Question: I'm looking for a platform that simplifies the setup and management of open-source data infrastructure for my AI project.

For making open-source data infrastructure easier to set up and manage for your AI project, Qubinets is a good choice. It streamlines data pipeline creation and operation with managed open-source tools, a schema connector repository and customizable cloud environments. The service supports a broad range of technologies and can run on multiple cloud computing foundations with a drag-and-drop user interface. And it has flexible pricing options, including a free tier, so it's good for teams with complex data projects.

Another option is Airbyte, an open-source data integration service that can move data from more than 300 sources to many destinations. It has features like a Connector Builder for custom connectors and Automated Schema Evolution for change data capture. It can be deployed in cloud-hosted and self-managed configurations, and it's got an easy-to-use interface. That makes it a good choice for small-scale and large-scale data integration projects.

Also worth a look is Aiven, a cloud-agnostic service that manages cloud data infrastructure. It runs open-source software like Apache Kafka and PostgreSQL on a variety of cloud computing foundations, including AWS, Google and Azure. Aiven's service is designed to let customers quickly deploy data infrastructure, see exactly how much it costs and benefit from security and compliance features. That makes it a good choice for companies large and small.

Last is dstack, an open-source engine that automates the provisioning of infrastructure for AI model development and deployment. It can run on multiple cloud computing providers and on-premises servers, making it easier to set up and manage AI workloads. With multiple deployment options and a large community of users, dstack is designed to let you focus on data and research while cutting costs.