ClickHouse® open source is free to download and use, but, of course, running it isn't free. As with any self-hosted open source software, you pay for servers, storage, bandwidth, and the engineering time to keep everything working. In our experience, most teams underestimate the hidden costs of self-hosting, especially the operational overhead that can consume an engineer's time once the cluster is in production.

This article breaks down the cost of self-hosting ClickHouse®, from expected "hard costs" like infrastructure expenses to "soft costs" like engineering overhead and 3 AM on call pages, and (hopfeully) shows you that a managed ClickHouse® service like Tinybird can actually cost less than doing it yourself.

Self-hosted ClickHouse® price components

Self-hosting ClickHouse® is free from a licensing perspective since it's open source under Apache 2.0, but you still pay for the infrastructure that runs it. The total cost breaks down into compute resources for running queries, storage for your data, network bandwidth for serving results, backup infrastructure, monitoring tools, and the engineering time to manage everything.

Infrastructure costs generally scale linearly with your compute. A small analytics workload might run on a single server, while a production deployment typically requires at least three nodes for high availability.

Compute and memory

Virtual machines or bare metal servers form the foundation of your ClickHouse® deployment. You pay for CPU cores and RAM based on your query concurrency and data processing requirements. A typical production node will probably 8-16 CPU cores with 64-128 GB of RAM, though your needs vary based on query complexity and the number of simultaneous users. Different deployment models offer various tradeoffs between cost and operational complexity.

Cloud providers charge by the hour for compute resources. AWS m6i instances cost around $0.192 per hour for 4 vCPUs and 16 GB RAM, while Hetzner's CCX instances run about $0.40 per hour for similar specs.

Block storage and object storage

ClickHouse® stores data on two types of storage systems. Primary storage uses fast SSDs attached to your compute instances for active data that queries access frequently. This storage is expensive but provides the low-latency reads that ClickHouse® needs for sub-second query performance.

Object storage like AWS S3 or Google Cloud Storage costs much less and works well for historical data that you query infrequently. S3 Standard costs $0.023 per GB monthly, while even cheaper tiers like Glacier Instant Retrieval run $0.004 per GB. You can configure ClickHouse® to automatically move older data to object storage, reducing your overall storage costs. The tradeoff is slower query performance when accessing cold data, since object storage has higher latency than local SSDs.

Network egress

Data transfer costs apply when query results leave your cloud provider's network. If your application servers run in the same region as your ClickHouse® cluster, internal data transfer is often free or very cheap.

Egress pricing varies significantly between providers. AWS charges $0.09 per GB for the first 10 TB transferred out to the internet each month, while other providers like Hetzner include generous transfer allowances.

Backups and disaster recovery

Production ClickHouse® deployments require regular backups and cross-region replication for disaster recovery. Backups consume additional storage, typically in object storage, and you pay for both the storage capacity and the compute time to create and restore backups.

Replication to a secondary region doubles your infrastructure costs since you're running a complete copy of your cluster in another location. Some teams compromise by backing up to object storage in a different region rather than maintaining a full replica, which reduces costs but increases recovery time after an outage.

Observability stack

Monitoring ClickHouse® clusters requires dedicated infrastructure for metrics, logs, and alerts. Tools like Prometheus, Grafana, and the ELK stack help track query performance, disk usage, and system health.

You can run monitoring tools on the same servers as ClickHouse® to save money, but this approach risks losing observability data during an outage. Most production deployments use separate infrastructure for monitoring, which provides better reliability but increases costs.

Support and SLAs

Enterprise support contracts from ClickHouse® Inc. or third-party vendors provide guaranteed response times and expert help. Alternatively, you rely on community support through forums and GitHub, which is free but offers no guaranteed response times.

Internal engineering time represents another support cost. Someone on your team handles routine maintenance, performance tuning, and incident response. This ongoing operational burden can consume 10-20% of an engineer's time for a stable cluster, or much more during scaling challenges or outages.

Monthly cost calculator for a typical workload

Let's walk through sizing a production ClickHouse® deployment for a realistic analytics workload. This example assumes you're processing 100 GB of new data daily with 50 concurrent users running queries throughout the day.

Step 1: size your cluster

Start with three nodes for high availability, each with 8 vCPUs and 32 GB RAM. This configuration handles modest query workloads while providing enough resources for ClickHouse®'s in-memory operations and basic replication. On AWS using m6i.2xlarge instances at $0.384 per hour on demand, your compute costs would run about $841 per month for the cluster. You can reduce AWS costs significantly with spot instances (60-70% savings), reserved instances (up to 72% savings), or intelligent autoscaling.

As your query load and data volume grows, you'll likely need to scale vertically to larger instance types (16 vCPUs, 64 GB RAM) or horizontally by adding more nodes. Each scaling decision roughly doubles compute costs, so a six-node cluster of m6i.2xlarge instances would cost approximately $1,682 per month.

Step 2: forecast storage growth

With 10 GB of daily data ingestion, you accumulate 300 GB per month. ClickHouse® compresses data efficiently, typically achieving 10:1 compression ratios, so your actual storage usage might be around 30 GB per month. Using AWS EBS SSD storage at $0.10 per GB-month, primary storage costs roughly $30 per month initially.

After a year, you'll have accumulated 3.6 TB of compressed data. Moving data older than 90 days to S3 at $0.023 per GB-month reduces long-term storage costs significantly. You can save even more with Glacier at $0.004 per GB.

Step 3: model network usage

Network costs depend on your architecture. If your application backend is hosted on AWS in the same region as ClickHouse®, data transfer between them is either free (same availability zone) or $0.01 per GB (different AZs). For 50 concurrent users running 100 queries per day with 1 MB average results, that's 150 GB monthly transferred from ClickHouse® to your application servers - costing either $0 (same AZ) or $1.50/month (different AZ).

The more significant cost is data egress from your application to end users. If your application sends the raw query results to clients, that 150 GB at AWS egress rates of $0.09 per GB costs $13.50/month. However, most applications aggregate or transform data before sending to clients, significantly reducing actual internet egress. A dashboard that renders charts from query results might only transfer 10-20 MB to the browser, reducing egress costs to pennies.

Cross-region deployments multiply these costs. Transferring data between AWS regions costs $0.02 per GB, and multi-region architectures require careful planning to avoid unexpected network charges.

Step 4: include redundancy multipliers

The base costs above assume a single production cluster. You will probably also want development and staging environments, which typically run smaller instance types but still add 20-30% to your compute costs.

Cross-region replication for disaster recovery essentially doubles your infrastructure costs. Many teams start without replication and add it later as their business grows and uptime requirements become more stringent.

For this example workload with a basic three-node cluster, you're looking at roughly $870-890 per month for compute, storage, and network on AWS. Adding development and staging environments increases this to approximately $1,100-1,200 per month. Cross-region replication for disaster recovery would roughly double these costs to $2,200-2,400 per month. These estimates don't include monitoring infrastructure, backup storage costs, support contracts, or engineering time.

Self-hosted ClickHouse® cost comparison by scale

This table shows monthly AWS infrastructure costs across different deployment scales, from minimal single-node setups to enterprise-grade production clusters with full redundancy.

Notes on scaling:

• Minimal: Development/testing only, generally not suitable for production
• Small: Entry-level production, handles modest analytics workloads (the example we calculated)
• Medium: Scales vertically for better per-query performance
• Large: Scales horizontally for higher concurrency and data volume
• XLarge: Enterprise deployments with multi-terabyte datasets and thousands of concurrent users

The "with Dev/Staging" totals include additional smaller environments for development and testing, typically adding 15-20% to base infrastructure costs. These environments usually run on smaller instance types with minimal data volumes.

Cross-region disaster recovery essentially doubles all infrastructure costs by replicating your entire production setup to a second AWS region.

These costs assume on-demand pricing. You can reduce compute costs by 40-75% through reserved instances, spot instances, or committed use discounts, but this, of course, requires capacity planning.

Hidden engineering and on-call overhead

Direct infrastructure costs are half the story. The engineering time to set up and maintain ClickHouse® represents a significant hidden expense that many teams underestimate when evaluating self-hosting.

Initial cluster setup typically takes 2-4 weeks for an experienced engineer. You'll configure replication, set up ZooKeeper or ClickHouse® Keeper for coordination, tune kernel parameters, establish backup procedures, and build monitoring dashboards. This work happens before you even start loading production data or building your application's query layer.

Expect ongoing maintenance to consume roughly 4-8 hours per week once your cluster is stable. During scaling events or performance issues, this time commitment can spike dramatically.

• Performance tuning: Analyzing query patterns and adjusting table schemas, indexes, and materialized views
• Capacity planning: Forecasting storage growth and determining when to add nodes or upgrade instance types
• Incident response: Debugging outages, failed queries, or replication lag outside business hours
• Security updates: Testing and applying patches, managing access controls, and rotating credentials

If you value engineering time at, say, $100-150 per hour, the operational overhead can adds $1,600-4,800 per month to your total cost of ownership. This assumes one engineer dedicating 10-20% of their time to ClickHouse® operations.

Comparing self-hosted costs to ClickHouse® Cloud pricing

ClickHouse® Cloud pricing separates compute and storage, charging $0.22-0.39 per compute unit-hour and $25.30 per TB-month for storage. A compute unit roughly equals one vCPU-hour, so the pricing model scales with your actual usage rather than provisioned capacity.

For the example workload above (3 nodes × 16 vCPUs = 48 vCPUs), you'd pay approximately $0.26 × 48 × 24 × 30 = $8,985 per month for compute if running continuously. Storage for 300 GB costs about $7.60 per month.

However, ClickHouse® Cloud includes features that reduce total cost of ownership. Automatic scaling means you only pay for compute when queries are running, not for idle capacity. The service handles all maintenance, backups, and monitoring without requiring dedicated engineering time. When you factor in the $1,600-4,800 monthly value of engineering time, the gap narrows considerably.

Base workload break-even

For small workloads processing less than 10 GB daily with low query concurrency, self-hosting typically costs less. You can run a single node on a modest instance type for $200-400 per month, while managed services have minimum monthly costs around $50-100 for their smallest tiers.

High concurrency break-even

Query concurrency affects the economics differently for self-hosted versus managed deployments. With self-hosting, you provision enough capacity to handle peak load, which means you're paying for idle resources during quiet periods.

If your application has 50 concurrent users during business hours but drops to near zero overnight, you're wasting roughly 60% of your self-hosted compute capacity. Managed services with auto-scaling or auto-idling make you pay only for the 8-10 hours of high activity, potentially reducing costs by half compared to always-on infrastructure.

Cold storage heavy break-even

Workloads that retain large amounts of historical data favor different cost structures. Self-hosting with object storage tiering can be very cost-effective since you pay only $0.023 per GB-month for cold data on S3.

For data warehouses with 100 TB of historical data queried infrequently, self-hosted storage costs around $2,300 per month on S3. Managed services might charge $2,530-3,000 per month for the same storage.

When managed ClickHouse® is cheaper than DIY

Managed services often cost less than self-hosting when you account for total cost of ownership, not just infrastructure expenses. Several scenarios clearly favor managed deployments over running your own clusters.

Small to medium workloads benefit most from managed services. If you're processing less than 1 TB of data with moderate query load, the engineering time to set up and maintain self-hosted infrastructure costs more than paying for a managed service. A single engineer spending even 5 hours per month on ClickHouse® operations at $150 per hour ($750 per month) can exceed the price difference between hosting options.

Unpredictable traffic patterns make managed services more economical. Applications with spiky query loads waste money on over-provisioned self-hosted infrastructure that sits idle most of the time. Managed services with auto-scaling charge only for actual usage, which can reduce costs by 40-60% compared to always-on clusters sized for peak load.

Limited DevOps resources change the equation significantly. If your team lacks ClickHouse® expertise, the learning curve and ongoing operational burden can consume far more engineering time than anticipated.

Why a managed service often costs less and ships faster

Tinybird provides managed ClickHouse® infrastructure that removes setup complexity and operational overhead. The platform handles cluster provisioning, scaling, backups, monitoring, and many database operations, letting developers focus on building their application logic rather than managing database infrastructure.

Beyond infrastructure management, Tinybird adds developer-focused features that reduce time to production. You can define data pipelines as code using SQL, test them locally, and deploy to production with CI/CD workflows. The platform generates REST API endpoints from your queries automatically, removing the need to build and maintain an API layer between ClickHouse® and your application.

The time savings compound quickly. Instead of spending 2-4 weeks setting up ClickHouse® and building ingestion pipelines, you can have a working system in hours or days.

Sign up for a free Tinybird plan and start querying data in minutes without managing any infrastructure.

FAQs about self-hosting ClickHouse® costs

How does open source ClickHouse® licensing affect total cost?

ClickHouse® is completely free to use under the Apache 2.0 license, meaning there are no licensing fees regardless of data volume or query load. The only costs come from the infrastructure you run it on and the engineering time to manage it.

Can I reduce egress fees with a BYOC deployment?

Bring-your-own-cloud deployments can lower network costs by keeping data within your existing cloud provider's network. Some managed ClickHouse® services like Altinity.Cloud run in your AWS or GCP account, which means data transfer between ClickHouse® and your application servers uses internal networking.

What happens to ClickHouse® costs if I enable object storage caching?

Object storage caching reduces query latency for cold data by keeping frequently accessed files on faster local SSDs. This improves performance but increases storage costs since you're paying for both the cheap object storage and the more expensive SSD cache. The cache size depends on your query patterns, but expect to allocate 10-20% of your cold data volume to the cache layer for meaningful performance gains.

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