how it works
What Unit Economics does
Unit economics is a methodology for connecting cloud spend to the business metrics that actually drive decisions. Cost per customer. Cost per ride. Cost per analyzed transaction. Cost per active user. These are the metrics that tell you whether your cloud investment is generating profit or quietly eroding it.
know your cost to serve
A rideshare company gets cost per ride. A SaaS platform gets cost per active user. A financial services firm gets cost per analyzed transaction. The metric comes from your runtime, not your data pipeline.
know your cost to produce
Cost to produce measures what it costs to build and run one unit of your product. Cloud Intelligence™ attributes shared infrastructure, including Kubernetes clusters, databases, queues, and GPU compute, to the features and teams that actually consume them.
see your AI economics
AI costs show up as a single line item. Cloud Intelligence™ maps every token call and GPU-hour back to the team, customer, feature, or AI agent that triggered it, broken down by model and provider. You can price AI features on real margins instead of guesswork.
measure what tags miss
Egress charges, cross-AZ transfer, NAT gateways, shared Kubernetes workloads, multi-tenant databases. These are the costs that every tag-based tool leaves unattributed. eBPF runtime observation attributes them automatically because it reads actual traffic, not labels.
the metric that gets you in the room
from technical metrics to business decisions
Unit economics is the common language between engineering, finance, and leadership. Cost per CPU means nothing to a CFO. Cost per ride, download, scan whatever matters to your business changes a board conversation.
Cloud Intelligence™ translates technical resource consumption into the unit cost metrics that drive business decisions: contribution margin by product, customer lifetime value informed by real infrastructure costs, and cost trends that tell you whether scaling is improving or eroding your margins.
Engineering can quantify its contribution to gross profit. Finance can forecast cloud costs tied to business demand. Product can price features based on real unit economics instead of estimates.
profit maximization
optimize for profitability, not just efficiency
The goal is not just to reduce cloud spend. It is to maximize the return on your cloud investment.
Cloud Intelligence gives you the data to do this. Monitor cost-to-serve trends at the customer level. Identify which accounts are profitable and which are quietly draining your P&L. See whether a new feature improves margins or destroys them. Track whether scaling your AI capabilities is generating value or burning cash.
other tools tell you costs are up. we tell you why.
Without runtime unit economics: "Your EC2 cost is up 17% month over month."
A number without context. Your team spends three days digging through tags, dashboards, and Slack threads to figure out why.
With runtime unit economics: "ACME Corp's cost to serve is up $44K driven by 50M additional tokens consumed by the auto-summary feature launched last month."
Usage tied to the business unit that drove it. Finance gets cost to serve. Engineering gets cost to produce. Leadership gets contribution margin. Same data, three conversations, no spreadsheet.
See your unit cost
15 minutes to deploy. Unit economics by end of week.
Integrated with your entire tech-stack
Works natively with your cloud providers, AI platforms, data platforms, DevOps and SecOps tooling.
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Frequently asked
questions
What is cloud unit economics?
The FinOps Foundation defines cloud unit economics as a system of profit maximization based on objective measurements of marginal cost and marginal revenue. In practice, it means defining a unit metric that matters to your business (cost per customer, cost per ride, cost per transaction) and tying cloud spend to that metric so you can make data-driven decisions about your cloud investment.
How does Cloud Intelligence extract unit economics?
A lightweight eBPF sensor deploys to your cluster and reads runtime network traffic. Costs are automatically attributed to the customer, team, feature, or AI agent that drove them based on actual consumption patterns. No tagging required. No data pipelines to build.
What unit metrics can I measure?
Any metric that maps to your business model. Cost per customer, cost per transaction, cost per ride, cost per active user, cost per AI inference, cost per feature. The sensor observes the runtime identifiers (customer IDs, service names, workload labels) that connect cloud spend to business activity.
How long does setup take?
The sensor deploys in about 15 minutes with no code changes. Most teams see per-customer and per-feature cost data within the first week.
Do I need engineering resources?
Minimal. The sensor deploys as a standard Kubernetes DaemonSet. No application code changes, no tag governance, no data pipelines to build or maintain. This is the primary difference from traditional unit economics approaches, which the FinOps Foundation describes as requiring significant engineering effort for data collection.