Every AI pricing decision is a bet. Know the math before you ship.
- Track what your AI features actually cost to serve — per user, per request
- See margins by customer — and costs by model, feature, and any dimension you define
- Get the per-user cost data that turns pricing decisions into strategies
Your AI bill went from $1K to $40K. Do you know what drove that?
Adoption is growing across your product — but your cost-to-serve is shifting with it. Which customers drive the most cost? Which features? Which models? Whether you're bootstrapped counting every dollar or VC-backed defending numbers to your board, granular cost data turns a growing bill into a defensible pricing strategy. Bear Lumen provides that granularity.
Defend your AI pricing strategies with real data — price with confidence
Per-user cost-to-serve is invisible
You charge $49/seat. Does that seat cost $10 or $100 to serve? Without per-feature cost data, pricing is based on assumptions — and assumptions compound over time.
Per-feature, per-user cost breakdown from day one. See exactly what each AI capability costs to serve — and adjust pricing when cost-to-serve shifts.
Your biggest customer might be your worst margin
Some customers cost more to serve than they pay. On flat-rate plans, heavy users are subsidized by lighter users — but aggregate data obscures which is which.
Contribution margin by customer and segment. See who is profitable, who is underwater, and which pricing tiers reflect real per-user cost-to-serve.
AI costs evolve — new customers and AI infra changes impact prices every month
New models, new customers, new usage patterns. What was profitable last quarter may not be today. Every new customer and infrastructure change shifts your per-user cost-to-serve.
Continuous cost tracking with visibility into cost shifts as they happen. See a feature trending margin-negative before it compounds — not after.
Pricing decisions need granular data
Your AI bill doubled this month. Why? Which customer caused it? Which feature? Stay the course or change pricing? These decisions require per-user, per-feature unit economics — not spreadsheet estimates.
Automated unit economics from usage data. See exactly why a bill spiked, which customer drove the cost, and whether your pricing still works — the granularity that turns reactive billing into proactive pricing.
How it works
Cost Intelligence
One SDK call per AI request. Auto-detects provider and extracts usage data — cost is calculated automatically from our rate cards. Works pre-revenue — no billing or customers required. Keep your existing stack; Bear Lumen adds the cost layer.
See cost trackingMargin Intelligence
When revenue flows, see contribution margin by customer — and cost breakdown by model, feature, and any custom dimension you define. See which customers are profitable and which are underwater.
See margin analyticsPricing Intelligence
Per-user cost data, margin breakdowns by customer segment, and granular unit economics — the numbers that turn pricing estimates into informed decisions. The same data pipeline that tracks costs powers pricing clarity.
See pricing intelligenceKnow what to charge — with data, not assumptions
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From the founder
AI companies use COGS estimates, margin targets, and tier assumptions to set pricing. Calibrating pricing is not simple, but sacrificing runway has its limits when AI costs are variable by nature. We built Bear Lumen to enrich the pricing data companies already have and expose hidden cost absorbers so they have more runway to sustain against the high AI demands their customers have. Track what AI features actually cost per user, see margins by customer and model when revenue starts flowing, share that data across teams, and model whether pricing aligns with the value delivered — all from the same data pipeline. Start with cost tracking. The rest follows when you need it.
— Blaise, Founder @ Bear Lumen