CtrlSpend maps every AI dollar to the customers, projects, and agents behind it. Get the margin picture your vendor dashboards never show.
Annual contracts only. No self-serve. Every customer onboarded personally.
Margin Contribution
Last 30 days · by customer
Total spend
$39,630
Weighted margin
54%
Customer
Spend
Margin
Acme Corp
$14,220
82%
Northwind Ltd
$11,880
61%
Contoso Holdings
$8,410
18%
Vertex Analytics
$5,120
47%
$2.52T
Global AI spending in 2026 (Gartner)
483%
YoY growth in enterprise AI budgets
40–60%
of production LLM token spend is pure waste
5–10×
Typical AI cost underestimation, pilot to production
The Problem
Horizontal FinOps tools show you totals. Dev observability tools show you traces. Neither tells the CFO whether each customer is actually profitable.
Your vendor dashboards show spend by model or endpoint. Your CFO needs spend by customer, project, and agent. Nobody tracks that today.
You know what you spent on tokens. You don't know how that compares to what you charged the customer. A lot of AI-first SaaS is running near-zero gross margin without realizing it.
Reruns, retry loops, oversized models. Together they add up to 40–60% of production LLM spend, all of it recoverable. Nobody surfaces that in dollar terms.
The Platform
Everything a CFO and a VP Engineering need to answer the same question without contradicting each other: which of our customers is actually profitable?
Wrap your OpenAI, Anthropic, or Google client once. Every call inherits customer, project, and agent context automatically. Zero per-call code changes, zero added latency.
Quadrant view of revenue vs margin per customer. Surface unprofitable customers, at-risk revenue, and margin trends in one screen. This is the CFO demo.
Cost per agent run, cost distribution, expensive-run alerts. Prove which agents are cost-efficient and which are burning your margin on retries.
Identical prompt repetition, retry loops, prompt bloat. All surfaced as dollars saved, not error counts. Typical customer finds $20K to $50K per month of waste in month one.
Where can you move workloads from frontier to smaller models without quality loss? We show you, with ROI math attached. Not just "try a smaller model."
Route through LiteLLM, Bifrost, or Portkey? One line of config captures every provider behind the gateway. No SDK install required.
For AI-native teams
Every AI-native SaaS is about to make the same call: which workloads move to smaller, specialized, or fine-tuned models? That decision needs per-workload cost, per-customer margin, and evidence that the frontier has stopped improving the task. That data is what CtrlSpend is built to produce.
Not every workload benefits from post-training. Attribution surfaces the volume, cost, and flat-frontier-improvement combo that actually justifies the engineering effort.
A post-training program costs $1M to $2M per year at scale. Our right-sizing engine models expected savings against team and compute cost, so you know if the math works before you hire the team.
Every quarter, frontier labs raise prices, change terms, or restrict access. Attribution tells you which workloads to specialize first as a hedge, and which to leave on the frontier.
Integrations
Native connectors for the frontier providers plus first-class gateway support. One integration with your existing LiteLLM setup covers your entire vendor list.
Available now
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Enterprise-ready
Security reviews, legal contracts, and compliance documentation, ready before your first call with InfoSec.
Every vendor credential is encrypted at rest with a random IV per write. Keys are never stored in plaintext, ever.
Supabase RLS ensures no query can leak cross-organization data. Enforced at the database layer, not just the app.
SOC 2 Type I planned alongside the first Enterprise deal, Type II following in year two. Security questionnaire available on request.
Native SAML 2.0 via Clerk. Works with Okta, Azure AD, Google Workspace, and any SAML-compliant IdP.
Data Processing Agreement available for every enterprise customer. Signed before data access begins.
Every customer gets a named implementation contact. No ticket queue. We're on-call for go-live.
We connect to your LLM stack live and show you your actual per-customer margins in the first call. Your numbers. Your customers. Your data.