Welcoming Stately Cloud to Databricks: Investing in the Foundation for Scalable AI Applications

2025-12-19 · Source: Databricks · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Data Science & Analytics · Depth: Advanced, quick

Summary

Databricks has acquired the Stately Cloud team to enhance the reliability and scalability of its Data Intelligence Platform. The Stately Cloud founders, Alex Strand and Ben Hollis, bring extensive experience from Amazon and Snapchat, where they led efforts in distributed systems, reliability engineering, and core infrastructure modernization. Their expertise includes managing uptime during peak events like Black Friday and a global rewrite of Snapchat's messaging infrastructure. Stately Cloud itself focused on innovative database schema management to prevent outages from data model changes. This acquisition aims to strengthen the platform's core to support mission-critical workloads and the next generation of Data Intelligent Applications, particularly as customers adopt Agent Bricks, Lakebase, and Databricks Apps for advanced AI use cases requiring continuous availability and fault tolerance.

Key takeaway

For CTOs and VPs of Engineering building mission-critical AI applications, this acquisition signals Databricks' commitment to foundational reliability. You should evaluate the Data Intelligence Platform for its enhanced capabilities in continuous availability, intelligent scaling, and fault tolerance, especially when deploying Agent Bricks, Lakebase, or Databricks Apps, to ensure your AI workloads operate safely and continuously at global scale.

Key insights

Acquiring Stately Cloud enhances Databricks' platform reliability for mission-critical AI workloads.

Principles

• Reliability is foundational for AI at scale.
• Distributed systems require robust schema management.

Method

Stately Cloud's approach involved innovative database schema management to eliminate outage risks from data model changes, a critical aspect for operating globally distributed systems reliably.

In practice

• Prioritize platform reliability for AI applications.
• Implement robust database schema management.

Topics

• Databricks Platform
• Distributed Systems
• Reliability Engineering
• AI Workloads
• Data Intelligence

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

Related on AIssential

• Data & AI Summit Takeaways: Breakout Sessions — Modern data platforms and AI enable scalable solutions for data quality, conversational analytics, and trust through robust data contracts.
• How the lakebase architecture stays resilient to cloud failures — Lakebase achieves high availability for agentic workloads through stateless compute, zone-redundant storage, and a refactored control plane.
• Interview: Databricks EMEA CTO on the company’s global expansion in challenging times — Databricks' EMEA CTO highlights strategic expansion and open-source advantage amidst rapid enterprise AI adoption.
• Databricks acquires Quotient AI to power AI agent evaluations — Acquiring Quotient AI strengthens Databricks' AI agent offerings with continuous evaluation and reinforcement learning capabilities.
• Databricks says it solved the decades-old data pipeline problem that's been slowing AI agents — Databricks unifies transactional and analytical data at the storage layer, enabling millisecond latency for AI agents.
• The Future of AI Is Stateful Infrastructure — Enterprise AI's future relies on stateful infrastructure managing context, not just model intelligence.

Open in AIssential →

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