Role - CTO and engineering

AI agents do not wait for the invoice, so your cost model should not either.

See cost by team, workload, environment and agent, allocated daily instead of a month late. The platform underneath, the people who run it and the AI on top sit in the same number as the cloud bill. Findings write back to Jira and ServiceNow, so an optimization becomes a ticket.
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Published result

A global firm is saving on cloud across many markets

Over £1.2m

Annual cloud savings across AWS and Azure, with daily cost allocations and natural-language querying, in 90+ markets.

Global data and consulting firm.

The moments you will recognize

Where engineering cost goes unexplained

  • Explain a cloud bill that grew faster than the product

    Which workloads are outgrowing the business they serve, and which anomaly is worth an engineer’s afternoon.

  • Put a cost on an agent

    An agent decides how many steps to take, which tools to call and when to stop. Its cost is tokens plus the harness, the retrieval, the infrastructure and the people who supervise exceptions. Few teams have that number.

  • Defend the platform team’s cost

    A shared platform gets charged to product teams as a percentage nobody accepts. A rate the platform owns and a volume the consumer owns ends the argument.

  • Make the saving stick

    Engineering rightsizes, the capacity is reclaimed, and the saving never reaches finance because it was never booked. The next workload absorbs it.

A saving that never gets booked is just capacity that someone else will use.
Questions this page answers

Questions about team, workload and agent cost, answered from one model

  • What did each team, workload and environment cost this month, allocated daily?
  • Which workloads are growing faster than the product they serve?
  • What is our commitment coverage, and how much on-demand spend should have been reserved?
  • What does one transaction, one inference or one API call cost, fully loaded?
  • Which agents consumed what, across model calls, tool calls, retries and duration, and what did each produce?
  • Which optimization actions are open, who owns them, and which have been verified and booked?
Where to start

The challenges engineering leaders start with, and the capabilities behind them

ChallengeWhy it fits this seat
Cloud financial managementDaily allocation, anomalies and commitments, with write-back to the tools engineering already uses.
AI and agent economicsAn owner and a purpose for every agent, and cost per successful outcome in place of cost per token.
Unit economics and cost to serveCost per unit with the platform and the people in it, so the number engineering optimizes is the number finance reports.
Labor and project costWhere engineering effort goes, by project and role, without a timesheet culture engineers refuse.
CapabilityWhat it gives this seat
FinOpsBilling from AWS, Azure and Google Cloud in one format, allocated daily with your tags or without them.
Time Capture and Resource PlanningEffort against approved projects, contractors included. The largest line in what your platform costs, made visible.
AI EconomicsToken, model, GPU and agent cost, classified and owned.

Turn cost findings into tickets your engineers can act on.

See one workload’s cost, traced from the invoice to the team that caused it.
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