User stories
Discover how companies are using ClickHouse to speed up their workloads and lower costs.
Machine learning and GenAI
From multi-day reporting to conversational analytics agents
“At Visa, we share a deep love of enterprise-scale data analytics. We’ve built a very strong partnership with ClickHouse over the last year-plus to enable a vision and achieve a strong data analytics platform that powers business insights and drives business outcomes.”
Machine learning and GenAI
How Jua delivers the world’s most accurate physics simulations 3x faster with ClickHouse Cloud
“Time really matters. If you have the data before others do, then you have an edge.”
Mark Frey, Engineering Lead
Machine learning and GenAI
Intelligent security at ClickHouse speed: How Cogent Security built an AI-native vulnerability management platform
"The key thing that ClickHouse unlocks for AI is speed. Everything we do needs to be done at machine speed to counter AI-enabled attackers.”
Karan Gugle, Founding Engineer
Real-time analytics, Machine learning and GenAI
How DoControl delivers real-time, AI-driven SaaS security insights with ClickHouse Cloud and MCP
“We were super excited to start working with ClickHouse. It has been a game-changer in how we serve the world’s largest companies, in a way that no competitive solution can match.”
Amit Fidler, Director of Engineering
“As DoControl grew, we needed a data infrastructure that could support the scale. ClickHouse met the mark.”
Bar Ifrah, Technical Lead
Machine learning and GenAI
How mpathic built better ML workflows by switching from Elasticsearch to ClickHouse Cloud
“We have faster pipelines, faster dev cycles. We can experiment in a flash.” - Caraline Bruzinski, Senior Machine Learning Engineer
Machine learning and GenAI
How SewerAI is using ClickHouse to modernize sewer management at scale
“Our compute usage remained stable. We didn’t have any more failing updates. Because of that, we didn’t have backlogs. Our analytics were up to date, and our customers were happy.”
Sabrina Kell, Senior Software Engineer
Machine learning and GenAI
ClickHouse Adoption at RBC Borealis
"Since adopting ClickHouse, we’ve been able to iterate on ML workloads 5-10x faster than with Spark or Teradata-based solutions, thanks to the faster compute performance and the ability to consolidate data in one location. User feedback has been overwhelmingly positive, particularly among researchers."
Machine learning and GenAI
How poolside is using ClickHouse to build next-gen AI for software development
"ClickHouse makes a lot of sense for what we’re trying to do. It lets us work faster, think bigger, and focus on what we care about most — scaling intelligence through AI." - Eiso Kant
Machine learning and GenAI
How Cognitiv Uses ClickHouse to Build Better ML Models for AdTech
“ClickHouse is the center of our data strategy. It’s really fast and cost-efficient, but most importantly it connects to everything. Data science as a discipline is not like engineering, where you can build in phases methodically. Iteration time is incredibly key to a data science team workflow.”
Machine learning and GenAI
From Rockset to ClickHouse: How Lens made its database faster and more efficient
"ClickHouse’s ability to handle a high volume of concurrent queries without performance degradation is another big advantage. Unlike Rockset, where query limits hindered Sree’s work and led to frustrating bottlenecks, ClickHouse allows the team to scale their ML operations as Lens Protocol grows."
Machine learning and GenAI
Query times improved 10x with ClickHouse
“After Testing Hadoop and Spark, We Chose ClickHouse“
Machine learning and GenAI
Inserting around 100 billion records per day, over 1 million records per second
“ClickHouse was perfect as Big Data Storage for our ML models.”
Machine learning and GenAI
Even when reading 10s of millions of rows, the performance was very nice
“We aggregate the user’s history in ClickHouse and use it as a data store for training and inference. Even when reading 10s of millions of rows, the performance was very nice and not the bottleneck when training new models.”
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