How Customer-Level Cost Attribution Enables Value-Based AI Pricing

Attribute™'s runtime cloud cost intelligence plugged directly into the company's environment with no tagging project and no engineering instrumentation. Within weeks, Attribute™ was attributing cloud spend down to the customer level and streaming it into the customer's existing BI environment alongside revenue data. For the first time, the team could see the true cost to serve each enterprise account, which customers on which tier were driving the most cloud consumption, and whether their highest-revenue accounts were also their highest-margin accounts. Attribute™ then extended to token consumption attribution for AI features, giving pricing a real equation between customer value actions, consumption footprint, and unit economics.

The pricing and finance teams operated with a fundamental blind spot. They knew what customers were paying, but they had no way to see what it actually cost to serve them. Their data warehouse was rich on the revenue side, including seats, tiers, add-ons, and BI, but completely dark on cost at the customer level. That made three critical conversations impossible to have with rigor: which customers are unprofitable and why, what a new AI product should cost and how to protect margin as usage scales, and whether the enterprise tier is correctly priced for the workloads it actually generates. As the pricing leader put it, the team had clear pricing models but had never understood the customer context behind them.

Attribute™'s runtime cloud cost intelligence plugged directly into the company's environment. There was no tagging project and no instrumentation requests to engineering. Within weeks, Attribute™ was attributing cloud spend down to the customer level and streaming it into the existing BI environment alongside revenue data. The team could finally answer what it cost to serve their largest enterprise accounts, which customers on which tier drove the most cloud consumption, and whether their highest-revenue accounts were also their highest-margin accounts.

Looking at roughly 7,500 of the most expensive accounts, the biggest cost drivers running inside their infrastructure, Attribute™ surfaced numbers the business had never seen before. Around 360 accounts were unprofitable, with COGS exceeding revenue. Aggregate losses across those accounts reached ~$1.3M. A clear gap appeared inside the Enterprise Standard tier, where customer usage patterns did not match what that tier had been priced to deliver. The blended consumption of dashboards, AI assistants, per-user add-ons, and complex tier math was producing economics the pricing model had never accounted for.

With customer-level cost context in place, the pricing team turned to the conversation every SaaS company is having: how to price AI features so they do not destroy margin. Most pricing teams today are guessing, running simulations and shipping pricing without a clean read on what AI features actually cost per customer. Attribute™ closed that gap by tracking token consumption by topic and attributing it to specific workloads, features, and customers. The pricing team now has a real equation between the value action a customer takes, the consumption footprint that action drives, and the unit economics the pricing model needs to protect. The new pricing models for the core AI assistant and the incoming AI sidekick add-on were shaped from this production data.

For a work management platform with a growing AI portfolio and increasingly complex pricing, customer context is the foundation every margin decision now runs on. Attribute™ delivers customer-level profitability for every account and every tier, token consumption attribution for AI products to enable true value-based pricing, and workload-level cost context without a tagging project. Pricing, finance, and product teams can stop guessing and start making margin decisions from live production data.