As data becomes an essential input for innovation and AI training, questions of how to sustain its equitable and responsible reuse have gained urgency. Beyond technical infrastructure and governance frameworks, the long-term viability of data commons depends on the institutional and financial models that underpin them. 

Over the last year, The Open Data Policy Lab collected over 60 examples of data commons that support public-interest AI. We analyzed different aspects of these commons including their governance structure, funding models, and the actors involved. We found that these initiatives are relying on an array of funding models ranging from philanthropic grants to government partnerships to donations to membership programs. 

In what follows, we curated 10 of the data commons gathered. For each example, we briefly summarize the main focus of the commons, the geographic region, the funding model, and how data is accessed. We selected these examples based on the breadth of funding models being used. They are intended to be exemplary, not comprehensive and are listed in alphabetical order. The latter part of this blog outlines several patterns from these examples and reflections on how the data commons landscape is evolving.

Data Commons Examples

Patterns

The examples above reveal that while most initiatives aspire to enable equitable data reuse, their underlying business and governance models vary significantly across contexts, reflecting differences in policy priorities, sectoral focus, and maturity of data ecosystems.

Three broad patterns emerge:

Government-led Models

Initiatives such as All of Us, AIDA Data Hub, MIDRC, and India AI Platform exemplify state-backed infrastructures, where governments act as both funders and conveners. These models benefit from long term funding commitments. For instance, All of Us was enabled through a 10-year funding commitment through the 21st Century Cures Act. They often combine public investment with partnerships across academia and industry, positioning the commons as a public good to be leveraged for research and innovation. Sustainability here depends on continued political and fiscal commitment rather than direct revenue streams.

Philanthropy and Hybrid Cooperation Models

Efforts like Mozilla’s Common Voice and GainForest.Earth illustrate philanthropic and multi-donor ecosystems where open data creation and stewardship are sustained by donations and grants. Such commons blend donor capital with community participation, aligning with the indirect benefit model identified in the literature: the value lies in mission fulfillment and ecosystem engagement rather than monetization.

Membership and Cooperative Models

OpenStreetMap, POSMO, and MLCommons demonstrate membership-based and cooperative financing, where users or contributors (including governments) become shareholders or members, effectively co-owning the commons. These models internalize governance and sustainability through collective ownership, echoing the freemium and razor-blade business archetypes where access is open but added services, tools, or governance privileges require contribution or fees.

Across these categories, the government’s role shifts from funder (All of Us) to facilitator (Mozilla) to partner (POSMO). This spectrum underscores that financial models alone do not define sustainability: legitimacy, trust, and shared value creation mechanisms are equally critical.

Reflections and Looking Forward

Building on the above and complementing it with other research and past experiences, several cross-cutting reflections and predictions emerge on how data commons may evolve robust, equitable, and sustainable business models:

1. From Cost Centers to Value Platforms

Traditional open data initiatives often operated under the assumption that releasing data was a compliance cost or public good with minimal return. The emerging paradigm reframes data commons as platforms for value creation, where investments yield operational efficiencies, innovation spillovers, and measurable social returns. This requires moving from legal obligation to operational necessity—embedding openness and reuse in core processes rather than as afterthoughts.

2. Plural Revenue Streams and “Layered Openness”

Following the traditional freemium–premium–indirect benefit typology, commons can adopt layered access models:

• Freemium tiers (basic public access, with value-added services such as data quality assurance or curation).

• Premium tiers (tailored APIs, enriched datasets, analytical dashboards).

• Indirect models (where open data supports core business functions, e.g., transportation or health platforms leveraging open mobility data).

This mirrors the razor-and-blades logic—open access as entry, with monetization through complementary services.

3. Cooperative and Mutualized Models

The cooperative mechanisms in projects like POSMO echo a broader movement toward data cooperatives and membership-based stewardship, where users and contributors collectively govern data resources. These models blur the line between producers and consumers of data and create incentives for contribution, trust, and shared value capture—an operationalization of “data as a commons.”

4. Public–Private–Philanthropic Hybrids

Hybrid financing—where governments provide infrastructure, philanthropy underwrites experimentation, and private actors co-invest through services or technology partnerships—is increasingly viable. Such tri-sectoral approaches ensure both legitimacy and flexibility while reducing dependency on single revenue sources.

5. Sustainability Through Service Design

Sustainable commons shift from “data release” to data services. Data is costly to produce but cheap to reproduce; hence, viable models rely on scaling re-use, not access fees. Commons can provide support, visualization, integration, and analytics services—monetizing added value rather than access itself.

6. Governance as a Differentiator

In a landscape where technical barriers are declining, governance becomes the new competitive advantage. Trusted intermediaries, transparent access processes, and demonstrable compliance with ethical and legal standards enable commons to attract users and funders. When openness increases, differentiation shifts from data access to governance and algorithmic quality.

7. AI-Ready and Trust-Based Commons

Finally, as AI transforms data value chains, data commons will increasingly function as AI training and testing ecosystems. Business models will depend on their capacity to ensure data quality, representativeness, and explainability. Initiatives that align openness with responsible AI development—through privacy-preserving infrastructures, social license mechanisms, and domain-specific curation—will define the next generation of commons.

Conclusion

The evolution from open data portals to fully fledged data commons ecosystems represents both an economic and institutional shift: from data as a public artifact to data as a shared asset. The emerging business models underpinning these commons—whether government-backed, cooperative, or hybrid— may signal a maturation of the data economy toward shared data stewardship, layered value creation, and mission-oriented sustainability. 

However, it is important to note that the above comparison is not fully comprehensive and many other funding models may exist. Our analysis summarizes several funding models that data commons initiatives are using and does not comment on the effectiveness of these models and whether they are sustainable over time. The information above was gathered through secondary research and was limited to the information made public on the initiatives’ websites. Additionally, we selected the examples from our repository where the majority are published in English. As such, we likely did not include funding models typically used in non-English speaking regions. 

To build on this work, future research may focus on the following questions:

1. Catalytic and Enabling Roles

• What roles do governments, development agencies, and philanthropic funders play in initiating and de-risking data commons?

• How do policy frameworks, public investment, and regulatory incentives shape the emergence of sustainable data commons ecosystems?

2. Funding Models and Sustainability

• Which funding and revenue models (e.g., government-backed, cooperative, public–private, subscription-based, or blended finance) have proven most effective in scaling and sustaining data commons—and under what conditions?

• How do these models balance economic viability, openness, and public value over time?

• What mechanisms exist to reinvest value generated by commons (e.g., through data dividends, shared infrastructure funds, or tiered access)?
  
  

3. Institutional and Governance Dynamics

• How do governance structures (e.g., cooperative boards, data trusts, federated management) interact with funding models to influence accountability and long-term resilience?

• Which institutional arrangements best mitigate collective action failures, such as underinvestment or the “tragedy of the commons”?
  
  

4. Equity, Inclusion, and Global Diversity

• How are data commons in low- and middle-income countries (LMICs) and non-English-speaking regions being funded and sustained?

• What contextual factors (e.g., infrastructure, donor dependence, regulatory environments) affect their financial and institutional models?

• How can funding mechanisms promote inclusive participation, especially from marginalized communities and local data producers?
  
  

5. Learning and Future Directions

• What metrics and indicators can assess the effectiveness and sustainability of data commons funding models?

• How might emerging technologies (e.g., blockchain, decentralized finance, smart contracts) enable new forms of collective financing and stewardship?

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Have any questions or are interested in collaborating? Reach out to us at [email protected].

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