Provisioning for the Agentic Era: How Databricks Built a Self-Serve Infrastructure Vending Machine

2026-07-23 · Source: Databricks · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

Databricks developed the Field Engineering Vending Machine (FEVM), a self-serve infrastructure provisioning tool designed to address the scaling challenges of its rapidly growing field engineering organization. Initially, shared workspaces sufficed for under 1,500 engineers, but with over 7,000 GTM personnel, including a significant field engineering component, manual provisioning led to operational complexity, resource contention, and difficult cost attribution. FEVM provides isolated, use-case-based environments provisioned in minutes, governed centrally, and automatically cleaned up. Built on Databricks Apps with a React frontend, Python backend, and Terraform for multi-cloud resource provisioning (AWS, Azure, GCP), FEVM tracks resources via a Lakebase state database. It supports agent-first workflows using Claude skills and handled nearly 1,200 provisioning requests in a single day during an internal event, managing over 2,600 active deployments across three clouds for 5,000+ active users.

Key takeaway

For AI Architects or MLOps Engineers scaling internal development or demo environments, consider implementing a self-serve infrastructure vending machine. This approach, exemplified by Databricks' FEVM, enables rapid, isolated environment provisioning, significantly reducing operational overhead and resource contention. You can improve cost attribution and auditability by automating lifecycle management and centralizing governance. Explore agent-first workflows to further streamline environment setup and integrate with existing tools like Git and Slack for seamless operations.

Key insights

Databricks' FEVM provides self-serve, isolated, and governed infrastructure provisioning, enabling rapid, scalable environment deployment for field engineers.

Principles

• Build with the user in mind.
• Transparency is a core feature.
• Ecosystem integration is crucial.

Method

FEVM uses a React frontend, Python backend, and Databricks Apps deployment. Terraform provisions multi-cloud resources (AWS, Azure, GCP), while a Lakebase database tracks resource state, ownership, purpose, and expiration.

In practice

• Use case-based provisioning.
• Implement agent-first workflows.
• Automate resource lifecycle management.

Topics

• Infrastructure Provisioning
• Self-Serve Environments
• Databricks Apps
• Multi-Cloud Provisioning
• Agentic Workflows
• Terraform Automation

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Engineer, MLOps Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Databricks.