DuckDB vs OpenSearch
- ✦ In-process execution
- ✦ Parquet/CSV/JSON direct query
- ✦ S3 and GCS file access
- ✦ Full-text search
- ✦ k-NN vector search
- ✦ SQL query support
Real-World Scenarios: When to Choose Which
The question that matters: “In what situation will I regret choosing A over B after 3 months?”
Query 50GB Parquet files on S3 directly from Python without ETL, returning results in seconds
Replace pandas aggregations with SQL-based DuckDB queries for 10-50x faster group-by operations
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
Migrate from Elasticsearch to OpenSearch with API-compatible clients and keep the same application code
Route CloudWatch logs to OpenSearch Service via Kinesis for centralized log search without leaving AWS
Store document embeddings and run hybrid text+vector search with the k-NN plugin to improve retrieval accuracy
Correlate AWS CloudTrail and VPC flow logs in OpenSearch to detect anomalous access patterns in near-real-time
Pricing Intelligence
DuckDB Plans
Free tier available
- • MIT license
- • Embedded in-process
- • No server needed
OpenSearch Plans
Free tier available
- • Apache 2.0 license
- • Self-hosted
- • Full features
- • From $0.096/hr per instance
- • Managed by AWS
- • Pay-as-you-go
Feature Matrix
8 differences found across 14 standardized features
- •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
- •Full-text search
- •k-NN vector search
- •SQL query support
- •Distributed sharding
- •ML inference nodes
- •Security plugin (built-in)
- •Observability dashboards
- •Anomaly detection
- •Index rollups
- •REST API
- •Cross-cluster replication
- •Snapshot to S3
- •ISM (Index State Management)
- •Alerting plugin
Pros & Cons Face-Off
Evaluative strengths and weaknesses: not feature lists
- +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
- +Apache 2.0 license - no SSPL restrictions
- +API-compatible with Elasticsearch for easy migration
- +Built-in security plugin at no extra cost vs Elastic
- −Feature development lags Elasticsearch in some areas
- −AWS-managed version has less flexibility than self-managed Elastic
At a Glance
Frequently Asked Questions
Related Comparisons
Sources
- 1.DuckDB Official Pricing–Vendor pricing page
- 2.OpenSearch Official Pricing–Vendor pricing page
- 3.DuckDB Official Website–Official product website
- 4.OpenSearch Official Website–Official product website