Why ClickHouse Is The Best Data Platform For Agentic AI

This article is one of a series looking at using ClickHouse for AI. Articles in the series include "Why ClickHouse Is The Best Data Platform For Agentic AI", "What Do we Mean By Agentic Analytics", "Using Open Source and Open Weight LLMs With ClickHouse" and "Self Hosting An Open Source Model With ClickHouse".

Companies across the globe are currently executing on their AI strategies. As they move from planning to implementation, they are finding that their new AI workloads dramatically increase the demands on their data infrastructure.

In the past, businesses ran on static dashboards which were updated infrequently and consulted a few times per day by a relatively small number of people. In the age of AI however, we are moving to a situation where large numbers of agents are constantly interacting with data, with much more demanding and dynamic query patterns. As well as internal users, many of these agentic systems also need to be exposed to end users of digital products, compounding the challenge.

As this happens, current data infrastructure begins to creak. Queries return too slowly, everything begins to slow down under high degrees of concurrency, costs spiral as more compute needs to be added, and there may also be outages or stability issues as volume increases by an order of magnitude in a short space of time.

The challenge is that most companies have their data trapped in databases which are not fit for the demands of AI agents. This includes both legacy data technologies and more modern cloud hosted data warehouses. Most databases in the market share the challenge that they're fundamentally not fast and concurrent enough to support the demands of interactive AI workloads at a sustainable price point.

Why ClickHouse?

ClickHouse is a real time database which fortunately is almost uniquely suited to this challenge. Due to its low latency and scalability, it is increasingly playing a role as part of AI initiatives. It is the database of choice for AI natives including headline case studies such as OpenAI and Anthropic, and is experiencing rapid growth within large traditional enterprise in large part driven by the AI agenda.

In this article we will unpack further why we believe ClickHouse is uniquely placed to sit at the centre of any business's agentic AI strategy. In subsequent articles we will bring this to life with demos and examples of how it is being consumed in practice.

Performance

The first and most obvious strength of ClickHouse for agentic workloads is its fast query performance over large data volumes. Rapid responses mean that agents can respond immediately, making them viable for use cases such as personalisation and fraud detection, whilst humans driving the agents get a much more interactive and engaging experience.

Concurrency

Agentic workloads are highly concurrent by their nature. An individual request into an LLM could trigger tens of queries into the database to gather all of the relevant context. ClickHouse is able to accept those queries and execute them in parallel with minimal reduction in latency. It can also scale as more agents and more users are carrying out their work in parallel. This capability of speed combined with concurrency is a perfect for fit for the agentic AI workload.

Freshness

Many agentic workflows benefit from real time data. Real time data makes agents viable for more use cases and improve the agent's ability to give the best and most accurate answer. Where many databases and their ecosystems are based on batch processing, ClickHouse is fundamentally a real time database, well suited to ingesting real time streaming data which can be made immediately available to agents.

Price Performance

Because ClickHouse is fast, highly concurrent and scalable, you can run demanding workloads with relatively small compute clusters. As a business, you won't have to keep throwing compute at your cluster to meet exponentially growing AI workloads, making it easier to keep control over costs. With ClickHouse Cloud, you will also benefit from autoscaling, allowing the data tier to scale up to meet peak capacity and scale down in periods of less demand in order to save money.

SQL As The Interface For Agents

ClickHouse is a SQL based database. We have found that even small quantised open source language models are exceptionally good at generating SQL, because it is one of the most widely represented languages in their training data. Rather than interacting with a bespoke query interface that a model has never seen, SQL allows the agent to express itself in a language it already understands well, dramatically improving accuracy and reduces the amount of scaffolding you need to build.

Materialised Views And Pre-Aggregation

One of ClickHouse's most important features is its materialised views. When an agent needs a rolled-up metric or a summary, it can read from a pre aggregated materialised view that is already up to date, rather than scanning billions of raw rows on every request. This further keeps latency low and compute costs down even as the number of agents grows.

Machine Learning

Though many companies will stop at deploying agentic workflows, many others will go deep into training and fine tuning their own models. ClickHouse has always been a great fit for these use cases, such as storing training data, tracking experiments, managing feature stores or serving real time inference. This gives you a data platform which can grow with you as you move into more sophisticated and bespoke LLM use cases.

Agentic Data Stack

ClickHouse have acquired and integrated two products which together add up to an integrated offering for AI. Together, these are referred to as the agentic data stack.

The first product is Langfuse, which is a platform for observability of LLMs and supporting the development of LLM powered applications. Langfuse allows you to track the performance, quality and cost of your LLM based systems giving you the ability to improve and govern them at scale.

The second product is Librechat, which is a frontend that supports conversational business intelligence and agentic analytics. It allows you to connect to any model to either interact with your data or build agents. This acts as a frontend to your ClickHouse based data which can be used by business users and analysts.

In addition, ClickHouse have developed other AI aligned capabilities such as an MCP server and a set of agent skills which all combine to make it easier to build and deploy end to end AI solutions.

Open Source And No Lock-In

ClickHouse is open source at its core, which is an important consideration when you are placing a database at the centre of your AI strategy. You can run it yourself, run it in the cloud, or move between the two. It also has excellent capabilities around data lakes and open standard data formats such as Iceberg. For businesses that are nervous about betting their AI future on a proprietary, closed platform, the ability to avoid lock-in and retain control over where and how their data lives is important risk reduction.

Making the Right Choice

Almost every enterprise technology team in the world is currently thinking about how to implement AI. Building these solutions on the wrong data platform could be a big and expensive mistake right at the heart of your AI strategy. ClickHouse should be considered due to its unique performance profile which is perfectly suited for the needs of AI agents.

In the next article in this series, we look at what agentic analytics actually means in practice — and why it is more than just text-to-SQL.