Comparison
Hydrolix and ClickHouse
Hydrolix and ClickHouse both run fast analytics on large volumes of log data, but they are built differently. This page compares Hydrolix with self-managed ClickHouse, not ClickHouse Cloud, on architecture, pricing, and scalability.
Hydrolix writes data straight to object storage and scales ingest, storage, and query independently, with 20–50x compression.
Key areas of comparison
Architectural Models
Hydrolix separates ingest, storage, and query into independently scaled services, with data written directly to object storage rather than local disk. For query execution, Hydrolix maintains its own fork of the ClickHouse SQL engine, alongside support for other interfaces like Spark. Scaling storage and compute independently on self-managed ClickHouse typically means configuring MergeTree tables on an S3 storage policy, a setup ClickHouse’s own docs describe as more complex to manage than a standard deployment. This setup also comes with cache-miss latency trade-offs.
Predictable pricing
Self-managed ClickHouse costs scale with the compute and storage you provision, so unpredictable ingest spikes or long retention windows require over-provisioning to stay safe. Hydrolix pricing is usage-based, per GB ingested, and its compression and object storage-native design keep retention cost close to the object-storage cost of your compressed data, which makes long-term log retention easier to budget for in advance.
Scalability
ClickHouse scales well within a cluster you manage, but growing from terabytes to petabytes on self-managed ClickHouse usually requires resharding, rebalancing, and tuning merge behavior. Hydrolix uses decoupled architecture that adds ingest and query capacity independently, without that re-architecture step.
Benefit comparison: Hydrolix vs. ClickHouse
| Category | Hydrolix | ClickHouse |
|---|---|---|
| Log data at scale | Yes: Near-real-time queries on object storage (sub-second query response, with newly ingested data queryable in seconds), built for petabyte-scale retention at low cost | Yes: Millisecond near real-time queries on local/attached storage, at the cost of provisioning fast storage on every node |
| Storage/compute separation | Yes: Native. Data lands in object storage by default | No: Not out of the box. Requires manual setup and has cache-miss latency trade-offs |
| High-cardinality query performance | Yes: Every column indexed automatically at ingest; no index design required | Yes: Achievable through sort-key design plus skip indexes, text indexes, or projections, which require schema design work |
| Ingest tuning | Yes: High-volume streaming writes with no client or server-side tuning required | Yes: Streaming ingest supported (Kafka engine, async inserts, HTTP/TCP), but stable high-rate throughput depends on choosing and tuning the right mechanism |
| Scaling ingest and query capacity | Yes: Scaled independently without resharding | Yes: Requires a sharding strategy and rebalancing as clusters grow |
| Retention cost at scale | Yes: Compression and object storage keep cost close to object-storage cost for the compressed data | Yes: Cost scales with provisioned cluster size; long retention means planning capacity ahead |
| Deployment model | Yes: Self-managed in your own cloud account (BYOC), or fully managed Hydrolix Cloud | Yes: Self-managed on your infrastructure; ClickHouse Cloud and ClickHouse BYOC are separate managed offerings with their own pricing |
Note: Comparison is with ClickHouse open source, self-managed.
How do Hydrolix and Clickhouse Compare?
Data ingest
ClickHouse ingests data at high throughput, but it performs best with large batches and requires tuning for streaming ingest. Hydrolix ingests at high volume without client-side batching or sampling, and newly written data is typically queryable within 5–10 seconds, even at massive scale. FOX achieved a sustained peak of 17.4 GB per second across four CDN providers during the 2025 Super Bowl with Hydrolix.
High cardinality
High-cardinality fields (user IDs, IPs, request paths) can force ClickHouse into slower scans when queries don’t filter on the table’s sort key, or require skip indexes, text indexes, or projections to keep queries fast. Hydrolix indexes every column automatically at ingest, so ad-hoc queries on high-cardinality fields stay fast without index design up front. Indexes are sparse and built per partition rather than per table, on partitions capped at one million rows at ingest. Hydrolix is especially well-suited for finding high-cardinality data points in narrow time-range queries and needle-in-a-haystack queries.
Data availability
ClickHouse can tier older data to cheaper storage such as S3, but queries against that cold tier are typically slower, especially on cache misses. Hydrolix storage is object storage-native from ingest onward, so historical data stays queryable at a consistent performance profile whether it landed an hour ago or a year ago.
Query performance
ClickHouse is built for millisecond performance: with data on local NVMe or attached block storage, a well-tuned cluster returns queries in milliseconds. That speed comes at a cost. Sustaining it at scale means provisioning and paying for fast local storage attached to every node, regardless of whether or not it’s being queried. Hydrolix is designed for near-real-time performance on object storage instead: queries return in sub-second time rather than milliseconds, but the same design that keeps queries fast also keeps storage cheap at any retention window, without provisioning hardware ahead of your data volume.
Performance at scale
ClickHouse performance at scale is well-proven in production. Companies run petabyte-scale ClickHouse deployments today. What differs is the effort behind sustaining that performance on self-managed clusters: it typically requires a team with deep expertise in sharding strategy, merge behavior, and query planning, on top of the hardware investment described above. Hydrolix keeps queries in the sub-second range at that same scale without shard management, merge tuning, or provisioning fast local storage ahead of time. That matters most for teams that don’t want to dedicate additional time and resources to database tuning.
Deployment model
Both Hydrolix and ClickHouse offer self-managed (BYOC) and cloud options.
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Frequently asked questions
In what scenarios is ClickHouse the appropriate tool for processing my data?
ClickHouse is a strong fit if you already have the engineering capacity to manage your cluster and data pipeline, or you want an open-source foundation with no vendor dependency. It also makes sense if your workloads are steady-state rather than bursty, since you’re provisioning and tuning capacity yourself.
When is Hydrolix the best tool for processing my data?
Hydrolix fits best when your log volume is growing faster than your team’s ability to manage infrastructure. That usually means high-cardinality, high-volume, time-series data where you need fast queries and long retention without a dedicated database-ops team tuning the cluster. You can also scale it up for major events and bursty traffic.
Is getting started with Hydrolix as easy as ClickHouse?
With Hydrolix Cloud, yes: setup is automated, and you can start analyzing data in minutes on a free trial. The bigger difference shows up later. Hydrolix maintains its performance from terabytes to petabytes without resharding, re-architecture, or constant tuning.
Isn’t ClickHouse faster for analytics queries?
The answer depends on the workload. For high-volume log investigations across months of retained data, Hydrolix is designed to combine fast queries with affordable retention and less tuning. For a customer-facing dashboard running repeated queries against pre-aggregated data, a well-tuned ClickHouse deployment may deliver lower query latency. A proof-of-concept with your actual data is the best way to compare performance and cost.
How does this comparison apply if we’re using ClickHouse Cloud instead of self-managed ClickHouse?
Hydrolix pricing is usage-based, per GB ingested. See our pricing page for details.
What if I have already invested in ClickHouse?
Hydrolix uses ClickHouse SQL and works with ClickHouse clients and drivers, including the native protocol, JDBC, and Python libraries, so your queries and tooling largely carry over. Table and ingest definitions are rebuilt as Hydrolix transforms.
Talk to an expert about your use case
Weighing ClickHouse against Hydrolix? We’ll help you map your ingest volumes, retention window, and query performance to the right architecture before you commit.