SAN FRANCISCO--November 4, 2025--ClickHouse, Inc., the company behind the world’s fastest and most popular real-time analytics database, today announced the acquisition of LibreChat, a leading open-source AI chat platform that provides a unified interface for interacting with large language models (LLMs). The acquisition brings LibreChat founder Danny Avila and his team to ClickHouse.

Together, ClickHouse and LibreChat form the foundation of the open-source Agentic Data Stack, empowering teams to deploy AI agents that analyze and act on large datasets through natural language. This shift to agentic analytics is already transforming how organizations such as Shopify, Daimler Truck, Fetch, and cBioPortal leverage their data, demonstrating the immediate value in real-world applications.

At Shopify, LibreChat has become central to internal AI workflows. “LibreChat powers reflexive AI use across Shopify. With near universal adoption and thousands of custom agents, teams use it to solve real problems, increase productivity, and keep the quality bar high. By connecting more than 30 internal MCP servers, it democratizes access to critical information across the company,” said Matt Burnett, Senior Engineer at Shopify.

In healthcare, cBioPortal uses the combined ClickHouse and LibreChat Agentic Data Stack to unlock new research capabilities. "By leveraging the ClickHouse, MCP, and LibreChat stack, we rapidly delivered a prototype to cBioPortal users that empowered them to ask entirely new questions about cancer genomics and treatment trajectories, get quick answers, and explore data in ways not possible through the existing UI," said Ino de Bruijn, Manager Bioinformatics Software Engineering at cBioPortal. "It puts discovery at cancer researchers' fingertips."

This LibreChat acquisition comes as enterprises rapidly adopt agentic analytics to democratize data access and accelerate time-to-insight. Fetch, a leading mobile rewards platform, built its FAST AI-powered analytics product on ClickHouse to transform household purchase behavior into actionable business intelligence. "We see agentic analytics as the future of data interaction, enabling more intuitive, dynamic, and impactful use of information," said Sam Corzine, Director of Machine Learning at Fetch. "With its unmatched speed and scalability, ClickHouse is well-positioned to power this new generation of agentic experiences, and we're thrilled to grow our partnership together."

ClickHouse leverages agentic analytics internally with LibreChat-and-ClickHouse-powered DWAINE (Data Warehouse AI Natural Expert), an agent that now handles approximately 70% of data warehouse queries for over 200 users, processing 33 million LLM tokens daily as of October 2025—up from 15 million just months after launch.

"What impressed me most is LibreChat's vibrant community that continuously innovates," said Dmitry Pavlov, Director of Engineering at ClickHouse. "The synergy between ClickHouse Cloud's blazing-fast query performance and LibreChat's flexible, multi-LLM architecture is unlocking a new generation of data analysis agents - real-time, secure, powerful, and accessible."

For existing LibreChat deployments, operations continue unchanged with ongoing investment in the open-source project and community. ClickHouse users will gain access to enhanced integration capabilities, including native LibreChat experience alongside ClickHouse Cloud instances, extended data visualization support, enterprise-grade security and governance, and a tailored semantic layer. These investments will ensure that the Agentic Data Stack is fully enterprise-ready, meeting the requirements of the world's most demanding organizations.

“Every breakthrough in data technology has been about reducing the distance between questions and answers. By joining forces with LibreChat, we’re tackling this distance. Now, any user can talk directly to petabytes of data and get instant, actionable insights,” said Aaron Katz, CEO of ClickHouse. “As AI agents proliferate across every industry, the companies that win will be those that can seamlessly connect human intelligence with machine-scale data processing. The future of analytics isn’t just dashboards, it’s intelligent agents that interpret data, trigger workflows, and power real-time decisions. The ClickHouse Agentic Data Stack makes that future available today.” 

### About ClickHouse

ClickHouse is a fast, open-source columnar database management system built for real-time data processing and analytics at scale. Engineered for high performance, ClickHouse Cloud delivers exceptional query speed and concurrency, making it ideal for applications that demand instant insight from massive volumes of data. As AI agents become increasingly embedded in software and are generating far more frequent and complex queries, ClickHouse brings a high-throughput, low-latency engine, purpose-built to meet this challenge. Trusted by leading companies like Sony, Tesla, Anthropic, Memorial Sloan Kettering, Lyft, and Instacart, ClickHouse helps teams unlock insights and drive smarter decisions with a scalable, efficient, and modern data platform. For more information, visit [clickhouse.com](http://clickhouse.com).

### About LibreChat

LibreChat is a leading open-source AI chat platform offering a unified interface for interacting with multiple large language models. The platform gives users and organizations complete control over their data, agents, and conversations while supporting enterprise-grade deployments at scale.

### Contacts

Tanya Bragin  
ClickHouse, Inc.  
tanya@clickhouse.com

ClickHouse today announced the acquisition of LibreChat, a leading open-source AI chat platform that provides a unified interface for interacting with large language models (LLMs).

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ClickHouse acquires LibreChat to democratize AI-driven analytics through the open-source Agentic Data Stack

After the success of our [ClickHouse Open House](https://clickhouse.com/openhouse) conference in San Francisco in May, we knew we had to take this show on the road. 

On October 7th, the ClickHouse community came together in New York to hear from ClickHouse customers Capital One, Ramp, and Modal, alongside the ClickHouse product and leadership teams. 

For those who couldn't make it, and for attendees who want to revisit the sessions, we've now released the recordings from the event!

![ClickHouse Open House 2025](https://cms.clickhouse-dev.com:1337/uploads/open_house_new_york_2025_36052dc03c.png)

We organized the presentations around four key use cases showcasing where ClickHouse shines. 

First is **real-time analytics**, or 'immersive data-driven applications,' that need both recent and historical data with instant interactive filtering. As one of our customers puts it, they're building apps where users expect responses right away, not in 5-10 seconds.

The second use case is **observability**, where SREs and DevOps teams increasingly adopt ClickHouse as observability data keeps getting more voluminous and structured over time. Companies like eBay moved from Elastic to ClickHouse early on, and we're seeing this trend accelerate as teams want more control over their data to build AI-enabled monitoring features.

The third is **data warehousing**, which includes leveraging ClickHouse as an interactive query layer on top of data lakes. The key word here is 'interactive.' Traditional query engines are optimized for batch workloads, but when you're applying AI to your data warehouse or data lake, you need that human-level responsiveness where you can ask a question, get a quick response, and iterate.

And finally, there's **AI and ML infrastructure** itself - ClickHouse is being used for data prep and transformation, as an offline and online feature store for model training and inference, for vector search, and of course for observability of AI systems. It's become essential infrastructure for data science teams.

## Capital One: Real-time analytics

Capital One, the banking and credit card company, uses ClickHouse to power its Slingshot data management and cost optimization platform. 

Salim Sayed and Syd Mehmood chose ClickHouse for its speed, scalability, and cost efficiency. They transformed dashboard load times from 5-6 seconds to under half a second, achieving **80% improvement in response times while cutting infrastructure costs by 50%.**

<iframe width="768" height="432" src="https://www.youtube.com/embed/8Avld0BlDlg?si=dkcRkjQX-kd5w8hL" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media;a gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

➡️ [How Capital One Slingshot cut infrastructure costs by 50%](https://clickhouse.com/videos/open-house-nyc-capital-one)

## Ramp: Observability

Ramp, a finance operations platform, faced challenges scaling analytics queries on Postgres. Four engineers were tasked with solving this problem, and within four weeks, they had put their new ClickHouse-based system into QA.

Ryan Delgado, who leads Ramp’s platform team, walks through the story and explains why turning those slow queries into reports that ran hundreds of times faster was so impactful that his entire OLAP team got promoted.

<iframe width="768" height="432" src="https://www.youtube.com/embed/mPZ7Bck_cMI?si=PfSIbO4sGNT4_nHU" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

➡️ [ClickHouse at Ramp: Just OLAP it](https://clickhouse.com/videos/open-house-nyc-ramp)

## Modal Labs: Data warehousing

Modal is an infrastructure platform for running large-scale GPU workloads in the cloud. They use ClickHouse as the backbone of their real-time observability platform.

In this talk, Ro Arepally explains how they solved a scaling bottleneck by switching to ClickHouse, transforming their logging infrastructure into a system that **handles half a trillion logs and delivers instant search results.**

<iframe width="768" height="432" src="https://www.youtube.com/embed/1LXK-mgdKCg?si=C3XlDag4pt5T5zF8" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

➡️ [How Modal delivers instant search on 500B+ logs](https://clickhouse.com/videos/open-house-nyc-modal)

## What's Next

Each of these stories deserves a deeper dive. Over the coming weeks, we’ll break down the technical architectures and lessons learned in individual posts [on our blog](https://clickhouse.com/blog?category=user-stories).

The [Open House Roadshow](https://clickhouse.com/openhouse) continues! We're bringing these conversations to more cities, connecting with data teams who are pushing the boundaries of what's possible with real-time analytics.

Check out the [full video playlist from NYC](https://clickhouse.com/videos?category=open-house), stay tuned for videos from Sydney and Bangalore, and join us at our final roadshow stop in Amsterdam on October 28th.

➡️ [Join us in Amsterdam](https://clickhouse.com/openhouse/amsterdam)


The ClickHouse Open House Roadshow kicked off in New York on October 7th with compelling customer stories from Modal, Ramp, and Capital One, and all session videos are now live.

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ClickHouse Open House Roadshow NYC videos are here

Today, we are pleased to announce that [chDB](https://doc.chdb.io/#/), an embedded SQL OLAP engine powered by ClickHouse, is now part of ClickHouse. chDB’s creator and main contributor, Auxten, [is joining forces with us](https://auxten.com/chdb-is-joining-clickhouse) to focus on evolving chDB and integrating it even more closely with the ClickHouse ecosystem.

## What is chDB and how did we get here? 

ClickHouse is best known as a scalable analytics database that uses a client-server architecture, but we’ve actually always had ClickHouse Local, a CLI tool that contains ClickHouse.

People [became aware of ClickHouse Local at the beginning of 2023](https://news.ycombinator.com/item?id=34265206) and they loved it, but we also got feedback that they wanted to be able to integrate ClickHouse Local with the other tools they were using both in their data pipelines and for data analysis.

Around this same time, Auxten had started working on chDB with the goal of being able to use ClickHouse as an “out of the box” Python module. 

You can use chDB to do in-memory analysis of data that lives locally or remotely in all the formats and sources supported by ClickHouse. You can also choose to persist data in a local ClickHouse instance and chDB uses the same storage format as ClickHouse Local, which means their databases are interchangeable. The diagram below shows how chDB works in comparison to other ClickHouse variants.

![chDB Image 4 updated 2.png](/uploads/ch_DB_Image_4_updated_2_6fd7ed2265.png)

We were really impressed with chDB and contacted Auxten to tell him that we loved his work and wanted to help promote it. This resulted in Alexey mentioning chDB in the [23.7 release call](https://www.youtube.com/live/TI1kONfON18?si=OhCU6_R58iByNXVQ&t=1872), as well as a subsequent [introductory blog post about chDB](https://clickhouse.com/blog/welcome-chdb-to-clickhouse) in August 2023. A few weeks later Auxten wrote [chDB - A Rocket Engine on a Bicycle](https://clickhouse.com/blog/chdb-embedded-clickhouse-rocket-engine-on-a-bicycle), in which he went through chDB’s design decisions and implementation details

In the months since then, [we’ve seen more interest in chDB](https://clickpy.clickhouse.com/dashboard/chdb), have been working closely with Auxten, and gradually saw an opportunity to take our collaboration to the next level. 

## What does the news mean if I’m a chDB user?

If you’re an existing user of chDB, this is great news for you. Auxten will now be working full-time on chDB rather than only in his spare time. This means we’ll be able to fix bugs/issues more quickly as well as adding new features and integrations.

chDB will soon be integrated into ClickHouse’s continuous integration pipeline, which will allow us to have chDB closely follow the release schedule of ClickHouse.

We’ll be moving the chdB documentation to live alongside the ClickHouse documentation and will make chDB more prominent in our getting started guides.

We’ll also be looking at ways that we can better integrate chDB with the rest of the data engineering ecosystem, so let us know if you have any opinions about where we should start!

## What happens next?

Our goal is to create a seamless experience whether you’re analyzing Parquet files on your laptop, creating data pipelines with other tools in the Python ecosystem, or building a real-time data warehouse to serve data products to your users.

We want to get to the point where it takes minimal effort to switch between the different flavors of ClickHouse.

We don’t know exactly what this will look like and we’d like your help figuring this out. Below are some of the things at the top of our todo list for what to do next:

1. Better performance, especially on Pandas DataFrame and Arrow Buffer
2. Keep up with latest ClickHouse release
3. Better integration with more programming languages and data sources

But maybe you have better ideas! We’d love to hear your thoughts on the [chDB Github Discussions](https://github.com/orgs/chdb-io/discussions). 

## Get started with chDB today

In the meantime, if you’re not yet familiar with chDB, we thought we’d include a little example to whet your appetite. chDB is [published on PyPi](https://pypi.org/project/chdb/) and can be installed with the pip package manager. We’ll also install Pandas and pyarrow as we'll be using those too:

```bash
pip install chdb pandas pyarrow
```

Once that’s installed we can open a Python REPL, Jupyter notebook, or similar and import the following module:

```python
import chdb
```

Let’s now look at how to query the Hugging Face [midjourney-messages](https://huggingface.co/datasets/vivym/midjourney-messages) dataset, which contains metadata about images generated by the Mid Journey Generative AI service over several months in 2023. This dataset contains 55 million rows spread over 55 Parquet files. 

The following query returns the count of images as well as the minimum/maximum size, width, and height:

```python
chdb.query("""
FROM url('https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/0000{00..55}.parquet')
SELECT count(), COLUMNS(width, height, size) APPLY(max), COLUMNS(width, height, size) APPLY(min)
SETTINGS max_http_get_redirects=1
""", "Vertical")

Row 1:
──────
count():     55082563
max(width):  18928
max(height): 16128
max(size):   24498571
min(width):  32
min(height): 56
min(size):   312
```

If we want to persist the data from those Parquet files, we can do that as well.

```python
from chdb import session as chs

sess = chs.Session("midjourney.chdb")
sess.query("CREATE DATABASE MidJourney")
sess.query("""
CREATE TABLE MidJourney.images
Engine = MergeTree
ORDER BY (size, height, width)
AS
SELECT *
FROM url('https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/0000{00..55}.parquet')
SETTINGS max_http_get_redirects=1, schema_inference_make_columns_nullable=0
""")
```

That will take around 4 minutes to pull down the data and ingest it into ClickHouse. We can then repeat the above query:


```python
sess.query("""
FROM MidJourney.images
SELECT count(), COLUMNS(width, height, size) APPLY(max), COLUMNS(width, height, size) APPLY(min)
""", "Vertical")
```

Hopefully that’s given you an idea of what you can do with chDB, but [check out the ](https://doc.chdb.io/#/)documentation for even more examples. We look forward to learning what you do with this compact yet powerful way to use ClickHouse in your local projects!

Read more about the history of chDB, and the creator Auxten's perspective, [in his post](https://auxten.com/chdb-is-joining-clickhouse).


We're excited to announce that chDB is now part of ClickHouse.

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chDB joins the ClickHouse family

We owe a lot to the cloud data warehouses, but their era of hegemony is coming to an end.

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The Unbundling of the Cloud Data Warehouse

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I often get questions about how I decided to become a product manager at startups, and what I learned over the years. The truth is, I had no such plan in life - I fell into the product management career somewhat by accident. But I have learned a few things along the way, and I will attempt to summarize the highlights in this blog. I hope it proves useful for folks considering a career in product management, especially at fast-growing technology startups.

## How I decided to become a product manager

I grew up in a family of scientists and thought until a certain point that everyone had to eventually earn a PhD. When I went to university, I did not declare a major and started studying everything under the sun. I only later realized that I liked working with computers and therefore decided to major in computer science. I went on to become a technology consultant at Deloitte, one of the huge “big four” consulting firms, and while that was a great learning experience for customer-facing skills, it was not what I was looking for. We worked with a lot of legacy technologies at big bureaucratic companies, which did not feel innovative or all that interesting to me.

I realized that instead I wanted to bring new products to life, but I did not know how to break in. So I went back to grad school to study computer science again, but quickly realized that unlike my parents, I did not want a PhD. After getting my masters, I was just as lost as before as to what to do next. This is where I got advice from one of my professors to “look into product management”. It sounded important, so I researched online and started interviewing at big companies in Seattle - Microsoft, Amazon, Google - regurgitating all the things I read and hoping they would believe I had the “right stuff” for an entry-level product management job.

Thankfully, at that stage, I was referred by another professor to a set of technical founders who were bringing a startup out of stealth mode and just got Series A funding. They did not have a product management role for me, but I agreed to join as a sales engineer (aka solution architect), running proofs of concept with early customers. I agreed to the role, because I was really curious about “working at a startup”, but I was also very much enamored by the product and by the team.

As it turned out, this choice was the deciding factor in shaping my career going forward.

## My product management experiences (so far)

ExtraHop was the first startup I worked at. It was a closely-knit 10-person team at the time, looking for smart, dedicated people to build a next-generation network and security analytics product. The company was led by incredibly talented technologists [Jesse Rothstein](https://www.linkedin.com/in/jrothste/) and [Raja Mukerji](https://www.linkedin.com/in/rajamukerji/) with a really sharp vision for the space, and a tenacious ability to execute. They built one of the strongest engineering teams I have ever had the privilege to work with. I started as the first sales engineer, but quickly became the de-facto PM, because I spoke with every customer and understood what they wanted and why. When I left the company 5 years later, I was the head of product with about 10 people on my team (the company grew from 10 to 400).

I left ExtraHop for personal reasons (a move to the Bay Area with my family) and joined Elastic, a 100-person startup around Elasticsearch, an open source project in the search and analytics space. I was lucky enough to join [Gaurav Gupta](https://www.linkedin.com/in/ggupta/), a seasoned product leader and a great mentor, as the second PM on his team. I knew nothing about open source, and was very curious about how to build a viable business around an essentially free technology. The company was led by truly visionary people, including of course the creator of Elasticsearch, [Shay Banon](https://www.linkedin.com/in/kimchy/), who remains a source of inspiration to me to this day. I went through years of very fast growth and an IPO with Elastic, working my way up from middle ranks of product management to head of product for the #1 revenue generating observability product line.

When I got the opportunity to lead product at ClickHouse, an increasingly popular open source project in the real time analytics space, it did not take much time to decide it was the right next step for me. While ExtraHop and Elastic were great learning experiences, I was not fully responsible for taking the first commercial product offering from 0 to 1 there. I really wanted to have that responsibility, and I knew I was ready for it. I was also inspired by the technology, which is taking the world by storm, the culturally-diverse and dedicated team, and the opportunity to work in an emerging market category of analytical databases.

## My product management philosophy

I sometimes get asked what product management means to me, and what I think it takes to be a good PM.

The first part is easy. Product management is responsible for making sure that the organization is aligned around a vision and a plan for how to build the right product(s) to sustain a successful business. There are whole books written on the subject, so I won’t opine in more detail here. My favorite book that simply explains the role of product management in relation to other roles at technology companies is “Inspired” by Marty Cagan.

How to be a good PM is much more difficult to describe. It depends a lot on the type of organization and team you are joining. As you can see from my experiences, I enjoy working at early-stage companies with very strong engineering teams. And as a result, I believe in building respectful and trusting relationships with technical leaders at the company, and being flexible about the role of product management depending on the stage of startup development.

But ultimately, I believe that a product manager is a leader that wakes up every morning, thinks about how to make their product successful in the market, and does what it takes to make it happen. The best and most succinct way I have seen this philosophy described is in Ben Horowitz [Good PM / Bad PM blog](https://a16z.com/2012/06/15/good-product-managerbad-product-manager/). He says it is dated, but I think it stands the test of time.

If you got to this point, thank you for reading!

I just started a [Medium](https://medium.com/@tbragin) site and will post product management and startup advice there going forward, starting with my first article “How to move to product management from technical roles”.

You can also follow me on [Twitter](https://twitter.com/tbragin) and [LinkedIn](https://www.linkedin.com/in/tbragin/) for my thoughts about product management, startups, and products I’m currently building.

**Tanya's Bio:**

Tanya Bragin leads product at ClickHouse, the fastest open source analytical database. She has over 20 years of experience in the data space, ranging from data analytics, to search, to security and network analytics. She started her career in consulting and sales engineering, and spent the last decade and a half growing product organizations from early stages to maturity at three data analytics startups, ExtraHop, Elastic, and now ClickHouse.



I often get questions about how I decided to become a product manager at startups, and what I learned over the years. The truth is, I had no such plan in life.

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My Journey as a Serial Startup Product Manager

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It’s the time of year where many of us start thinking about holiday gift-giving and look ahead to our New Years’ resolutions, and here at ClickHouse we are no different! This year, our gift to you is to let you know that ClickHouse Cloud is Generally Available, and our never-ending resolution will always be to build the world’s fastest database with the best possible experience for our community and customers. 

Before we talk about all of the goodness available in GA, let’s take a look back — in April, we launched a private preview of ClickHouse Cloud, which progressed to our public beta [announcement](https://clickhouse.com/blog/clickhouse-cloud-public-beta) in October. It is thanks to the thousands of users who tried the service and gave us feedback that we’re able to now progress to General Availability. If you haven’t had a chance to try ClickHouse Cloud yet — **you can start a [30-day trial](https://clickhouse.cloud/signUp?loc=blog) with $300 in free credits to explore the service.** 

ClickHouse Cloud makes it simple to use ClickHouse to get the insights you need at incredible speed and scale. Its serverless architecture, powered by a separated storage and compute engine, makes the most efficient use of resources to deliver robust, secure, and scalable performance that adapts to the needs of your workload: no sizing upfront, no rebalancing later, and no resources sit idle as your environment auto-scales with your usage. 

ClickHouse Cloud is fully production-ready. It is SOC 2 Type II compliant, offers uptime SLAs, and is available in the AWS Marketplace. But we haven’t forgotten about the millions of developers who are looking to experiment and grow — we have also launched services that are purpose-built for your starter projects. And that’s not all: we’ve added a cloud-native SQL console for ClickHouse and many other delightful features for both experienced and new users. 

Check out the details below!

## SOC 2 Type II compliance
Security is a top priority here at ClickHouse, and we’ve been working hard to achieve SOC 2 Type II compliance for GA. 

This certification demonstrates that we conform to industry standards with regard to security, availability, data processing integrity, and confidentiality. We have always treated your services with the utmost attention and care, but now you can have the peace of mind knowing that independent auditors agree. For more on what this entails, check out our recent [blog post](https://clickhouse.com/blog/clickhouse-cloud-is-now-soc-2-type-ii-compliant) on this subject.

As part of our ongoing commitment to security, we have also launched our [Trust Center](http://trust.clickhouse.com). This is the place to go when you want to dig deeper into our security posture and the various documentation around it.

![GA_trust_center.png](/uploads/GA_trust_center_0ce4694f2d.png)

## Uptime status and SLAs
It doesn’t matter if your service is secure, if it is down or unreliable. We know that availability and reliability is one of the most important considerations in choosing a cloud service. As a part of GA, ClickHouse Cloud now has a [public status page](https://status.clickhouse.com/). It was important for us to be transparent about the quality and reliability of our service. The status page shows regional availability and incidents (both current and historical), and supports the ability for users to subscribe to alerts on any service disruptions. 

Of course, nobody wants to experience a disruption at all, and so ClickHouse also provides an uptime SLA for users running production workloads on ClickHouse Cloud. Please [contact us](/company/contact) to learn more about our SLA policy. 

![GA_status_page.png](/uploads/GA_status_page_bb51b6d9e0.png)

##Available in the AWS Marketplace

We know many users rely on the AWS Marketplace to reduce friction between their services and to streamline their billing. We are delighted to share that as part of our GA release, ClickHouse Cloud is also available on the AWS Marketplace.  

To get started, go to the [ClickHouse Cloud AWS Marketplace listing](https://aws.amazon.com/marketplace/pp/prodview-jettukeanwrfc). When you subscribe to the AWS Marketplace, you get $300 in free credits towards your usage.

![GA_aws_marketplace.png](/uploads/GA_aws_marketplace_8f4c0a08f3.png)

## Development and Production Services designed for your use case

ClickHouse Cloud is always deployed in a redundant manner to ensure that your analytical data is highly available and secure. We run a typical production-level service across three availability zones and take care to ensure high availability during auto-scaling, idle/resume, and ClickHouse version upgrade operations. 

However, we recognize that for some use cases, users are looking for simplified services that are more cost-effective. In addition to robust production-level services, the GA release introduces Development services – still highly available and redundant, but fixed-capacity, deployed on leaner hardware and across two availability zones instead of three. 

Development services cost less than $200/mo for the most widely used AWS regions, and a lot less for intermittent development workloads that are automatically idled by ClickHouse Cloud. With this new offering, ClickHouse Cloud users can now much more effectively architect, model, and experiment with ClickHouse Cloud until they are ready to deploy the solution in production.

Check out our [pricing page](https://clickhouse.com/pricing) or simply spin up a Development service in your cloud console to find out more. 

![GA_dev_pricing.png](/uploads/GA_dev_pricing_7082cab390.png)

## Interactive SQL console for ClickHouse Cloud users

ClickHouse has a great command-line client used and loved by many. However, we know that many users prefer to use a GUI instead of a terminal.

For the ClickHouse Cloud GA release, we are also launching the new SQL console, a fully-featured workbench for ClickHouse Cloud. This UI enables database schema browsing, interactive SQL queries, auto-complete, query history and sharing, basic visualizations, and more!

The new SQL console makes it easy to learn, manage, and use ClickHouse all from your web browser.

![GA_sql_console.png](/uploads/GA_sql_console_4ea474d5a1.png)

## ClickHouse Academy
In the spirit of helping even more users learn how to use ClickHouse to unlock the potential of their analytical datasets, we are launching ClickHouse Academy, a self-paced learning center for ClickHouse. This offering includes courses that are available for free to everyone. 

Visit the ClickHouse Academy [catalog](http://learn.clickhouse.com/visitor_class_catalog) to view our free on-demand courses, and also recordings of previous live deliveries of our onboarding workshops.

![GA_academy.png](/uploads/GA_academy_c379fc9a73.png)

## New and improved integrations

We launched a curated listing of cloud-native integrations in Beta, and expanded it in GA. 

Most notably, we now have the following new integrations:
- The official [clickhouse-kafka-connect](https://github.com/ClickHouse/clickhouse-kafka-connect) sink is now in Beta
- We’ve launched [Metabase](https://clickhouse.com/docs/en/connect-a-ui/metabase-and-clickhouse/) and [Tableau](https://clickhouse.com/docs/en/connect-a-ui/tableau-and-clickhouse/) connectors for BI use cases
- A [HEX](https://hex.tech/integrations/clickhouse) connector for data scientists
- An official [Javascript language client](https://clickhouse.com/docs/en/integrations/language-clients/nodejs/), compatible with Node.js
- Native support for a community [C# language client](https://github.com/DarkWanderer/ClickHouse.Client/wiki/Quick-start) 

In addition to new integrations listed, we also improved ClickHouse Cloud support for most of our core and partner integrations including Airbyte, DBT, Python, Superset and Grafana. Finally, we have also improved the integrations UI for easier navigation, metadata display, and related content listings.

![GA_integrations.png](/uploads/GA_integrations_7dc5bbf6c8.png)

## PostgreSQL and MySQL engines, dictionaries, and SQL UDFs

We upgrade your ClickHouse services automatically to the latest release version with no downtime or disruptions. ClickHouse Cloud is currently running 22.11, so be sure to check out the latest features announced in the [last release](https://clickhouse.com/blog/clickhouse-release-22-11), such as support for Apache Hudi and Delta Lake. 

However, some features previously released in open-source ClickHouse are not yet available for self-service usage in ClickHouse Cloud, either because they are experimental or need to be re-architected in a cloud-native way. 

In Beta, we worked hard to introduce support for three most-requested ClickHouse features, and are very happy to share that as of GA, you can now use ClickHouse Cloud with support for:
- [PostgreSQL](https://clickhouse.com/docs/en/engines/database-engines/postgresql) and [MySQL](https://clickhouse.com/docs/en/engines/database-engines/mysql) integration engines for federated queries
- [Local and HTTP dictionaries](https://clickhouse.com/docs/en/sql-reference/statements/create/dictionary) for fast data lookups at query time
- [SQL user-defined functions](https://clickhouse.com/docs/en/sql-reference/statements/create/function) (or UDFs) for complex analytical queries 

And more improvements are on the way! So, check our [Cloud Compatibility](https://clickhouse.com/docs/en/whats-new/cloud-compatibility/) documentation to learn more.

## Many other improvements
We would like to thank everyone that tried out the ClickHouse Cloud service in Beta and engaged with us to share feedback. We have carefully reviewed all of your suggestions, already started to make improvements, and will continue to listen to your feedback as we iterate on our offering post GA. 

See highlights of the feedback we addressed, and check out [cloud changelog](https://clickhouse.com/docs/en/whats-new/cloud/) for more:
- Introduced multi-factor authentication in cloud console
- Streamlined auto-scaling policies
- Improved metering granularity and fidelity
- Tuned pricing dimensions and price points
- Added cloud console navigation for mobile devices
- Introduced services in the Mumbai region

## Try it out for free
To get started with ClickHouse Cloud, sign up [here](https://clickhouse.cloud/signUp?loc=blog) for a 30-day free trial.




ClickHouse Cloud is production-ready with SOC 2 Type II compliance, Uptime SLAs, and AWS Marketplace integration.

Tanya Bragin

Blog posts by Tanya Bragin

ClickHouse acquires LibreChat to democratize AI-driven analytics through the open-source Agentic Data Stack

ClickHouse Open House Roadshow NYC videos are here

chDB joins the ClickHouse family

The Unbundling of the Cloud Data Warehouse

My Journey as a Serial Startup Product Manager

ClickHouse Cloud is now Generally Available