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Open-Source Large Model Overseas Revenue-Sharing Licensing

1) Commission based on a percentage (e.g., 30%) of model inference token usage or downstream customer revenue, collected

MODEL

Key Fields

FIELD STAMPS

IndustryAI / LLM

RegionGlobal

ScaleGiant

ChannelOnline

📌 Background

In 2026, open-source large models are shifting from free to commercial licensing: Moonshot is reportedly in talks with cloud giants like Microsoft, Amazon, and Google regarding revenue sharing for the K3 model, with a commission rate of up to 30% (according to media reports, unverified). The billing point is also shifting from input-side licensing fees to output-side revenue sharing based on inference token usage or a percentage of downstream revenue. This shift is supported by the willingness of overseas cloud providers to pay, with related AI spending growing by 47% annually (based on public data). Resistance from the open-source community and self-developed alternatives by cloud providers are two major hurdles.

👤 Target Customers

Cloud providers, enterprise developers, and application developers. The payers are cloud platforms or enterprises that use the models and generate commercial revenue.

💰 Revenue Streams

1) Commission based on a percentage (e.g., 30%) of model inference token usage or downstream customer revenue, collected from cloud providers; 2) Licensing fees charged based on API call volume; 3) Fixed annual licensing fees obtained through custom deployment contracts with key enterprise clients.

🧮 Cost Structure

Computing power and data costs for model training; business development (BD) and legal compliance costs for negotiations with cloud providers; open-source community maintenance and technical support costs.

🛡️ Moat

Bargaining power derived from leading model capabilities; lock-in effect of leading open-source model ecosystems and developer communities; de facto standards formed through deep integration with global cloud providers.

🔑 Keys to Success

Build differentiated high-performance benchmark models to secure bargaining power
Deepen exclusive partnerships with cloud providers to form binding exclusivity
Design flexible tiered commission structures to match clients of varying scales

⚠️ Risks

Collaboration interruption caused by negotiation breakdowns
Damage to open-source community distribution and reputation
Significant loss of bargaining power if model performance is surpassed

🏢 Cases

Moonshot's revenue-sharing negotiations for the K3 model with Microsoft, Amazon, and Google
Multiple open-source model vendors exploring commercialization paths based on token revenue sharing

📊 SWOT Analysis

Strengths

Deep technological leadership advantages in open-source models
Broad cloud provider channel coverage with an asset-light, high-gross-margin revenue-sharing model

Weaknesses

High uncertainty due to reliance on cloud provider negotiation outcomes
Resistance within the open-source community toward commercialization

Opportunities

Strong willingness of overseas cloud providers to pay, with AI spending up 47% year-over-year
Mature validation of the token economy, with the revenue-sharing model accepted by the market

Threats

Numerous open-source alternatives intensifying model commoditization and competition
Risk of replacement by cloud providers' self-developed models, making revenue-sharing negotiations prone to breakdown

Sources