CrateDB vs. ClickHouse: Choosing the Right Database When You've Outgrown Postgres

You've outgrown Postgres. Now what?

The signs are familiar. Analytical queries that used to return in seconds now take minutes. Your ingestion pipeline is falling behind as data volume grows. Your team is adding indexes that help one query and break another. Someone suggested partitioning. Someone else suggested a read replica. Neither solved the underlying problem.

Postgres is a superb database — but it was designed for transactional workloads, not large-scale analytics on fast-moving data. When your analytical queries start competing with your application workload, it's time to move to something purpose-built.

Two databases come up most often in this evaluation: ClickHouse and CrateDB. Both are significant upgrades from Postgres for analytics. Both handle large data volumes with fast query performance. But they make different architectural choices that make one or the other a better fit depending on what you're building.

This page is a practical guide to help you choose; honestly, based on your specific workload.

What they have in common

Before the differences, it's worth noting what both databases share:

• Distributed architecture designed for analytical workloads at scale
• SQL query interface (with varying degrees of standard SQL compliance)
• Horizontal scalability; add nodes to grow capacity
• Open-source cores with managed cloud options
• Strong performance on aggregation-heavy analytical queries
• Active communities and production deployments at significant scale

If your primary requirement is "faster analytics than Postgres," either will solve that problem. The decision comes down to what else your workload demands.

The core architectural difference

ClickHouse is a pure columnar OLAP engine. Its architecture is optimized for one thing: executing analytical queries on large volumes of structured data as fast as physically possible. It achieves this through aggressive columnar compression, vectorized query execution, and a highly tunable storage engine (MergeTree). That focus on raw analytical throughput is its greatest strength — and also its constraint.

CrateDB is a distributed multi-model database built on a shared-nothing architecture. It handles structured analytics, JSON documents, time-series data, full-text search, vector embeddings, and geospatial data within a single engine — all queryable through standard SQL. It trades some of ClickHouse's raw analytical throughput for the flexibility to handle multiple data models without requiring separate systems.

Side-by-side comparison

Choose ClickHouse if your workload looks like this

ClickHouse is the stronger choice when:

• Your data is structured and schema-stable. If your events arrive in a consistent, flat format with defined columns and you rarely need to add new fields dynamically, ClickHouse's rigid schema is not a liability — it's what enables its performance.
• Raw query speed on large batch datasets is your top priority. ClickHouse consistently tops analytical benchmarks. If you're running complex aggregations over billions of rows on historical data and sub-second response time is the primary goal, ClickHouse is hard to beat.
• You're building a classic data warehouse or BI layer. ClickHouse integrates well with dbt, Airbyte, and the modern ELT data stack. If your pipeline is extract → load → transform → query, it fits naturally.
• You have engineering capacity to tune and operate it. ClickHouse rewards teams that understand its internals. Choosing the right table engine, designing your ORDER BY and partition key correctly, and managing merge behavior are real operational tasks. Teams with that expertise get exceptional results.
• Your analytics are primarily historical, not real-time. If your dashboards and queries run over data that is hours or days old rather than seconds old, ClickHouse's asynchronous merge behavior is not a problem.

Choose CrateDB if your workload looks like this

CrateDB is the stronger choice when:

• Your data is not purely structured. If your events include nested JSON payloads, variable fields, or metadata that evolves over time — device configurations, customer attributes, enrichment fields — CrateDB handles this natively without a separate document store or a flattening ETL step.
• You need data queryable within milliseconds of ingest. CrateDB auto-indexes every field on arrival. For real-time dashboards, operational alerting, or applications where the last few seconds of data matter, this is a meaningful architectural advantage.
• You're managing high-cardinality, multi-dimensional data. IoT deployments, multi-tenant SaaS analytics, and any workload where you're slicing data across many simultaneous dimensions — device, customer, region, firmware version — benefit from CrateDB's architecture, which has no cardinality penalties.
• You want one system instead of many. Teams that would otherwise run ClickHouse for analytics + MongoDB for JSON documents + Pinecone for vectors + Elasticsearch for full-text search can consolidate all four into CrateDB. One ingestion pipeline. One SQL interface. One system to operate.
• Your analysts and BI tools should just work. CrateDB speaks the PostgreSQL wire protocol. Grafana, Metabase, Tableau, Superset, DBeaver, and psql connect without custom drivers or adapters. If simplifying your tooling layer matters, this is a real advantage.
• You want a database that operates itself. CrateDB handles sharding, rebalancing, and replication automatically. For data engineering teams that want to spend time building pipelines rather than tuning database internals, this reduces ongoing operational load significantly.

The most common scenarios and which fits better

IoT or industrial sensor data with diverse metadata → CrateDB. High-cardinality device dimensions, evolving JSON payloads, and the need for real-time queryability all favor CrateDB's architecture.

SaaS product analytics — user events, funnels, retention → Depends. If your events are structured and stable, ClickHouse's raw speed is attractive. If your event schema evolves frequently or you need to join event data with JSON-structured user profiles, CrateDB's flexibility pays off.

Internal BI and reporting on historical data → ClickHouse. If your analysts are querying data that's hours or days old with stable schemas and the goal is fast dashboard queries, ClickHouse is a strong fit.

AI-powered applications — RAG pipelines, semantic search, anomaly detection → CrateDB. Native vector search means you don't need a separate vector database alongside your analytics layer.

Log analytics and observability → Both are viable. ClickHouse has a strong track record here. CrateDB's full-text search and dynamic schema give it an edge if your log structure varies significantly across services.

Multi-tenant analytics platform serving customer-facing dashboards → CrateDB. The combination of high-cardinality handling, real-time queryability, and multi-model support fits the demands of customer-facing analytics products where data arrives fast and dimensions multiply with customer count.

What customers who chose CrateDB over alternatives say

"CrateDB allows us to do real-time dashboards on very big streaming and historic datasets in a simple way. We can scale the system easily as we grow the load and customers and have it all done with SQL." — Bitmovin (2 billion new events per day)

"Having a standardized SQL language is a big advantage with CrateDB. That makes it very easy for people to access this data and work with it in different tools like Grafana or Tableau." — TGW Logistics (900,000 sensors, 30,000 messages per second)

Coming from InfluxDB? If your primary pain is cardinality limits on IoT or sensor data rather than outgrowing Postgres, our CrateDB vs. InfluxDB comparison is a more direct fit for your situation.

Try CrateDB on your workload

The fastest way to evaluate either database is to run it against your actual data and queries — not benchmarks, not demos. Start with a free CrateDB Cloud instance and bring your existing Postgres schema and queries. Most teams have CrateDB running and returning results within an hour.

• Start free - no credit card required →
• Book a technical demo →
• See how CrateDB handles real-time analytics →