Know the True Cost
of Every Customer

You can’t tag a customer, but your inbound traffic is still consuming your infrastructure.
Whether its customers, vendors, partners or bots, visibility into your margins is strategic.
Attribute’s eBPF deep packet inspection captures every signal, calculating cost-to-serve based on consumption data.

Customer Cost · MTD
Total Cloud Spend
$2.84M
Total Revenue
$6.12M
Blended Margin
53.6%
across 147 customers
Unprofitable Customers
112
$5.7M in losses
Customer Monthly cost Annual revenue Proj. margin Top feature
Showing 8 of 147 customers

Know Your Customer COGS

Attribute observes customer behavior at runtime and attributes cost based on actual resource usage and consumption. 

  • Total Cost of Ownership full customer cost breakdown (not just compute)
  • Automatic COGS/Margin data based on actual customer usage 
  • Solved for blind spots like k8s, AI Cost, cross-AZ traffic, NAT cost, etc. 
  • Full break down of “shared resources” without manual intervention
  • Works natively in multi-tenant architectures
Customer Cost · auto-detected
Customers tracked

0
Top-spender accounts
11.7% drive most cost
0
Customers by tier · top-cost
0 customers
Pro Enterprise Starter
Customer Tier Cost
Netflix Enterprise $0
Uber Pro $0
JP Morgan Enterprise $0
Walmart Pro $0
Starbucks Starter $0

You Can't Tag a Customer; How It Works

Attribute’s eBPF and Deep Packet Inspection (DPI) tech allows us to scan and identify customers’ identifiers from the traffic and correlate cloud consumption in runtime.

  •  eBPF-based data extraction
  • Automatically identify and extrats IDs (e.g., JWT, HTTP headers) from traffic to allocate cost per customer
  • Runtime cost distribution across all customer accounts
  • No change in code or configuration
user: "acme-corp" pod: acme-api-7d8f client_id='ACME' src: 72.12.45.12 group: acme-prod AI AI / LLM Cost K8s K8s Compute RDS Usage Network Traffic Kafka Usage ACME CORP TOTAL COST $0.00k per hour · live

See the cost per feature/ endpoint for every customer

  • Full feature usage breakdown by customer
  • Cost per feature per customer
  • Identify low-margin-high-usage features
  • Data driven pricing and tier structure decisions
  • Churn indicators and customer behaviors
Cost per Feature · MTD
Endpoint Calls Cost
/api/checkout Shopify 38%
2.4Mcalls $0
/api/recommendations Netflix 51%
1.8Mcalls $0
/api/search HubSpot 22%
3.1Mcalls $0
/api/checkout/refund Walmart 44%
412Kcalls $0
/api/ride/dispatch Uber 89%
5.2Mcalls $0
5 endpoints · 13M calls $0

"The voice of our customers"

Testimonials
Akamai logo featuring a stylized wave graphic to the left of the company name in green text

Stop digging. Start knowing.

“Their tagless technology made it extremely easy to overcome the data blindspots we had, and we can clearly discover and contextualize how each service or customer utilizes cloud resources, helping us make informed decisions rapidly.”

Ziv Sivan VP of Engineering
Read Case Study
Claroty logo with a magenta circular icon on the left and the word CLAROTY in bold, black uppercase letters on the right.

How Claroty Achieved Precise Customer Cost Attribution with Attribute

“The data from Attribute transformed our understanding of cost structures, influencing key strategic decisions in pricing, renegotiations, and market positioning.”

Jonathan Langer COO
Read Case Study

Attribute customer unlocks cost margin visibility, pivoting go-to-market approach

“We never had customer context. We saw total deal value and application-level data, but never how the customer actually uses the system.”

VP of Pricing & Monetization, Global SaaS
Read Case Study
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Ready to understand your true customer margins?

description

Why can't I use tags to track cost per customer?

Tags track infrastructure, not the customers using it. A customer moves through your platform hitting your API, consuming shared resources, triggering inference calls without leaving any tag behind. In multi-tenant architectures, there is no resource to tag. You need visibility at the traffic layer, not the infrastructure metadata layer, to see what each customer actually costs.

How does Attribute identify which customer is consuming which resources?

Attribute's eBPF sensor uses deep packet inspection to read customer identifiers such as JWT tokens, HTTP headers, or client IDs directly from the traffic. It correlates those identifiers with the infrastructure resources consumed in real time, attributing compute, network, database, and AI spend to the correct customer without any changes to your application code.

How do I find out which customers are unprofitable?

Attribute surfaces cost-to-serve per customer alongside your revenue data, flagging accounts where infrastructure costs exceed what the customer pays. One Attribute customer discovered over 360 accounts where COGS exceeded revenue, totaling more than $1.3 million in losses that were completely invisible before.