Agentic Spatial Pathologist Commercialization Plan#
This document outlines a practical way to commercialize spatho while keeping a strong academic and non-commercial entry point.
Reference Pattern: CyteType#
CyteType is a useful benchmark because it separates free academic access from paid commercial access instead of forcing a single licensing mode.
Public documentation currently states:
academic and non-commercial use is free
free use is capped at three annotation runs per day
users can also bring their own LLM API keys or run local models
commercial use is licensed with an annual fee plus a usage-based fee
enterprise options include isolated or on-prem deployments
Reference:
Recommended Spatial Pathologist Edition Split#
1. Academic Community Edition#
Goal:
maximize adoption in academic labs
make methods reproducible
let users run locally with their own OpenAI key or local models
Recommended scope:
local CLI and Python package
built-in organ packs such as
lungandbreastworkflow JSON templates
HTML reports
artifact manifests
community support only
Recommended license approach:
keep the public code under a non-commercial research license
require commercial users to obtain a separate commercial license
Operational model:
software itself is free for academic and non-commercial use
API spend is paid by the user through their own OpenAI account
no service-level guarantee
2. Commercial Team Edition#
Goal:
sell productivity and governance, not just source code
Recommended scope:
commercial-use license
managed updates and validated builds
private organ packs and customer reference packs
shared reports and review dashboards
organization settings, role-based access, and audit logs
support and onboarding
Pricing model:
annual platform fee per organization or team
usage-based fee per analyzed case or per annotated cluster bundle
optionally allow bring-your-own OpenAI key for customers who want direct model billing
3. Enterprise Edition#
Goal:
support clinical-adjacent and regulated environments
Recommended scope:
private cloud or on-prem deployment
customer-managed OpenAI or local LLM credentials
zero-retention data paths
longer report retention controls
SSO, audit export, and procurement support
Pricing model:
larger annual contract
implementation / deployment fee
optional premium support SLA
Why This Split Fits Spatial Pathologist#
Unlike a purely algorithmic library, spatho has a real marginal cost when users rely on managed LLM inference.
That means a good product split is:
free software for academic self-service users
paid convenience, governance, support, and managed inference for commercial users
This avoids blocking academic adoption while still creating a business model.
Recommended Technical Architecture for Editions#
To support both free and paid editions cleanly, the codebase should separate four concerns:
Core workflow engine Local workflow orchestration, organ packs, schema validation, and artifact manifests.
Provider layer OpenAI, Anthropic, and local-model adapters behind a stable interface.
Entitlement layer Edition flags such as community vs commercial vs enterprise.
Service layer Optional hosted APIs, team workspaces, report storage, billing, and admin UI.
Concrete Product Recommendation#
The cleanest product packaging is:
spathoCommunity Edition Local package and CLI, non-commercial, BYO API key or local models.spathoCommercial Licensed commercial build and support, optional managed OpenAI billing, optional hosted collaboration layer.spatho EnterprisePrivate deployment, customer-managed credentials, governance and security controls.
Suggested Next Business Steps#
Keep the local CLI and organ-pack workflow public and non-commercial.
Add provider abstraction so commercial customers can choose managed inference or BYO key.
Add token and job metering at the case, cluster, and structure-review levels.
Add authenticated report storage and organization-level access control.
Define a commercial license and order form separate from the public repo license.