Know which AI features, customers, and plans are actually making money.
Metron connects every dollar of model spend to the features, customers, and pricing plans that generate it — and surfaces the economics that decide whether your AI business is healthy.
- 5
- Economic dimensions
- 8+
- AI providers, normalized
- 2 lines
- To attribute every call
Monthly AI spend
Gross margin
Healthy features
WatchUnderwater accounts
Margin-negativeFeature margin
$ / active user
- Chat assistant$2.10
- Onboarding AI$3.40
- Semantic search$7.80
- Doc summarization$16.00
Built for the moment AI cost stops being acceptable — Series A through Series C, $10K–$500K per month in spend.
- Series Afirst real bill
- Series Bmargin scrutiny
- Series Cboard questions
- AI-native SaaScore product
- Vertical AIthin-margin
- Dev tools w/ AIusage growth
The problem
Three teams. Three answers. None of them complete.
Finance, product, and engineering each see a piece of the puzzle. Nobody has assembled it — because no tool connects AI spend to the business objects that give it meaning.
“AI spend is up 95% YoY. Margin is compressing. The board is asking why and we don't have a clean answer.”
“Inference is growing because usage is growing. That's the product working as intended.”
“We can't slow down the most-loved feature in the product because finance is nervous about a line item.”
Each one is right. None of them is enough. The pieces don't connect because nothing connects AI spend to the features, customers, and plans that generated it.
Why Metron
Cost dashboards tell you what you spent. Metron tells you whether your AI business is working.
Provider dashboards show tokens. FinOps tools show infrastructure. Observability platforms show traces. None of them know who your customer is, what plan they're on, what feature generated the cost, or whether the economics make sense. That's the gap Metron closes.
- Provider dashboardsToken usageMargin per feature
- FinOps toolsInfrastructure allocationCustomer profitability
- AI observabilityPer-request tracesPlan economics
- SpreadsheetsQuarterly snapshotContinuous economic view
Product overview
Five dimensions of AI economic visibility, in one platform.
Each dimension answers a different question your finance, product, or engineering lead is being asked right now.
Feature Economics
Which features are making money. Which are quietly destroying margin.
- Margin health: healthy, watch, at-risk, margin-negative
- Cost trend over time per feature
- Cost per active user by plan tier
Customer Profitability
Which accounts are underwater — before the renewal conversation, not after.
- Per-account AI cost and revenue
- Underwater detection at configurable thresholds
- Top cost-driving features per customer
Plan Economics
Whether your pricing actually matches how AI gets used.
- Contribution margin by plan
- Std deviation reveals dangerous tails
- Overconsumption patterns by tier
Waste & Savings
Specific, quantified, actionable. Not generic recommendations.
- Six finding types, each with a dollar estimate
- Remediation path included
- Backed by your historical output quality
Forecasting & Scenarios
Model the unit economics before you ship the feature, not after.
- Feature rollout scenarios
- Pricing and plan-restructure modeling
- Per-plan unit economics under growth
See how each dimension is computed.
Read the product pageFeature highlight · Waste & savings
Six finding types. Each one specific, quantified, and action-ready.
Metron's waste detection isn't generic recommendations. Every finding ties to your actual usage, an estimated dollar impact, and a concrete remediation path.
Model overkill
Frontier models running tasks where an 8× cheaper model produces equivalent outputs.
Batch eligibility
Workflows running synchronously with no real-time requirement — eligible for 50% batch pricing.
Prompt caching gaps
High-volume calls with large repeated system prompts paying for the same tokens over and over.
Orphaned spend
API keys generating cost with no feature attribution, no owner, and no clear product purpose.
Staging on prod models
Development and test environments running frontier models. Staging should never burn production tokens.
Free-tier subsidy
Free users consuming AI-heavy features at a rate that generates negative margin with no conversion path.
How it works
Four sources. One unified economic model.
Metron ingests four data types and connects them into a single view of AI economics. Most customers reach a usable view in 1–2 days.
- Source 01
Cost data
API spend from OpenAI, Anthropic, Vertex, Bedrock, Cohere, Together, Replicate, and self-hosted inference — normalized into one schema.
- Source 02
Product data
Feature usage events via the Metron SDK or your existing analytics layer — the bridge from a token to the feature that generated it.
- Source 03
Customer data
Account records, plan assignments, and revenue from Stripe, Chargebee, your CRM, or upload — connecting cost to customer.
- Source 04
Pricing data
Plans, contracts, renewal dates, and usage commitments — the commercial structure that decides whether a cost is acceptable or dangerous.
Unified economic model
Every token tied to a feature, a customer, and a plan.
The attribution layer joins cost, product, customer, and pricing data into one model — the substrate every Metron view runs on.
Output views
- Feature margin
- Customer profitability
- Plan economics
- Waste findings
- Forecasts
Example
A real Series B story, in four findings.
An AI SaaS company watches its OpenAI bill go from $41K to $80K in three months. Finance asks why. Engineering and product disagree. Within a week of plugging Metron in, four facts surface — each one already sitting in the data, none of them previously visible.
March OpenAI bill
Revenue growth, same period
Annualized findings
- 01
Free-tier subsidy
Margin-negativeFree users invoke summarization 47× per month at $0.34 each. The AI cost of an engaged free user is $16/mo — more than the gross margin contribution of a Pro subscriber.
AI cost / free user / mo
$16
Recommended action
Cap or gate summarization on free tier
- 02
Underwater enterprise accounts
Margin-negativeThree accounts paying a combined $180K/yr are consuming $310K/yr in inference. Priced 18 months ago, before summarization shipped. Renewing in four months.
Projected annual loss
$130K
Recommended action
Reprice before renewal — surface to AE
- 03
Model overkill on search
Watch73% of semantic-search queries are simple keyword lookups. Currently routed through GPT-4o. An 8× cheaper model produces equivalent outputs on the customer's own historical examples.
Estimated search spend
−61%
Recommended action
Tiered model routing on simple queries
- 04
Healthy chat economics
HealthyChat assistant uses prompt caching and tiered routing. Margin-positive at every plan level. The pattern can be templated across other features.
Margin vs. plan avg.
+38%
Recommended action
Treat as the reference architecture
Composite based on patterns Metron is built to surface. Numbers are illustrative, not from a real customer.
Pricing
Pricing scales with AI spend under management.
Annual contracts at 20% off. 14-day trial available on Growth and Scale, no credit card required.
Growth
Up to $30K/moFor early-stage teams getting their first clean view of feature economics.
- Feature economics
- Waste detection
- Cost & margin alerts
- Up to 5 seats
Scale
Up to $150K/moFor Series A–B teams managing real customer concentration and plan complexity.
- Everything in Growth
- Customer profitability
- Plan economics
- Up to 15 seats
Enterprise
Up to $500K/moFor Series B–C teams with board-level margin scrutiny.
- Everything in Scale
- Forecasting & scenario modeling
- Custom integrations
- Unlimited seats
Custom
$500K+/moFor platforms with warehouse connectivity, governance, and proxy-mode requirements.
- Everything in Enterprise
- Warehouse connectivity
- Proxy mode & budget enforcement
- Dedicated solutions architect
ROI
A Scale customer at $1,200/mo has $150K/mo in spend under management. Surface a single $50K mispriced enterprise account before renewal and Metron pays for itself for three and a half years.
FAQ
Common questions, short answers.
The full list — product, pricing, integrations, security, implementation — lives on the FAQ page.
Get started
Stop guessing what your AI economics look like.
Bring finance, product, and engineering onto the same page — with the data they each need to make the next decision.