Ingext Community Edition

Ingext Community Edition is now available as a self-hosted, Kubernetes-based deployment, distributed using Helm charts. This release makes Ingext platform independent and accessible across multiple cloud environments without changing how the system is deployed or operated.

The Community Edition supports installation in the following cloud platforms:

• AWS
• Azure
• Google Cloud

In addition to these environments, Ingext is designed to run in any Kubernetes cluster, as a self-hosted deployment. The same Helm-based installation model is used regardless of where the cluster is running, allowing users to operate Ingext without being tied to a specific cloud provider.

Why Self-Hosted Matters

The core problem Ingext addresses is how data is collected, transformed, and routed under continuous load.

Ingext Community Edition provides a self-hosted data fabric that allows organizations to solve data collection and storage. Data is collected once, processed locally, and routed to one or more destinations. This includes writing selected data to a data lake in the cloud to later search.

Because the Ingext data fabric is self-hosted, organizations can learn, experiment, and design pipelines. There are no usage fees, no forced data movement, and no external dependencies beyond the underlying cloud infrastructure. Cost is limited to direct cloud charges and resources the organization controls.

This makes Ingext Community Edition a practical starting point for teams that want to understand how to deploy a data fabric, integrate with a data lake, and build cost-effective and secure data pipelines. It allows organizations to experiment, iterate, and develop solutions on their own terms, before committing data or budget to external platforms.

From Data Fabric to Data Lake

A data fabric controls how data is collected, transformed, enriched, and routed. It is responsible for reliable, continuous data movement. A data lake is responsible for retention. It stores large volumes of data so it can be searched and analyzed over time.

Ingext combines these two layers into a single, cohesive system.

Ingext provides data collection, Parquet-based data lake storage, and search capability as part of the same workflow. Data is collected once, processed intentionally, and written directly to an organization-controlled data lake in a usable form. Search is available without requiring a separate platform or additional tooling.

This makes creating and operating a data lake straightforward. Teams do not need to assemble multiple components, manage fragile integrations, or push data into external services just to make it usable. The data fabric and the data lake work together by design, allowing organizations to collect, store, and search data simply and under their own control.

AI Enabled

In addition to search, the Ingext data lake can be accessed through MCP and AI-based workflows.

Because data is collected, transformed, and stored under the organization’s control, it can be safely exposed to AI systems without sending raw telemetry to external SaaS platforms. MCP provides a controlled access layer that allows AI processes to query and reason over data where it resides.

This completes the path from hard-to-access data sources to AI integration. Data moves from collection, through transformation and storage, to AI-enabled access without breaking control boundaries or introducing unnecessary data movement.

By integrating AI access at the data fabric and data lake layer, Ingext allows organizations to experiment with AI-driven analysis using real data, while maintaining cost control, security, and architectural flexibility.

Community

Ingext is making this release to accelerate the roles data fabric have into today’s infrastructures. Provided a means will a low barrier of entry to imlement data fabrics, data lakes and AI integration.

Ingext is releasing Community Edition to accelerate the adoption of data fabric patterns in modern infrastructures. The goal is to reduce the barrier to entry for implementing data fabrics, data lakes, and AI integration using real systems and real data.

This release provides a practical way for individuals and teams to learn how to deploy and operate a data fabric, connect it to a data lake, and expose data for search and AI workflows. It is designed for experimentation, learning, and problem-solving rather than packaged services or managed platforms.

By making these capabilities available with minimal friction and no external service dependencies, Ingext Community Edition enables a broader community to explore cost-effective, secure architectures and share practical knowledge about how data fabrics work in practice.