How Databricks rolls out frontier models to 14,000 employees on Day 1

Summary

Databricks provides employees Day 1 access to new AI models using Unity Gateway for governance and cost management. The company uses a three-step pipeline: immediate experimental availability, per-user budget constraints, and data-driven promotion decisions. Unity Gateway CLI distributes model configurations to employee laptops, tagging new models as experimental. Per-user budgets include a monthly maximum, daily runaway limit, quality frontier budget, and experimental budget to control costs. Within three days of the Opus 5.5 and GPT-6 Sol launches, Databricks gathered benchmark, user feedback, and cost data to confirm they were on the efficiency frontier and promoted them, with Opus 5.5 showing a 29% cost reduction per session over Opus 4.8 and GPT-6 Sol showing a 48% reduction over GPT-5.6 Sol.

Takeaways

1. 1

   Naive model rollout can cause a 60% cost increase, as seen when GPT Astra was released without cost mitigations.

2. 2

   Databricks uses stratified cost analysis (reweighting sessions by single/multi-turn and file edits) to fairly compare model costs per session.

3. 3

   Within three days of launch, Databricks confirmed Opus 5.5 and GPT-6 Sol were on the efficiency frontier and promoted them, with Opus 5.5 becoming the default for Claude Code.

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