ClickHouse^® vs Databend: Comparing AI data warehouses

Choosing between ClickHouse and Databend means deciding whether raw query speed or cloud-native flexibility matters more for your AI application. Both databases handle analytical workloads at scale, but they take fundamentally different approaches to storage, compute, and operations.

This comparison covers architecture differences, performance benchmarks for AI workloads, developer experience, and cost considerations to help you pick the right database for your use case.

ClickHouse vs Databend quick verdict

ClickHouse is a mature columnar database built for speed in real-time analytics, while Databend is a newer cloud-native data warehouse designed for flexible scaling. ClickHouse delivers sub-second query latency on analytical workloads, which makes it a strong choice for AI applications that serve features to models in real-time. Databend separates storage from compute, allowing teams to scale each independently and potentially reduce costs for workloads with unpredictable resource needs.

The decision comes down to what you value more: proven query performance with consistent low latency (ClickHouse) or cloud-native architecture with elastic scaling (Databend). For AI feature stores and real-time model inference, ClickHouse's vectorized execution and mature tooling typically deliver better results.

What is ClickHouse

ClickHouse is an open-source columnar database designed for online analytical processing. Yandex built it in 2009 to power their web analytics platform, where it handled billions of events daily with queries returning results in under a second.

The database stores data in columns rather than rows, which allows for better compression and faster reads when queries only access specific columns. This architecture makes ClickHouse fast for analytical queries that aggregate or filter large datasets, like counting events or calculating averages across millions of rows.

Real-time analytics, event logging, monitoring systems, and AI applications commonly use ClickHouse when they need fast access to historical data. It handles high-volume data ingestion while serving concurrent analytical queries, which explains why companies like Cloudflare and Uber use it for user-facing analytics features.

What is Databend

Databend is an open-source data warehouse written in Rust with a cloud-native architecture that separates storage from compute. The project launched in 2021 with a focus on making data warehousing simpler and cheaper in cloud environments.

Traditional databases couple storage and compute tightly together, but Databend stores data in object storage like Amazon S3 or Azure Blob Storage while running compute separately. This separation allows teams to scale storage and compute independently, paying only for what they use at any given time.

The project targets data analytics and warehousing workloads, particularly for teams that want to avoid managing database infrastructure. Databend emphasizes compatibility with existing SQL tools and standards, aiming to provide a simpler alternative to more complex data warehouse systems.

Architecture side by side

The architectural differences between ClickHouse and Databend affect performance, scalability, and how much operational work you'll face.

Storage format

ClickHouse uses its own MergeTree storage engine, which organizes data into parts that get periodically merged and compressed. Data is stored in columns rather than rows, which allows for better compression and faster reads when queries only access specific columns. This architecture makes ClickHouse fast for analytical queries that aggregate or filter large datasets, like counting events or calculating averages across millions of rows.

Databend stores data in Apache Parquet format on object storage, which provides good compression and columnar access. Parquet files are immutable once written, so updates and deletes require rewriting entire files or managing delete markers separately.

For AI feature stores that need frequent updates or real-time data ingestion, ClickHouse's MergeTree engine handles incremental updates more efficiently. Databend works better for append-only workloads where data is written once and read many times, like historical model training datasets.

Query execution

ClickHouse uses vectorized query execution, processing thousands of rows at a time using SIMD instructions. This approach maximizes CPU efficiency and cache utilization, translating to faster query performance on analytical workloads.

Databend implements distributed query processing with a focus on cloud-native execution. Queries break into stages that run across multiple compute nodes, with intermediate results stored in object storage when needed.

The difference shows up in latency-sensitive applications. ClickHouse typically delivers sub-100ms query latencies for well-tuned queries, while Databend's cloud-native architecture introduces overhead from network calls to object storage, usually resulting in higher baseline latencies.

Scalability model

ClickHouse scales horizontally through sharding, where data distributes across multiple servers according to a sharding key. Each shard operates independently, and distributed queries are coordinated by the server that receives the query, which aggregates results from all shards.

Databend scales by adding or removing compute nodes dynamically since storage lives separately in object storage. This elastic scaling model allows teams to increase query capacity during peak times and reduce it during off-hours.

For AI applications with predictable query patterns, ClickHouse's sharding model provides consistent low latency. For workloads with highly variable compute needs, Databend's elastic scaling offers more flexibility, though at the cost of higher query latencies.

Performance for real-time analytics

Performance characteristics differ significantly between ClickHouse and Databend for queries common in AI applications.

Latency benchmarks

ClickHouse typically achieves query latencies between 10ms and 500ms for well-optimized queries on properly indexed data. Queries that scan billions of rows can return results in under a second when the data is properly partitioned and the query uses appropriate indexes.

Databend's latency profile depends heavily on whether the query can use cached data or needs to read from object storage. Cold queries that require reading from S3 typically see latencies starting around 500ms to several seconds, while warm queries using cached data can approach ClickHouse's performance.

For AI feature stores serving real-time model inference, ClickHouse's lower baseline latency usually provides a better user experience. Databend works well for batch feature generation or offline model training where latency matters less than cost efficiency.

Throughput benchmarks

ClickHouse handles thousands of concurrent queries per second on properly sized clusters, with query throughput scaling linearly as you add more shards. The database maintains consistent performance even under high concurrency because each shard processes queries independently.

Databend's throughput scales by adding compute nodes, though the shared storage layer can become a bottleneck for write-heavy workloads. Read throughput scales well when queries can be distributed across multiple compute nodes and the working set fits in cache.

High-traffic AI applications that serve features to many concurrent users typically see better throughput with ClickHouse.

Concurrency scaling

ClickHouse maintains query performance under high concurrency through its architecture, where each server handles queries independently. As long as the cluster has sufficient CPU and memory resources, adding concurrent queries doesn't significantly degrade individual query performance.

Databend scales concurrency by spinning up additional compute nodes, which can happen automatically based on query load. This approach works well for unpredictable traffic patterns, though there's a delay of several seconds to minutes when scaling up compute resources.

Developer experience and tooling

The ease of integrating ClickHouse and Databend into AI/ML workflows varies based on the tools and workflows each system supports.

Local development workflow

ClickHouse runs locally using Docker or native binaries, allowing developers to test queries and schema changes on their laptops. The database starts in seconds and uses minimal resources for small datasets, making it practical for local development and testing.

Databend also supports local development through Docker containers, though the experience is optimized for cloud deployments. Setting up a local Databend instance that mimics production behavior requires configuring object storage emulation, which adds complexity.

CI/CD and versioning

ClickHouse schema changes are typically managed through SQL migration scripts, similar to traditional databases. Tools like Liquibase or custom migration frameworks can track and apply schema changes across environments.

Databend follows similar patterns for schema management, with support for SQL-based migrations. The separation of storage and compute means schema changes don't require data movement, which can make large-scale schema changes faster.

Observability and tuning

ClickHouse provides detailed system tables that expose query performance metrics, table statistics, and cluster health information. Developers can query system tables to understand query execution plans, identify slow queries, and optimize table structures.

Databend offers similar observability through system tables and integration with cloud monitoring tools. Query profiling shows how queries distribute across compute nodes and where time is spent, though the tooling is less mature than ClickHouse's ecosystem.

Feature comparison for AI workloads

Specific capabilities matter for machine learning and AI applications that process and serve data at scale.

Vector search support

ClickHouse supports storing vector embeddings as Array columns and provides functions for calculating distances between vectors.

Databend also supports array types for storing embeddings and provides basic vector operations with 23× faster similarity search using HNSW indexing. However, the ecosystem around vector search is less developed compared to ClickHouse, with fewer examples and optimization patterns available.

Streaming ingestion

ClickHouse handles streaming ingestion through multiple methods:

• Direct inserts: High-frequency writes directly to tables
• Kafka integration: Native connectors for streaming platforms
• Buffer tables: Batch small inserts for better performance

The database can ingest millions of rows per second while serving concurrent queries, making it suitable for real-time AI feature updates.

Databend ingests data primarily through batch loading from object storage or through its streaming API. The architecture optimizes for bulk loading rather than high-frequency small inserts, which can make real-time ingestion more challenging.

Materialized views and joins

ClickHouse materialized views automatically maintain pre-computed aggregations as new data arrives, which can significantly speed up queries for common aggregation patterns.

Databend supports materialized views for pre-computing aggregations, though the implementation differs from ClickHouse's incremental updates. Join performance in Databend depends on data distribution and whether the optimizer can push join operations down to individual compute nodes.

When to choose each engine

The decision depends on your team's priorities, technical requirements, and operational capabilities.

Small team prototypes

For small teams building AI application prototypes, ClickHouse provides faster time to value. You can run ClickHouse locally or use a managed service like Tinybird to get a production-ready API in hours rather than days.

Databend's cloud-native architecture can be more complex to set up initially, though it may reduce operational overhead once configured. Teams comfortable with cloud infrastructure and object storage will find Databend's model familiar.

Petabyte scale dashboards

At petabyte scale, both systems can handle the data volume, but operational considerations differ. ClickHouse has proven deployments at companies processing petabytes of data daily with consistent query performance.

Databend's architecture theoretically scales to petabytes through its use of object storage, though fewer production examples exist at this scale. The separation of storage and compute can make it easier to scale storage independently, but query performance at petabyte scale depends heavily on data organization and caching strategies.

Feature store for ML

AI feature stores need to serve features to models with low latency while handling frequent updates from streaming data sources. ClickHouse's combination of fast writes, sub-second reads, and materialized views makes it well-suited for this use case.

Databend can work as a feature store for batch ML workflows where features are computed periodically and served to training jobs. The higher query latency and batch-oriented ingestion make it less suitable for real-time feature serving to online models.

Pricing and total cost of ownership

Cost analysis varies significantly based on deployment model and usage patterns.

Running ClickHouse yourself requires servers with sufficient CPU, memory, and fast storage, preferably NVMe SSDs. A production cluster typically starts with three nodes for high availability, each with 32-64 CPU cores, 256-512GB RAM, and several terabytes of NVMe storage.

Databend's self-hosted option requires compute servers plus object storage, which can be cheaper than local NVMe storage at scale. Compute servers need less local storage since data lives in object storage, potentially reducing hardware costs.

ClickHouse Cloud offers fully managed ClickHouse with pricing based on compute and storage usage. Databend Cloud provides a managed service with pricing based on compute time and storage volume. The separation of storage and compute can result in lower costs for workloads with high storage needs but intermittent compute requirements, with Databend Cloud reporting up to 90% cost savings compared to traditional coupled architectures.

Tinybird as a managed ClickHouse option

Tinybird provides a managed ClickHouse platform designed specifically for developers building AI and analytics features into their applications. Unlike other managed ClickHouse services that focus on database administration, Tinybird emphasizes developer experience and API-first integration.

The platform handles ClickHouse cluster management, scaling, and optimization automatically, allowing developers to focus on defining data pipelines and queries rather than infrastructure. Tinybird's built-in CI/CD workflow lets you define data sources and queries as code, test them locally, and deploy to production with a single command.

Key features for AI applications include sub-second API endpoints where queries automatically become REST APIs with authentication built in, local development for testing ClickHouse queries using Docker before deploying to production, streaming ingestion with built-in connectors for Kafka and webhooks, and materialized views with automatic incremental updates for pre-computed aggregations.

Sign up for a free Tinybird account to start building with ClickHouse without managing infrastructure.

FAQs about ClickHouse vs Databend

Can ClickHouse replace a traditional data warehouse?

ClickHouse excels at analytical workloads but lacks some traditional data warehouse features like complex transactions and strong consistency guarantees. It works best for read-heavy analytics rather than operational data storage that requires frequent updates or deletes.

Does Databend support materialized views?

Databend supports materialized views for pre-computing aggregations, though the implementation differs from ClickHouse's incremental materialized views. Databend's materialized views are refreshed on a schedule rather than updating automatically as new data arrives.

How do I store embeddings in ClickHouse?

ClickHouse stores vector embeddings using Array data types, with specialized functions for similarity calculations and vector operations. For example, Array(Float32) columns can store embeddings, and functions like cosineDistance() calculate similarity between vectors.

What are the downsides of ClickHouse for AI workloads?

ClickHouse requires more operational expertise and manual tuning compared to cloud-native alternatives, though managed services like Tinybird address these challenges. The database also uses more expensive local storage compared to object storage, which can increase costs for very large datasets.

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