CockroachDB vs DuckDB
- ✦ Distributed SQL
- ✦ PostgreSQL Wire Protocol
- ✦ Multi-Region Replication
- ✦ In-process execution
- ✦ Parquet/CSV/JSON direct query
- ✦ S3 and GCS file access
Real-World Scenarios: When to Choose Which
The question that matters: “In what situation will I regret choosing A over B after 3 months?”
CockroachDB's geo-partitioning pins rows to specific cloud regions by key prefix, ensuring GDPR-regulated customer data never leaves EU nodes without sacrificing SQL query capability.
Replace pandas aggregations with SQL-based DuckDB queries for 10-50x faster group-by operations
CockroachDB's multi-zone deployment survives the loss of an entire availability zone automatically, with zero data loss and sub-30-second recovery versus minutes of manual failover for traditional primary/replica setups.
CockroachDB's online schema changes apply column additions and index builds while the database serves traffic, avoiding the maintenance windows that single-node SQL databases require for schema changes.
Query 50GB Parquet files on S3 directly from Python without ETL, returning results in seconds
Run dbt models locally against DuckDB instead of cloud warehouses to cut development cycle time
Use DuckDB as a compute engine over Delta Lake or Iceberg tables without a dedicated cluster
Pricing Intelligence
CockroachDB Plans
Free tier available
- • 10 GiB storage
- • Shared cluster
- • PostgreSQL compatible
- • $0.20/hr
- • Dedicated cluster
- • Multi-region support
- • Custom deployment
- • SLA
- • HIPAA/FedRAMP
DuckDB Plans
Free tier available
- • MIT license
- • Embedded in-process
- • No server needed
Feature Matrix
9 differences found across 18 standardized features
- •Distributed SQL
- •PostgreSQL Wire Protocol
- •Multi-Region Replication
- •Serializable Isolation
- •Automatic Sharding
- •Geo-Partitioning
- •Follower Reads
- •Online Schema Changes
- •ACID Transactions
- •Backup to Cloud Storage
- •Change Data Capture
- •LDAP/SAML Auth
- •Row-Level TTL
- •Import/Export
- •Prometheus Monitoring
- •Kubernetes Operator
- •In-process execution
- •Parquet/CSV/JSON direct query
- •S3 and GCS file access
- •SQL support
- •Python/R/Node.js integration
- •Vectorized execution
- •Parallel query processing
- •Apache Arrow integration
- •Zero-copy pandas exchange
- •Window functions
- •ACID transactions
- •Column-oriented storage
- •Schema inference
- •HTTPFS extension
Pros & Cons Face-Off
Evaluative strengths and weaknesses: not feature lists
- +PostgreSQL compatibility minimizes migration effort
- +Survives node and zone failures automatically
- +Global multi-region with low-latency reads via follower reads
- +Free tier for development and small workloads
- −More expensive than single-node PostgreSQL for simple use cases
- −Distributed transaction overhead vs single-node performance
- +Runs in-process with zero infrastructure setup
- +Directly queries Parquet and CSV on S3 without ETL
- +Outperforms many server-based DBs on single-machine workloads
- −Single-node only - no horizontal scaling or clustering
- −Not suitable for multi-user concurrent write workloads
At a Glance
Frequently Asked Questions
Related Comparisons
Sources
- 1.CockroachDB Official Pricing–Vendor pricing page
- 2.DuckDB Official Pricing–Vendor pricing page
- 3.CockroachDB Official Website–Official product website
- 4.DuckDB Official Website–Official product website