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Pricing Philosophy

The pricing model must balance three goals:
  1. Low barrier to entry — Small exporters must be able to try the platform without financial risk.
  2. Alignment with AI cost — AI discovery queries generate real cost through:
    • LLM calls
    • search APIs
    • enrichment processing
    Pricing must reflect usage.
  3. Simple purchasing decision — Exporters should understand pricing within seconds.
Therefore the model chosen is: Hybrid SaaS + Credit Model

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Why Not Pure Subscription?

Many SaaS platforms offer unlimited usage subscriptions. This model does not work well for AI products. Example problem:
heavy users generate massive AI costs
For example:
usersearches
light user3 per month
heavy user200 per month
Unlimited pricing would create large losses. Credits solve this problem.

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Why Not Pure Credits?

Another option is a pure credit system. However this creates friction:
Users constantly think about cost.
Example:
“Should I spend this credit?”
This reduces product usage. A hybrid model solves this.

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Hybrid Pricing Model

The chosen structure: 
Base subscription
+
Usage credits
This provides:
  • predictable revenue
  • controlled AI cost
  • low psychological friction

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Plan Structure

Three plans are sufficient.
plantarget user
Freetrial users
Proindividual exporters
Teamexport teams
Keeping the number of plans small improves conversion.

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Free Plan

The purpose of the free plan is activation, not revenue. Limits (updated April 2026 — R0-8 decision):
featurelimit
searches5 per month
companies per search10
contact reveals0 (completely gated — Pro required)
saved leads10
card scans5
Goal: Deliver the wow moment through discovery results — company names, types, FitScore — without giving away the key (contact reveal).

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Pro Plan

This is the core commercial plan. Example limits:
featurelimit
searches50
companies per search25
contact reveals100
saved leadsunlimited
card scansunlimited
Additional features:
  • AI company analysis
  • follow-up reminders

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Team Plan

For small export teams. Additional features:
featuredescription
multi userteam accounts
shared leadscommon workspace
activity historyinteraction tracking
higher limitsmore credits
Team collaboration becomes important once exporters scale.

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Credit Usage Model

Credits represent AI compute consumption. Example actions:
actioncredits
buyer discovery1
contact reveal1
deep company analysis2
market intelligence search3
This structure ensures: AI costs remain manageable.

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Credit Packs

Users can purchase additional credits. Example packs:
packcredits
small50
medium200
large1000
Credits allow power users to scale.

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Pricing Psychology

Exporters think in terms of:
“How many leads can I generate?”
Pricing should therefore feel like:
“lead generation capacity”
not AI usage.

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SaaS Unit Economics

Example cost breakdown per discovery search:
componentcost
LLM queries$0.02
web search APIs$0.03
data processing$0.01
Total cost: ~$0.06 per search With a 1-credit price equivalent to 1–1–2 value, margins remain strong.

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Expected Usage Patterns

Typical exporter usage:
user typesearches/month
light user10
medium user30
heavy user80
This allows predictable infrastructure scaling.

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Lifetime Value Potential

Revenue per plan (April 2026 confirmed pricing):
planmonthly
Pro49(49 (39/mo annual)
Team149(149 (119/mo annual)
Expected customer lifetime: 24–36 months This creates strong LTV potential.

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Revenue Expansion

Additional revenue sources include:
  • extra credits
  • team seats
  • intelligence modules
  • enterprise features
These expansions increase ARPU.

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Monetizing Export Intelligence

Future premium features include:
featureprice potential
market discoverypremium
tariff intelligencepremium
competitor analysispremium
These features target advanced exporters.

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Pricing Summary

The final pricing architecture: 
Free plan → activation
Pro plan → core revenue
Team plan → expansion
Credits → usage scaling
This combination provides a sustainable SaaS model.