Know the True Cost of Your
AI & LLM Usage
AI spend is a black box. LLM Gateways show token totals but can’t tell you who burned them. Attribute traces every token, inference, and training run back to the team, product, and customer that drove it. True AI TCO, across every provider.
AI Consumption Insights
- Track token consumption at the application level
- Usage based AI cost allocation broken down by customer
- See the complete AI workload TCO: tokens, compute, GPUs, and databases, based on runtime consumption
Your LLM Gateway Shouldn't Hide Your Costs
Most teams route AI through gateways. The problem is that every billing tool sees the gateway as the consumer.
Attribute’s eBPF sensor sees traffic into/out of the gateway, tracing each inference call back to the workload, product, and customer that triggered it. There’s no instrumentation required. Works with managed and self-hosted gateways, across OpenAI, Azure OpenAI, Bedrock, and Anthropic.
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Human vs. Non-Human AI Cost
Automation platforms are making API calls to your products around the clock, often under the same billing as your human users. Attribute separates human traffic from non-human traffic at runtime, so you can see exactly what AI agents, bots, and integrations are costing you versus real users.
- Scale infrastructure decisions around how humans and AI agents actually consume your platform
- Identify automations and AI agents driving your infrastructure costs
- Price AI tiers and API access based on actual consumption patterns
- Catch runaway automation before it hits your margins
Track AI Usage Per Customer
AI cost is dynamic and usage-based. Without context, it’s impossible to design pricing, forecast margins, or scale responsibly. Attribute ties AI spend to consumption, so every pricing and scaling decision is backed by actual usage data.
- AI token consumption surfaced in customer context
- Per-feature AI cost visibility across your product architecture
- Margin impact measured per AI capability, not just per model
- Early detection of runaway consumption before it hits the P&L
Attribute sees what your billing tools miss
LLM gateways, shared GPU clusters, and AI agents don't have tags. They never will. Attribute's eBPF sensor reads runtime network traffic directly, identifying which model was called, by which workload, triggered by which customer or agent.
Testimonials
AI pricing built on real margins, not guesswork
“Attribute gave us visibility into margins at the customer level for the first time. That changed how we price and how we sell. For the first time, finance and go-to-market are working from the same numbers, so we can price AI based on real margins, not assumptions.”
From Black Box to Budget Visibility in Days
“Not every day you come across something that delivers value as quickly as yours did for us. I was seeing useful insights inside the POC, and we had only deployed it to a couple of real clusters.”
Crystal-clear visibility into cloud spend
“We now have crystal‑clear visibility into our cloud spend at the workload and tenant level, and that insight has already led to actionable savings”
Ready to scale AI without losing margin?
Know your true AI cost-to-serve before you price it, sell it, or scale it.
Most FinOps tools rely on billing exports and tags. AI infrastructure, LLM gateways, shared GPU clusters, inference endpoints have no tagging surface. Every call looks the same at the billing layer. Without runtime visibility into the traffic itself, there's no way to separate which customer or workload triggered each request.
Attribute deploys a lightweight eBPF sensor that reads network traffic at runtime. It identifies which workload made each inference call, which customer or feature triggered it, and maps the cost back accordingly. It works with managed and self-hosted LLM gateways across OpenAI, Anthropic, Bedrock, Azure OpenAI, and Google Vertex AI.
Human traffic comes from real users interacting with your product. Non-human traffic comes from automation platforms, AI agents, bots, and integrations making API calls in the background. Without separating them, you can't accurately price AI tiers, forecast usage, or catch a runaway automation before it hits your margins. Attribute identifies both at runtime without requiring separate API keys.
Profitability per AI feature requires knowing the true cost to run it, tokens, compute, GPU hours, and the databases behind it mapped to the revenue or value it generates. Attribute surfaces the full AI workload TCO per feature based on runtime consumption, giving pricing and product teams the data to make those calls with actual numbers instead of estimates.
Yes. Attribute's eBPF sensor reads traffic into and out of your LLM gateway, tracing each inference call back to the workload, product, or customer that triggered it regardless of whether the gateway is managed or self-hosted. This resolves the most common AI cost blind spot: the gateway shows total spend, but hides who caused it.
Most customers see attributed AI cost data within the same day. Deployment requires a 15-minute sensor install, attribution begins as soon as the sensor observes traffic.