Revenue-Sharing Clauses for Open-Source LLMs: The Freemium Model of Qwen3.8-Max and Kimi K3
1) Primarily derived from revenue sharing, taking a fixed percentage of commercial income exceeding the threshold (e.g.,
Key Fields
FIELD STAMPS📌 Background
As the competition for open-source LLMs intensified in 2026, the high cost of computing power became difficult to cover through pure free open-source models, leading leading vendors to introduce revenue-sharing clauses. Alibaba Cloud's Qwen3.8-Max and Moonshot AI's Kimi K3, while open-sourcing their model weights, now require a percentage of revenue once commercial earnings exceed a certain threshold (e.g., $20 million), marking a shift from free to a freemium model for open-source LLMs.
👤 Target Customers
Enterprise clients deploying open-source LLMs commercially, including cloud providers, SaaS companies, and AI application developers; the payers are commercial users whose revenue hits the sharing threshold.
💰 Revenue Streams
1) Primarily derived from revenue sharing, taking a fixed percentage of commercial income exceeding the threshold (e.g., $20 million); 2) Incremental revenue through bundled cloud API calls and enterprise support services; 3) Threshold-based surcharges: a percentage of commercial revenue above the threshold, with cloud API calls and enterprise support billed separately based on usage.
🧮 Cost Structure
Model training compute costs, open-source community maintenance costs, legal and revenue-sharing accounting costs, and cloud infrastructure costs.
🛡️ Moat
First-mover advantage in model weights and ecosystem, creating a closed loop of open-source traffic acquisition and cloud monetization, with revenue-sharing clauses built upon the scarcity of LLM capabilities and brand trust.
🔑 Keys to Success
- Precisely design revenue-sharing thresholds and percentages to balance adoption rates with monetization
- Provide supporting cloud services and toolchains to naturally convert open-source users into paying cloud customers
⚠️ Risks
- Enterprises bypassing revenue-sharing oversight through private deployment
- Open-source community protests against commercial clauses leading to ecosystem fragmentation
- Regulatory constraints on technology exports and revenue-sharing settlements
🏢 Cases
- Alibaba Cloud Qwen3.8-Max launches a revenue-sharing program with a $20 million threshold
- Moonshot AI Kimi K3 introduces revenue-sharing clauses
📊 SWOT Analysis
Strengths
- Lowers the barrier to entry for enterprises, rapidly expanding the developer ecosystem and influence
- Provides a capital recovery channel for continuous model iteration
Weaknesses
- Revenue-sharing clauses may limit deep enterprise adoption, forcing some users to switch to completely free models
- Poorly designed thresholds and percentages can easily trigger community backlash
Opportunities
- Strong demand for enterprise-grade AI commercialization, allowing for bundling with cloud services and toolchain consumption
- Open-source model attracts global developers, facilitating expansion into overseas markets
Threats
- Substitution by other completely free or more open-source models
- Closed-source LLMs capturing commercial clients through superior performance