Canopy Spend - AI Infrastructure ROI Tracker
Helps software companies measure, justify, and optimize returns on hyperscaler AI infrastructure spending
Software companies are spending millions on AI infrastructure (GPU instances, LLM APIs, model training) across AWS, Azure, and GCP but have no way to systematically measure whether that spend generates financial returns. CFOs and FinOps teams lack tooling to connect AI infrastructure costs to business outcomes like revenue lift, margin impact, or productivity gains.
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The Business
$3-6B
Market Size
$2K-$8K
USA
Highest Potential
Customer
CFOs, VP Finance, and FinOps leads at Series C+ software companies ($50M-$5B revenue) spending $500K+ annually on AI/ML workloads across major cloud providers.
Pricing
Tiered SaaS subscription based on monthly AI infrastructure spend under management. Starter tier at $2,000/mo (up to $1M managed spend), Growth at $5,000/mo (up to $5M), and Enterprise at $8,000+/mo with custom integrations, SSO, and dedicated support. Expansion revenue from optimization recommendations that demonstrate measurable savings.
$48.0M
Estimated Annual Revenue
2,000 customers at $2,000-$8,000/mo
10% market capture
Features
Connects to AWS, Azure, and GCP billing APIs to automatically pull and normalize cost data across all accounts and regions.
Uses heuristics and ML to identify and tag AI-specific spend (GPU instances, SageMaker, Vertex AI, OpenAI API calls, etc.) from general cloud costs.
Maps tagged AI costs to configurable business KPIs (revenue per feature, cost-per-inference, user engagement lift) with drill-down by team, project, or model.
Produces executive PDF and slide-deck reports summarizing AI spend vs. returns with trend lines, benchmarks, and narrative summaries generated by LLM.
Detects unexpected spikes in AI infrastructure spend and sends Slack/email alerts with root cause analysis.
Suggests rightsizing GPU instances, switching inference providers, or consolidating underutilized models based on cost-to-value analysis.
Anonymized cross-customer benchmarks showing how your AI spend efficiency compares to industry peers.
What-if analysis tool for forecasting ROI impact of scaling up or cutting specific AI workloads.
Enterprise SSO integration with granular permissions for finance, engineering, and executive roles.
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Tech Stack
backend
Next.js API Routes
Server-side API layer handling cloud billing ingestion, data processing, and report generation
Supabase Edge Functions
Background jobs for scheduled billing data syncs, anomaly detection, and alert dispatching
hosting
Vercel
Frontend hosting with edge rendering for low-latency dashboard access globally
database
Supabase
Primary database for user accounts, project configs, tagged cost data, and KPI mappings with row-level security for multi-tenant enterprise data
ClickHouse Cloud
Columnar analytics database for high-volume billing data queries, time-series aggregations, and fast dashboard rendering across millions of cost line items
frontend
5 Day Sprint UI
Component library built on shadcn/ui and Tailwind for rapid dashboard and report UI development
Recharts
Data visualization library for cost trend charts, ROI attribution graphs, and anomaly detection displays
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Frequently Asked Questions
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