Why Anthropic, Meta, and Tesla All Chose the Same Database | Aaron Katz, ClickHouse

2026-03-31 · Source: Weights & Biases · Field: Technology & Digital — Software Development & Engineering, Cloud Computing & IT Infrastructure, Artificial Intelligence & Machine Learning · Depth: Intermediate, extended

Summary

Aaron Katz, CEO of ClickHouse, discusses the company's origin, growth, and strategic vision for its cloud database service. ClickHouse, initially developed at Yandex in 2009 for web analytics, was open-sourced in 2016 and gained significant adoption by companies like Uber, eBay, and Microsoft. ClickHouse Inc. was formed in August 2021 with a $50 million seed round, followed by a $250 million round, despite having no product or revenue at the time, leveraging the open-source project's existing traction and a strong founding team. The company's go-to-market strategy prioritizes a frictionless, developer-centric approach, offering a fully managed, serverless cloud service with compute-storage separation. Katz also reflects on the "SaaS apocalypse," the role of infrastructure in the current AI landscape, and the impact of AI on hiring and engineering roadmaps, noting that AI is accelerating productivity and enabling new automation functions.

Key takeaway

For Directors of AI/ML and Data Engineers evaluating analytical database solutions, ClickHouse Cloud offers a compelling option for real-time, high-performance workloads. Its developer-centric approach, combined with a forthcoming unified data stack including a managed Postgres service, suggests a strategic platform for future AI-driven applications. You should investigate its capabilities for both analytical and transactional needs, especially given its strong performance and cost-effectiveness for observability use cases.

Key insights

ClickHouse's success stems from its open-source foundation, developer-first cloud strategy, and focus on real-time analytics.

Principles

• Prioritize developer experience for infrastructure adoption.
• Open-source projects benefit from dedicated committer teams.
• Infrastructure plays a "picks and shovels" role in tech booms.

Method

ClickHouse's go-to-market involves a frictionless, self-service cloud offering, supported by direct engineer-to-engineer technical support and free migration services to accelerate customer adoption.

In practice

• Evaluate ClickHouse Cloud for low-latency, customer-facing analytics.
• Consider using AI agents for lead qualification and demand generation.
• Explore ClickHouse as an observability layer alternative to Datadog.

Topics

• ClickHouse Database
• Open-Source Infrastructure
• Managed Cloud Services
• Real-time Analytics
• Product-Led Growth

Best for: Director of AI/ML, Data Engineer, CTO

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Editorial summary, takeaway, and curation by AIssential. Original article published by Weights & Biases.