AI Unit Economics Platform
Your AI spend
is a black box.
SpendLens makes it measurable.
SpendLens is the AI unit economics platform for AI-native companies — revealing the true cost and margin of every feature, workflow, and customer interaction.
Measure • Attribute • Control AI spend
94%
of IT leaders lack AI cost visibility
$86K
avg monthly AI spend per company in 2025
200x
cost variance: unoptimized vs optimized AI
30 min
from install to AI unit economics
AI Unit Economics
Spend translated into product economics
Total AI Spend
$64,117
+18.4% vs last month
Feature Margin
62%
Healthy paid-tier mix
Runway to Budget
9 days
Alert threshold active
Feature Economics
Cost / margin by workflow
AI Support Agent
$0.07 / ticket
+41% margin
Customer Onboarding
$0.43 / user
+19% margin
Product Copilot
$0.12 / session
-8% margin
Customer Tier Economics
Business outcomes, not tokens
Free Tier
$0.18 / user
Negative
Pro Tier
$0.42 / user
Profitable
Enterprise
$1.92 / seat
High margin
SpendLens connects provider spend to features, customers, and margin — so teams can understand the true economics of AI.
The Problem
AI adoption is outpacing AI economics.
Companies are embedding AI across every product surface — search, copilots, support, onboarding, analytics, and internal workflows.
But the infrastructure to understand the economics of those AI features does not exist.
Finance teams cannot explain the spend.
Engineering teams cannot predict it.
Boards are asking questions nobody can answer.
01 —
No Single Source of Truth
Companies run models across OpenAI, Anthropic, Bedrock, Azure, and open-source inference. Each platform has different token models, pricing structures, and billing cycles. Teams are forced to reconcile multiple dashboards manually. There is no single place to understand what AI actually costs.
02 —
Tokens ≠ Business Outcomes
Existing tools show token counts and aggregate dollar totals. They cannot answer the questions that matter:
- What does it cost to power an AI feature?
- What does it cost to serve a customer?
- What does it cost to onboard an enterprise account?
03 —
Runaway AI Spend Surprises
A prompt regression, a viral product moment, or a misconfigured agent loop can generate a five-figure bill overnight.
Most companies discover these problems after the invoice arrives.
There are no guardrails that control AI spend at the feature, customer, or agent level.
Most companies discover these problems after the invoice arrives.
There are no guardrails that control AI spend at the feature, customer, or agent level.
04 —
Agentic Complexity Is Coming
The next generation of AI products will be built on multi-agent systems.
Agents call other agents.
Tools call models.
Workflows cascade across multiple providers.
Costs become non-linear and impossible to predict. The gap between AI adoption and AI economics is widening rapidly.
Agents call other agents.
Tools call models.
Workflows cascade across multiple providers.
Costs become non-linear and impossible to predict. The gap between AI adoption and AI economics is widening rapidly.
$86K
Average monthly AI API spend per company in 2025. Up from $48K in 2024. Most companies still have no FinOps tooling for AI.
94%
of IT leaders report inadequate visibility into AI costs. Finance teams cannot explain the spend. Engineering teams cannot predict it.
200x
Cost variance between unoptimized and optimized AI deployments. The difference between unmanaged and optimized AI systems can be two orders of magnitude.
The Solution
The three pillars of the new category.
AI Unit Economics Infrastructure. SpendLens gives companies the infrastructure to measure, understand, and control the economics of AI products.
Unified AI Cost Intelligence
SpendLens creates a single normalized cost ledger across your AI stack — reconciling provider pricing, billing models, and usage in real time.
- Native integrations with the 6 major LLM providers
- Normalized token pricing across providers
- Real-time spend monitoring and anomaly detection
- Finance-ready exports (CSV / PDF)
- Sub-minute cost visibility via proxy layer
AI Unit Economics
SpendLens connects model usage to the business layer of software — so teams can understand the real cost of AI features, workflows, and customers.
Tag by
- Feature
- Customer tier
- Product workflow
- Team or service
Answer questions like
- What does it cost to run our AI support agent?
- What does it cost to onboard a new customer?
- Which AI features generate margin — and which lose money?
- Where are AI costs concentrated across the product?
AI Spend Guardrails
SpendLens provides real-time guardrails that prevent runaway AI costs before they become five-figure surprises.
- Budget policies by provider, feature, team, or customer tier
- Burn-rate forecasting and budget projections
- Hard spend limits with automatic throttling
- Slack and email alerts with 5-minute setup
- Monthly finance summaries for leadership
How It Works
From install to AI unit economics in 30 minutes
Connect AI Providers
Add your API keys. SpendLens ingests billing data and normalizes pricing across providers — giving you a unified AI cost ledger within minutes.
Install the SDK
Add a single import to your existing LLM client. The SpendLens SDK wraps your current AI calls automatically — no refactoring required.
Tag Features & Workflows
Attach feature, workflow, or customer tags to model calls. SpendLens begins building a real-time map of cost per feature, workflow, and customer segment.
Set Guardrails
Define budgets and policies across your stack. Receive alerts instantly — or automatically throttle runaway processes before they generate surprise bills.
Pricing
SpendLens scales with your AI footprint
Start small and scale as your AI infrastructure grows.
Starter
$149
/ month · up to $10K AI spend
- Up to 3 provider connections
- Unified cost dashboard
- 30-day data history
- Email alerts
- Basic feature tagging (5 tags)
- CSV export
Most Popular
Growth
$499
/ month · up to $50K AI spend
- All 6 provider connections
- Full feature attribution SDK
- Unlimited tags & segments
- 90-day data history
- Slack + email alerts
- Feature-level budget guardrails
- Burn-rate forecasting
- CFO-ready PDF reports
Scale
$1,499
/ month · $50K+ AI spend
- Everything in Growth
- Unlimited data history
- Multi-team workspaces
- Cost anomaly detection
- Agentic workflow cost tracing
- SSO / SAML
- Dedicated Slack channel
- Custom contract available
Platform Roadmap
Building the infrastructure for AI unit economics
Now
Core AI cost intelligence
- Multi-provider cost aggregation
- Unified AI spend dashboard
- Feature-level cost attribution
- Real-time anomaly detection
Coming Soon
AI unit economics & guardrails
- Feature and workflow cost attribution
- Budget guardrails and burn-rate forecasting
- Customer-level AI cost tracking
- CFO-ready reporting
Future Platform
Infrastructure for AI-native companies
- Agentic workflow cost tracing
- Automated cost optimization recommendations
- AI spend simulation & forecasting
- Deeper provider integrations
The Team
Built by people who lived the problem
The SpendLens team combines experience in AI infrastructure, enterprise SaaS, and developer platforms — the three disciplines required to build the infrastructure layer for AI unit economics.
Founder / CEO
Product · GTM · Vision
10+ years building AI and SaaS platforms. Previously led product at an AI infrastructure company scaling LLM workloads to 50M+ requests per month, where the team encountered firsthand the lack of tools to understand the economics of AI systems. SpendLens was created to solve that problem.
Engineering
Infrastructure · Distributed Systems
The engineering team is building the core platform powering SpendLens including real-time cost ingestion, provider normalization, and feature-level AI economics attribution. The platform is designed to support high-volume AI workloads across multiple model providers.
Advisors
Strategic Guidance
- FinOps platforms
- AI infrastructure
- Developer-led growth
- Enterprise SaaS
The AI bill
is coming.
Own the layer that governs it.
SpendLens helps AI-native companies understand the true economics behind every feature, workflow, and customer.
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