No Customer-Level Costs
You see which models are expensive, but not which customers. Is Customer A profitable at $50/mo when they cost $80 in LLM calls?
Real-time team attribution and cost analysis
The only LLM cost platform that shows cost per customer, not just cost per model.
Know exactly which customers are profitable and which are costing you money.
Real-time team attribution and cost analysis
Integrates with your favorite AI providers
You track everything else. Why is AI spend still a mystery?
You see which models are expensive, but not which customers. Is Customer A profitable at $50/mo when they cost $80 in LLM calls?
You find out you blew budget after it happens. No ML-powered predictions. No month-end projections until it's too late.
Setting budgets is easy, enforcing them isn't. Teams exceed limits with no automatic throttling or blocking. You're left playing bad cop.
The only platform built for finance teams, not just developers
See exactly which customers are profitable and which are costing you money. No other tool does this.
Set budgets and never overspend again. Automatic enforcement with soft and hard limits.
Predict your month-end LLM bill with confidence. Know what you'll spend before it happens.
OpenAI, Anthropic, Google, Azure - unified cost tracking across every LLM provider.
Catch runaway costs instantly. Get alerts before small issues become big bills.
OpenAI, Anthropic, Google AI unified. One dashboard for all your AI costs.
No SDK. No code changes. Just upload and see.
Download usage data from OpenAI, Anthropic, Google AI, or Azure OpenAI.
We auto-detect teams, calculate costs, and find optimization opportunities.
Team breakdown, forecasts, savings opportunities, and budget recommendations.
Our SDK wraps your existing OpenAI/Anthropic calls with zero code changes. Get instant team attribution, cost tracking, and budget enforcement.
# Replace your OpenAI import
from substacker import OpenAI
# That's it! Same API, instant cost tracking
client = OpenAI(
api_key="sk-...",
team="engineering" # Auto-attribute costs
)
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Hello!"}]
)Built for Finance teams and FinOps, not just developers
| Feature | Substacker | Langfuse | Helicone |
|---|---|---|---|
| Cost Per Customer | ✓ Built-in | ✗ | ✗ |
| Profit Margin Analysis | ✓ Per Customer | ✗ | ✗ |
| Budget Enforcement | ✓ Policy Engine | ✗ | Basic |
| Cost Forecasting | ✓ ML-Powered | ✗ | ✗ |
| Multi-Provider View | ✓ Unified | ✓ | ✓ |
| Finance System Export | ✓ CSV/API | Limited | ✗ |
| Primary Buyer | Finance/FinOps | Developers | Developers |
Start free. Upgrade when you need forecasting and enforcement.
One-time analysis
For growing teams
For scaling companies
See what our customers are saying
"Our AI bill went from $5K to $47K in 3 months. We had no idea which team was responsible until Substacker showed us Engineering was 78% of spend."
"The SDK integration took 10 minutes. Now we have real-time cost attribution per team without changing any of our existing OpenAI code."
"Budget enforcement saved us from a $12K overage last month. The alert came at 80% and we were able to throttle non-critical workloads."
Everything you need to know about Substacker
Cost Per Customer shows exactly how much each customer costs you in LLM calls. If Customer A pays you $50/mo but costs $80 in API calls, you're losing $30/mo. No other tool tracks this - they only show cost per model or per team. This is the missing piece for understanding unit economics and profitability.
They're developer observability tools for debugging prompts and tracing requests. We're a finance tool for cost accountability. Different buyer, different use case. We add Cost Per Customer, profit margin analysis, ML-powered forecasting, policy enforcement, and finance system integration - features designed for FinOps and Finance teams, not developers.
Yes. For free analysis, we process and delete data immediately after generating your report. For paid plans, data is encrypted at rest and in transit. We never see or store your prompts - only metadata like model, tokens, and team identifiers.
We auto-detect teams from API keys, email domains, customer IDs, or metadata patterns. Most companies get 80%+ attribution automatically. For the rest, you can add custom tags or we help you set up team identification.
We use ML-based time series analysis with exponential smoothing and seasonality detection. Based on your historical usage patterns, we project month-end spend with confidence intervals. You get early warnings before you exceed budget.
You set limits per team (e.g., Engineering: $5,000/month). When approaching limits (70%, 90%), you get alerts via Slack or email. When hitting limits, you can auto-throttle requests, send warnings, or hard-block. Our SDK proxy handles enforcement in real-time.
Upload your usage data and see exactly where your money is going.
Free forever tier available. No credit card required.