Cloud Intelligence™Cloud Intelligence™

Put your AI spend under a microscope.

Every token, every agent, every customer. Attributed automatically: No tags, no SDKs, no code changes.

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what you get

Attribute™ solves the AI cost attribution problem

Most AI cost tools stop at the billing layer. Attribute™ goes deeper - reading kernel-level network traffic to map every inference call back to the workload, product, and customer that triggered it.

Token-in, token-out

Token-in, token-out

Input, output, and cached tokens broken out per request.

Allocate AI cost per feature

Allocate AI cost per feature

See which product features drive LLM spend.

Per-agent cost

Per-agent cost

Measure what each AI agent costs to run.

AI anomalies. In real-time.

AI anomalies. In real-time.

Catch unexpected token spikes before they hit margins.

AI usage per customer, human vs. non-human

AI usage per customer, human vs. non-human

Separate human spend from agent and know what each customer actually costs to serve.

Signals → actions

Signals → actions

Pause keys or swap models when usage crosses policy.

FinOps without tagging

Attribute™'s eBPF sensor traces each inference call through managed and self-hosted gateways alike, all the way back to the workload, product, and customer that triggered it.
Works across OpenAI, Azure OpenAI, Bedrock, Anthropic, and Google Vertex AI. No instrumentation required.

Every token. Every agent. Every customer. Attributed.

Attribute™ separates human traffic from agent traffic at runtime, giving you a clear view of what real users and automated workloads are each consuming.

  • Right-size infrastructure based on how humans and agents actually use it.
  • Understand which agents are contributing to cost growth.
  • Build pricing and tiers from actual consumption data.
  • Spot unexpected usage spikes before they reach the P&L.

Connect AI spend to the customers and features that drive it

Usage-based AI cost is hard to forecast without context. Attribute™ ties spend to consumption so every pricing, scaling, and investment decision is backed by real data.

  • Token consumption surfaced per customer, in context.
  • Per-feature AI cost visibility across your product architecture.
  • Margin contribution measured per capability, not just per model.
  • Early visibility into consumption trends before they compound.