Databricks on Wednesday unveiled OpenSharing, a new open protocol designed to let enterprises share AI models, agent skills, dashboards, and unstructured data across platforms without having to copy or move those assets.

That sharing is made possible by OpenSharing’s zero-copy credential vending model that allows recipients to securely access shared assets directly from a provider’s cloud storage using temporary, scoped credentials rather than requiring the assets themselves to be copied, moved, or replicated, the company wrote on its GitHub page.

Reducing the integration tax of enterprise AI

The ability to share AI assets without creating duplicate copies could help reduce integration complexity, improve governance, and limit the operational overhead associated with operationalizing AI systems across environments for CIOs, said Ashish Chaturvedi, leader of executive research at HFS Research.

“Every organization building AI, such as multi-agentic systems, is hitting the same wall, i.e., the model, the skill, and the consumer reside on three different platforms. The integration tax is enormous, and it grows exponentially with every new partner, customer, or internal team,” Chaturvedi said.

Echoing Chaturvedi, The Futurum Group’s lead of the CIO practice, Dion Hinchcliffe, pointed out that the reduction in operational overhead could help CIOs cut down on the hidden costs of integration around AI deployments: “Today, hidden costs include more than just model development. It is the endless packaging, translation, sync, and governance effort required to operationalize AI assets across organizational boundaries.”

From data sharing to AI asset sharing

That cost reduction is becoming even more important because enterprises are beginning to treat AI assets as business assets that need to be shared, said Stephanie Walter, practice lead of the AI stack at HyperFRAME Research.

“Enterprises are quickly realizing that the value is no longer just in the dataset. It is in the governed context, logic, and intelligence built around the dataset. Existing approaches can share datasets well, but they often do not address the broader AI package,” Walter said.

“OpenSharing is directionally aligned with that shift because it extends the sharing model beyond tables and files toward the artifacts that power AI workflows,” Walter added.

For Hinchcliffe, that alignment should work in CIOs’ favor, trying to operationalize AI across their systems: “CIOs increasingly want AI supply chains, not isolated data lakes like before.”

Additionally, Chaturvedi pointed out that the new protocol can help CIOs accelerate the monetization of AI investments.

“For CIOs, the speed at which you can share AI assets across partners, subsidiaries, and customers determines the speed at which you can monetize your AI investments. If sharing an agent skill takes six weeks of integration work, you’ve lost the window. If it takes a protocol call, you’ve turned AI into a distribution business,” he said.

How OpenSharing could simplify AI development

Achieving those benefits, however, will require developers to move AI assets across disparate platforms more efficiently, and analysts pointed out that OpenSharing’s ability to reduce integration complexity could significantly improve productivity.

“Developer productivity depends on reducing platform translation work. Developers do not want to rebuild the same asset for every consuming environment, and enterprises do not want every partner or customer interaction to become a platform migration conversation,” Walter said.

In fact, Chaturvedi sees the new protocol as unique in the industry, in the specific sense that “no other open protocol covers agent skills and AI models as shareable, governed objects”.

Walter, in contrast, sees the openness of the protocol as novel: “What is more interesting is the combination: an open protocol, cross-platform interoperability, Linux Foundation governance, and a broader asset model that extends beyond datasets into AI models, agent skills, dashboards, applications, and unstructured data.”

“The novelty is not that Databricks invented sharing, zero-copy access, or marketplace-style distribution. Those capabilities already exist in various forms across the market,” Walter said, pointing towards Snowflake’s offerings, such as the Zero-Copy integrations.

The difference, though, the analyst noted, is that Snowflake allows data to be copied only if both provider and receiver are on Snowflake.

With Databricks’ OpenSharing, data can be copied across platforms, Walter added.

OpenSharing, which is an evolution of Databricks’ existing Delta Sharing protocol, is currently a sandbox project under the Linux Foundation AI & Data Foundation and is available via GitHub.

Its current list of generally available connectors includes Python, Apache Spark, Tableau, PowerBI, Snowflake, DuckDB, Clojure, Node.js, Java, Arcurate, Rust, Go, C++, and R.

Other connectors that are expected to be made generally available soon include Google Spreadsheet, Excel, Airflow, and Lakehouse Sharing.