2019
- Established market · AI estimate
ScreensConcept
Desktop app: DevOps teams analyze cost ledgers, namespaces, overhead tables, and send statements—sustained analytic work on detailed dashboards. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
This month
Matches bill$18,420
- payments
About this appAI proposal
Nodeledger turns cluster usage into a cost ledger per namespace. It counts CPU, memory and GPU, and shows the non-allocatable share of each node as its own line, so the bill adds up instead of leaving an unexplained gap.
It is for infrastructure and DevOps teams who must explain cluster spend to the teams that use it. Set your node prices once, and each namespace gets its cost, its share of overhead and a monthly statement you can send.
Ask questions in plain language: which namespace grew most this week, or what moving GPU jobs to smaller nodes would change. Answers come from your own numbers, with the working shown, and you can reach them from your AI assistant too.
- Tagline
- Every node, every namespace, every dollar accounted for.
- Voice
- PrecisePlainCalm
- Why this name
- Node is the unit of a cluster and ledger is where costs are made to add up. Two plain words joined, easy to say.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Per-namespace cost ledger
CPU, memory and GPU cost for every namespace, by day and month, based on the node prices you enter.
No member yet
Overhead made visible
Non-allocatable capacity is shown per node and spread across namespaces by a rule you choose, so totals match the bill.
No member yet
Plain-language questions
Ask about spend in your own words and get an answer with the figures behind it. No query language needed.
No member yet
Assistant connection
Connect your AI assistant through MCP so cost questions can be asked where your team already works.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#1F5C4ALedger Green · primary#F6F3EAPaper · surface#1D2420Ink · text#D98E1FAmber · accent / overhead#4C6A85Slate Blue · data
Typography
Aa
Nodeledger
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Every node, every namespace, every dollar accounted for.
PricingAI proposal
The material gives no competitor prices. These tiers, scaled by node count as the opportunity suggests, are a starting point to test.
Starter
Free
- One cluster up to 5 nodes
- Namespace cost ledger
- 7 days of history
Team
$99 a month
- Up to 50 nodes
- Overhead rules and GPU view
- Team statements
- Assistant connection (MCP)
- 12 months of history
Business
$399 a month
- Up to 300 nodes
- Multiple clusters
- Single sign-on
- Priority support
Would you pay Team · $99 a month?
Your answer tells PWAS which prices people accept. It changes no score.
No answers yet.
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$194,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- Shared outputs. Platform teams send monthly cost statements by link to team leads and finance, who see the product's name and the figures behind their share.
- Do they become customers?
- Recipients are team leads, who may push for their own view or ask platform teams to adopt it. Test whether statement viewers request access or only read once.
- First customers
- Platform engineers at 50 to 500 person companies running one to three cloud Kubernetes clusters with GPU workloads, who must report cost to teams.
- Where to reach them
- Kubernetes and FinOps communities: Share a free overhead explainer and sample ledger in forums, Slack groups and meetups.
- Open-source repos: Publish the MCP server and agent code, linking to the hosted version.
- Direct outreach: Message platform leads with a free one-cluster cost report of their own data.
- First milestone
- Ten teams connect a cluster and three pay for Team within 60 days.
Why people already pay for this
- VerifiedGitHub repository gained 485 stars in 2835 days
- Public claimThe product provides per-namespace CPU, memory and GPU usage accounting, with non-allocatable overhead made visible
- Public claimThe product connects to AI assistants via MCP for plain-language analysis, supporting multiple AI providers
- Public claimCast AI: $108M Series C, valuation close to $900M (reported) (April 2025)
- Estimate
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · Cluster agentSmall in-cluster collector reading usage metricsUsage per namespace must come from the customer's cluster, sent out read-only.
- This app · Time-series storagePostgres time-series extension or rollup tablesDaily usage per namespace grows fast; rollups keep queries quick.
RisksAI inference
- CommercialOpen-source and cloud-provider cost tools may already satisfy many teams; paying demand for a hosted version is unproven.
- TechnicalAllocating overhead and GPU cost accurately across cluster types and clouds is hard, and wrong figures would destroy trust.
- DataCustomers may resist sending cluster metadata to a third party; namespace names can be sensitive.
- AIPlain-language answers may state wrong numbers if the model misreads data; every answer must show its working.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.