ClickHouse Is The Best Data Platform For Building AI Initiatives

Companies across the globe and across industries are currently executing on their AI strategies. As they do this, they will find that this dramatically increases the demands on their data infrastructure.

In the past, maybe a business had some static dashboards which were updated overnight and which people pulled up a few times a day. In a world of AI agents however, we are moving to a world where agents are constantly interacting with data both in the background and interactively, and with more demanding and dynamic query patterns.

Clickhouse is already becoming the leading database to serve AI native businesses. For instance, Open AI, Anthropic and Tesla are public case studies. Many AI native businesses who do not have the legacy are choosing ClickHouse as their greenfield platform of choice.

More importantly, we are also going to see end user organizations and enterprises begin to take advantage of Clickhouse to power their internal AI solutions. Today, many of these businesses might have their data trapped in databases such as SQL Server or Oracle, or even cloud data warehouses such as Snowflake or Redshift. The challenge with all of these technologies is they're not fast enough and concurrent enough to support the demands of interactive AI workloads. In this article I will explain why I believe ClickHouse is uniquly placed to solve these challenges.

Performance

The first reason why Clickhouse is a good fit for AI workloads is it's performance in terms of low query latency. For intance, one common use case is to connect LLMs to data, so users can ask questions with natural language. In that situation, having queries answered very quickly leads to a much better experience for users. Fast responses will mean that they will be able to go much deeper into their data, ask questions from different angles and conduct more complex scneario modelling.

Concurrency

Agentic workloads are highly concurrent by their nature. A individual request into an LLM could trigger tens of queries to access all of the relevant context. Clickhouse is able to accept those queries and execute them in parallel with more efficiency than any other databases in my market.

Freshness

For a lot of agentic workloads. It's going to be important to expose real time data to the agent. A lot of enterprise IT is based on batch workloads where data might be loaded overnight or maybe every couple of hours at best, but the challenge is that this can lead to our agents giving incorrect information if they don't have the latest data. I think generally moving towards more. real time data has been a trend, but it will be accelerated with the move to AI.

Price Performance

Because ClickHouse is fast, highly concurrent and scalable, you can get away with relatively small clusters even with demanding agentic workflows. As a business, you won't have to keep throwing hardware at your cluster to meet exponentially growing workloads such as agentic analytics, keeping control over costs. To me, this removes some of the fear of costs spiralling out of control if I have a nice fast and scalable infrastructure to build on.

Perfect Fit for Machine Learning Workloads

As a data store, ClickHouse is a great fit for machine learning workloads such as training data, experiments, feature stores or for serving real time inference. I have built a number of systems like this before LLMs were cool and ClickHouse is a strong proposition for traditional machine learning workflows.

Observability

ClickHouse is highly capable as an observability solution, Incorporating logs, metrics, and traces in a AI world where everything is less deterministic. observability is going to be increasingly important. With Clickhouse, what we can do is bring both our business data and our observability data, such as loads, metrics, and traces into a same database, manage them the same way, and potentially drawing across those 2 data sets. Clickhouse have also recently acquired lang views, which is a LLM observability platform which stores its backend data within Clickhouse. So we can use this as a combined stack to monitor and evaluate our LLM systems.

Expanding AI Capabilities Through Acquisitions

Through their acquisitions, ClickHouse are also expanding to an even wider surface area with a good story around AI. For instance, they acquired LangFuse for observability of LLMs and supporting the development of LLM powered applications, and Librechat to support conversational BI and agentic analytics.

In time this will give one unified platform for running, building and monitoring AI solutions and apps. Businesses can use all of these tools and consolidate their data in one location.

Making the Right Choice

Every enterprise technology team in the world is probably thinking about how to implement AI now, and doing it on the wrong data platform could be a big and expensive mistake. ClickHouse should always be evaluated as the datastore that