The Financial OS forAI agents
in production
One ledger for every dollar of AI spend. See where it goes, who owns it, what it produced, and what it returned. Stop the surprises before the invoice arrives.
RunrateWhy now
AI spend has outgrown the tools built to manage it.
Teams use AI faster than finance can track. AI bills arrive 30 days late. Tokens get expensed on personal credit cards. Agents run in production with no owner. And the workflows that do work are silently subsidizing the ones that don't — because nobody can tell them apart. The tools your team uses today were built before any of this existed.
The problem
The visibility gap compounds with every AI workload you ship.
The moment AI hits production — whether it is an autonomous agent, a coding copilot, or a SageMaker job a developer spun up — the visibility problem begins. And every new workload deepens it. The longer it runs unchecked, the more it costs you to keep — even when the workflow itself works.
No cost truth
You get a cloud bill, an LLM invoice, and a stack of SaaS subscription receipts. You don't get cost-per-ticket, cost-per-claim, cost-per-engineer, or cost-per-project. You can't tell which workflows are profitable and which are quietly bleeding margin.
Forecasting is guesswork
Agent spend is variable by design — it scales with business volume. Without unit economics, every forecast is a ceiling estimate with no floor.
Shadow AI is everywhere
Engineers expense Cursor, Claude Pro, and ChatGPT Plus on personal credit cards. Teams sign up for AI tools without IT or procurement involved. By the time finance sees the spend, it is sprayed across receipts, expense reports, and a dozen unmanaged accounts. There is no chargeback path and no audit trail.
Audit and controls lag
When a workflow goes off-budget by $40K — or a SageMaker instance runs through the holidays — nobody knows until the invoice arrives. There is no approval chain, no real-time alert, no immutable record of who approved what. By the time finance flags it, the money is gone.
Margin erodes inside working workflows
A workflow's invoice line is the smallest part of what it actually costs. Retries, fallback model calls, evaluator passes, RAG lookups, and shared infra triple the true unit cost — and they all sit outside the LLM bill. The workflow runs. Quality looks fine. The margin disappears anyway.
RunrateHow it works
Five steps from opaque to owned and earning.
Runrate connects every dollar of AI spend to the team that used it, the work it produced, the policy it must follow, and the return it generated.
Runrate ingests cost and usage data from every AI surface in your stack — LLM providers, cloud infrastructure, data and compute platforms, agent frameworks, and developer-facing AI tools. Connectors are read-only by default. Runrate extends your existing tagging taxonomy rather than replacing it. Every event is normalized into a canonical format before reconciliation.
The first hour after connection typically attributes 60–70% of the trailing 30 days of spend. Shadow tools you didn’t know about show up here.
We ingest from across your AI stack
More integrations available in design partner builds — tell us about your stack.
What it's for
See, govern, defend, and return on every dollar of AI spend.
Runrate gives the same control over digital labor that ERP gave over headcount.
For private equity operating partners: the same four pillars, applied across portfolio companies. Talk to us.
Full-stack AI cost visibility
One ledger across LLM providers, cloud infrastructure, data platforms, agent frameworks, and developer-facing AI tools. See where every AI dollar goes — from a Cursor seat to a SageMaker job to an autonomous workflow — without a custom integration project.
cross_stack_reconciliationWorkflow cost attribution
Reconcile AI spend across LLMs, cloud infrastructure, data platforms, and SaaS subscriptions — attributed to workflows, teams, projects, environments, work-item classes, and individual tool seats.
explainable_cost_allocationCost per completed unit
Understand what each resolved ticket, processed claim, completed case, or qualified lead truly costs once every cost source is reconciled. The unit economics view that lets operations leaders defend cost-to-serve to the CFO.
cost_per_completed_workflowShadow AI discovery
Surface every AI tool your organization is using — sanctioned, expensed, or self-procured. See engineers expensing Cursor on personal cards, teams self-procuring ChatGPT Plus, and copilots running without IT review. Bring shadow IT into the central ledger before it becomes a board issue.
shadow_ai_inventoryCost & latency hotspots
See which step, model, or tool is driving spend and response time inside every agentic workflow. Drill from a workflow to a step to a payload in two clicks. Replace the engineering-versus-finance "where is the money going" debate with a shared answer.
step_level_cost_waterfallVariance analysis
Break spend spikes into volume, unit cost, provider mix, and exception drivers. Tell finance not just that AI spend rose 12%, but why — was it more tickets, more retries, a model change, or a new workflow?
volume_x_unit_cost_varianceWhy Runrate
Nothing else is built for this.
Traditional FinOps tools were built for cloud infrastructure. Observability tools were built for engineers debugging traces. Neither was built to tell a CFO what the AI line item returned — and neither will. Runrate is built for the financial reality of AI in production — across agents, productivity tools, infrastructure, and data platforms.
FAQ