How to deploy and manage ClickHouse^® on AWS

Running ClickHouse on AWS gives you the performance of a columnar analytical database with the scalability and integration options of cloud infrastructure, leveraging AWS's 37.7% IaaS market share dominance. The challenge is deciding whether to self-host on EC2, use a managed service, or deploy through a BYOC model that keeps resources in your AWS account.

This guide walks through deployment options, ingestion patterns, cost optimization strategies, and vendor comparisons to help you choose the right approach for your workload.

What is managed ClickHouse on AWS

Managed ClickHouse on AWS refers to hosted database services that run ClickHouse on Amazon Web Services infrastructure without requiring you to configure servers, tune performance, or handle operational maintenance. ClickHouse is a column-oriented database designed for online analytical processing (OLAP), which means it excels at running analytical queries on large datasets.

When you use a managed service, the provider handles cluster provisioning, software updates, backups, and scaling while you focus on writing SQL and building features. Most managed ClickHouse services on AWS offer automatic scaling based on workload, data tiering to S3 for cost optimization, and integration with AWS networking features like VPCs and PrivateLink for secure connectivity.

The main advantage is speed. Instead of spending weeks setting up infrastructure and learning ClickHouse operations, you can start querying data within minutes of signup.

Deployment models for hosted ClickHouse in AWS

You have three main options for running ClickHouse on AWS, each with different tradeoffs between convenience and control.

Multi-tenant SaaS clusters

Multi-tenant deployments run your databases on shared infrastructure managed by the service provider. Setup takes minutes because the provider maintains pools of pre-configured ClickHouse clusters that you can start using immediately.

This model works well for development environments, proof-of-concept projects, and production workloads that don't require dedicated hardware. Costs stay lower for smaller workloads since compute and storage resources are shared across multiple customers, though providers implement resource limits and query prioritization to prevent one customer from affecting others.

Bring your own cloud in-VPC

BYOC (Bring Your Own Cloud) deployments provision dedicated ClickHouse clusters inside your AWS account and VPC, a model gaining traction as hybrid/multi-cloud adoption rises at 28.20% CAGR. You get full network control, can implement custom security policies, and meet compliance requirements that mandate data residency in your infrastructure.

The provider still manages the ClickHouse software, updates, and scaling automation, but the EC2 instances and EBS volumes run in your AWS environment. This approach costs more than multi-tenant options but provides the isolation that regulated industries often require.

Self-managed on EC2 or EKS

Self-managed deployments give you complete control over the ClickHouse installation, from kernel parameters to table engine configurations. You provision EC2 instances or Kubernetes clusters, configure storage, set up monitoring, and handle all operational tasks yourself.

This model makes sense when you have deep ClickHouse expertise in-house and need customizations that managed services don't support. However, it requires ongoing engineering time for maintenance, security patches, and performance tuning.

Step-by-step guide to deploy a managed ClickHouse cluster on AWS using Tinybird

The deployment process varies between providers, so in this workflow I'll walk you through how to set up your managed ClickHouse on AWS using Tinybird.

1. Sign up and create a workspace

Tinybird starts with account creation and workspace setup. A workspace acts as a logical boundary for your databases, queries, connections, etc.

You can sign up to Tinybird for free here.

During signup, you'll select an AWS region for your deployment. Pick a region close to your data sources or application servers to minimize network latency between your app and the database.

curl -L tinybird.co | sh # Install Tinybird CLI
tb login --host us-east-1 # Login/signup and create a workspace in AWS us-east-1

2. Define a data source and pipe locally

With Tinybird, you define your ClickHouse tables as data sources using .datasource files. A data source file specifies the schema, table engine, and sorting keys in a declarative format that you can version control alongside your application code.

Here's an example:

SCHEMA >
    `timestamp` DateTime `json:$.timestamp`,
    `session_id` String `json:$.session_id`,
    `action` LowCardinality(String) `json:$.action`,
    `version` LowCardinality(String) `json:$.version`,
    `payload` String `json:$.payload`

ENGINE "MergeTree"
ENGINE_SORTING_KEY "timestamp"
ENGINE_PARTITION_KEY "toYYYYMM(timestamp)"
ENGINE_TTL "timestamp + toIntervalDay(180)"

Next, create a .pipe file to write your first SQL query. Pipes in Tinybird are SQL transformations that can be chained together and exposed as API endpoints.

Here's an example multi-node endpoint pipe with query parameters:

TOKEN dashboard READ
DESCRIPTION endpoint to get sales by hour filtering by date and country

TAGS sales

NODE daily_sales
SQL >
    %
    SELECT day, country, sum(total_sales) as total_sales
    FROM sales_by_hour
    WHERE
    day BETWEEN toStartOfDay(now()) - interval 1 day AND toStartOfDay(now())
    and country = {{ String(country, 'US')}}
    GROUP BY day, country

NODE result
SQL >
    %
    SELECT * FROM daily_sales
    LIMIT {{Int32(page_size, 100)}}
    OFFSET {{Int32(page, 0) * Int32(page_size, 100)}}
TYPE ENDPOINT

You can test everything locally using tb dev, which runs Tinybird's ClickHouse instance and other infra in Docker on your machine.

tb local start  # Start local ClickHouse container
tb dev          # Open interactive SQL console

4. Deploy to AWS with CI/CD

Once your data sources and pipes work locally, deploy them to production with tb --cloud deploy.

Tinybird integrates with GitHub Actions, GitLab CI, and other CI/CD tools. You can automate deployments by running tb deploy in your pipeline, treating your ClickHouse infrastructure as code that follows the same review and testing processes as your application code.

Deploying will create your ClickHouse tables and queries as well as hosted API endpoints on Tinybird-managed AWS infrastructure.

5. Validate query latency and cost

After deployment, run test queries against your production endpoints to verify performance. Most managed services provide dashboards showing query latency percentiles, memory usage, and disk I/O in real time.

Check your billing dashboard to understand how your usage translates to costs. Running a few days of realistic workload helps you forecast monthly expenses before scaling up to handle production traffic.

Ingesting data from S3, Kinesis, and Kafka into AWS ClickHouse

Getting data into ClickHouse from AWS services requires connectors that handle authentication, serialization, and error handling automatically.

Streaming with Kafka connector

Tinybird offers a Kafka connector that creates a consumer for any Kafka-compatible streaming platform, including Amazon MSK (Managed Streaming for Kafka) or self-hosted Kafka clusters. You configure the connector with your MSK bootstrap servers, topic names, and authentication credentials through a web interface or configuration file.

The connector handles offset management and schema registry integration without additional code. For high-throughput topics, Tinybird will scale to keep ingestion lag low even during traffic spikes.

Real-time Kinesis streaming

You can use Tinybird's Events API to forward Kinesis Streams to your managed ClickHouse instance.

More info on streaming from Kinesis to Tinybird can be found here.

Batch ingest from S3

For large historical datasets or periodic batch imports, S3 provides a cost-effective staging area. ClickHouse's s3 table function can Parquet, CSV, or JSON files directly from S3 buckets without requiring intermediate storage or ETL tools.

In addition, you can use Tinybird's managed S3-to-ClickHouse connector to schedule file imports using cron schedules.

Exposing sub-second queries through secure APIs

Tinybird's managed ClickHouse becomes useful as an application backend when you can query it from your code without managing database connection pools or writing API servers.

Creating parameterized pipes

Tinybird's parameterized queries accept user inputs safely without SQL injection risks. In Tinybird, you use template syntax like {{String(user_id)}} to define parameters with type validation built in.

NODE filtered_events
SQL >
  %
  SELECT event_name, count() as event_count
  FROM events
  WHERE user_id = {{String(user_id)}}
  GROUP BY event_name

The parameter system validates types, applies defaults, and rejects malformed inputs before the query executes. This prevents common security vulnerabilities while keeping your SQL readable and maintainable.

Managing auth tokens and rate limits

API endpoints require authentication tokens to prevent unauthorized access. Tinybird offer token scoping, where each token has specific read or write permissions on individual ClickHouse tables or query lambdas (pipes).

Rate limiting protects your cluster from excessive query load. You can set per-token limits based on requests per second or concurrent queries, preventing one client from monopolizing cluster resources and degrading performance for others.

Scaling, monitoring, and backups for managed ClickHouse

Operational concerns like scaling, observability, and disaster recovery work differently depending on whether you choose a managed service or self-host.

Autoscaling compute and storage

Managed services monitor query load and automatically add compute capacity when CPU or memory utilization crosses thresholds you define. Some providers offer serverless tiers that scale to zero during idle periods, reducing costs for intermittent workloads like internal dashboards or development environments.

Storage autoscaling expands disk capacity as your data grows. ClickHouse's tiered storage moves older data to S3 automatically based on time-to-live policies, keeping hot data on fast SSDs while archiving cold data to cheaper object storage.

Observability dashboards and alerts

Built-in monitoring dashboards show query performance metrics, including execution time, rows scanned, and memory usage per query. You can identify slow queries, optimize table schemas, and adjust sorting keys based on actual usage patterns rather than guesswork.

Alert configuration lets you receive notifications when query latency exceeds thresholds, disk usage approaches limits, or replication lag grows. Integration with PagerDuty, Slack, or email means your team knows about issues before they impact users.

Point-in-time recovery and snapshots

Managed services take automatic backups at regular intervals, typically every few hours. Point-in-time recovery allows you to restore your database to any moment within the retention window, usually 7 to 30 days depending on your plan.

Snapshot-based backups store compressed copies of your data in S3, providing protection against accidental deletions or data corruption. Some providers offer cross-region backup replication for additional disaster recovery protection, though this increases storage costs.

Cost breakdown and optimization tips for managed ClickHouse

Understanding how managed ClickHouse services charge helps you optimize spending without sacrificing performance.

Storage tier choices and TTL

ClickHouse supports tiered storage where recent data lives on fast SSDs and older data moves to S3 automatically. Time-to-live (TTL) policies define when data transitions between tiers based on age or specific column values.

For example, you might keep the last 30 days on SSD for fast queries and move everything older to S3, reducing storage costs by 70% while maintaining query performance for recent data. TTL policies can also delete data entirely after a retention period expires, which is useful for compliance requirements or managing storage growth.

Compute credit planning

Many managed services use a credit-based pricing model where you purchase compute credits that get consumed based on query complexity and execution time. Simple queries on small datasets use fewer credits than complex aggregations on billions of rows.

Monitoring your credit burn rate helps you forecast monthly costs accurately. If you consistently exceed your budget, consider optimizing queries, adjusting table engines, or upgrading to a plan with better credit efficiency.

Compression and partition strategy

ClickHouse achieves 10x to 100x compression ratios on columnar data, but codec choice matters for both storage costs and query performance. The ZSTD codec provides high compression with reasonable CPU overhead, while LZ4 prioritizes speed over compression ratio.

Partition keys determine how ClickHouse organizes data on disk. Partitioning by date (like toYYYYMM(timestamp)) allows efficient data pruning and TTL application, but too many partitions (more than a few hundred) can slow down queries and increase memory usage.

Vendor comparison of managed ClickHouse services

Several providers offer managed ClickHouse on AWS, each with different strengths, pricing models, and feature sets.

Deployment flexibility

ClickHouse Cloud offers both shared infrastructure and BYOC options, with deployments available in most AWS regions. Altinity focuses exclusively on BYOC and dedicated clusters, providing more control at the expense of higher minimum costs.

Tinybird runs multi-tenant infrastructure optimized for developer workflows. You don't manage clusters directly; instead, you define data sources and pipes that Tinybird provisions automatically across its managed fleet.

Ingestion and API tooling

Most managed ClickHouse services provide SQL interfaces and standard ClickHouse client libraries for connecting from your application code. Tinybird adds an API layer that generates REST endpoints from your SQL queries, eliminating the need to write API servers or manage database connection pools.

Connectors for Kafka, Kinesis, and S3 are available across providers, though configuration complexity varies. Some require manual setup of consumer groups and offset management, while others offer one-click connector deployment through web interfaces.

Support and SLA differences

Support tiers typically correlate with pricing. Entry-level plans offer email support with 24-48 hour response times, while enterprise plans include dedicated Slack channels, phone support, and guaranteed response SLAs measured in hours.

Uptime SLAs range from 99.5% to 99.99% depending on the service tier. Higher SLAs come with multi-AZ deployments, automatic failover, and credits for downtime that exceeds the guarantee.

Pricing models

ClickHouse Cloud charges based on compute hours and storage volume, with separate rates for SSD and S3 storage. Serverless tiers add per-query pricing that scales to zero when idle, which works well for intermittent workloads.

Altinity uses custom pricing based on cluster size, support level, and whether you choose shared or dedicated infrastructure. Minimum commitments typically start at several thousand dollars per month for production deployments.

Tinybird offers a free tier with limited resources, then plan-based pricing that scales with compute and storage. There are no minimum commitments, making it accessible for small projects that grow over time.

Common pitfalls and how to avoid them

Inefficient merge trees

The MergeTree table engine is ClickHouse's workhorse, but it requires careful configuration. Your primary key determines how data is sorted on disk, which significantly affects query performance.

Choose primary keys based on your most common query filters. If you frequently filter by user_id and timestamp, use ORDER BY (user_id, timestamp). Avoid high-cardinality columns like UUIDs as the first key, since this creates too many small parts that slow down merges and increase memory overhead.

Under-provisioned disk bandwidth

ClickHouse is I/O intensive, especially during merges and large scans. AWS EBS volumes have different performance tiers (gp2, gp3, io2) with varying IOPS and throughput limits that directly affect query speed.

If queries slow down during peak hours or you see high disk wait times in monitoring dashboards, your storage might be the bottleneck. Upgrading to gp3 volumes with provisioned throughput or using instance types with NVMe SSDs can resolve performance issues without changing your schema.

Missing timezone conversions

ClickHouse stores DateTime values in UTC by default, but many applications work with local times. Forgetting to convert timezones leads to off-by-hours errors in reports and dashboards that can be difficult to debug.

Use DateTime('America/New_York') column types to store timezone-aware data, or convert at query time with toTimeZone(). Always be explicit about which timezone your data represents, especially when ingesting from sources in different regions.

Next steps to build with Tinybird

Tinybird provides a managed ClickHouse platform designed for developers who want to integrate real-time analytics into their applications without managing infrastructure. You can define data sources and SQL pipes locally, test them with tb dev, and deploy production APIs with tb push in seconds.

The platform handles cluster scaling, monitoring, and backups automatically while you focus on writing SQL and building features. Sign up for a free Tinybird account to start building. The CLI and documentation walk you through creating your first data source and API endpoint in minutes.

FAQs about managed ClickHouse on AWS

How long does managed ClickHouse take to deploy on AWS?

Multi-tenant deployments provision in 2-5 minutes since they use pre-existing infrastructure. BYOC deployments take 15-30 minutes because the provider creates VPC resources, EC2 instances, and load balancers in your AWS account before installing ClickHouse.

Can I migrate existing ClickHouse tables to managed AWS hosting without downtime?

Yes, most providers support dual-write patterns where your application writes to both the old and new clusters simultaneously. You can backfill historical data in parallel, then switch reads to the new cluster once replication catches up.

Does managed ClickHouse support AWS PrivateLink connections?

Enterprise tiers typically support PrivateLink, which creates private VPC endpoints for secure connectivity without exposing your database to the public internet. This feature is important for compliance requirements that prohibit data from traversing public networks.

Can I run the same ClickHouse SQL locally and in managed AWS clusters?

Yes, ClickHouse maintains SQL compatibility across versions and deployment types. However, some managed services restrict access to system tables or disable specific functions for security reasons. Tinybird's local development environment runs the same ClickHouse version as production, so queries work identically in both environments.

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