Druid is a columnar OLAP database optimized for real-time analytics on time-series data with sub-second latency, while Elasticsearch is a distributed search and analytics engine built for full-text search and log analysis. Druid excels at numerical aggregations across massive datasets, whereas Elasticsearch prioritizes flexible querying and text search capabilities.
Druid vs Elasticsearch — Side-by-side comparison on A Versus B
Choose Druid if you need sub-second analytics queries on time-series data at massive scale with pre-aggregated rollups and extreme storage efficiency. Choose Elasticsearch if you prioritize full-text search capabilities, flexible JSON document querying, or need a more accessible solution for log analytics with a larger ecosystem and community support.
Real-time OLAP database for streaming analytics and time-series data
Proprietary distributed search and analytics engine by Elastic with cloud-native architecture.
Typical Storage Efficiency (compression ratio)
Community Size (GitHub stars as of 2026)
Druid vs Elasticsearch — Comparison Data
Structured comparison data for Druid vs Elasticsearch with 171 attributes.
Query Latency (100M rows, simple aggregation)
Average Query Latency (Standard Aggregation)
Maximum Concurrent Queries (Standard Node)
Monthly Cost (100GB index, 1M queries/month)
Mean Time to Resolution (MTTR) Improvement
Total Cost of Ownership (3-year, 500GB/month ingestion)
Required Infrastructure Team Size (100 users)
Typical Query Latency (1B rows, GROUP BY)
Annual Licensing Cost (Small Deployment)
Druid vs Elasticsearch — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Druid vs Elasticsearch.
When should I use Druid over Elasticsearch?
Use Druid when you need sub-second analytics on massive time-series datasets (100B+ rows), require extreme storage compression, perform frequent numerical aggregations, or need real-time metric pre-computation via rollups. Druid's columnar architecture excels at OLAP workloads with predictable query patterns.
When should I use Elasticsearch over Druid?
Use Elasticsearch for full-text search, log analysis, flexible ad-hoc querying, security analytics, or when you need a lower barrier to entry. Elasticsearch is better for text-heavy workloads, evolving schemas, and applications where search relevance scoring matters more than raw aggregation speed.
Can Druid and Elasticsearch be used together?
Yes. Many organizations use Elasticsearch for log indexing and search, then export/replicate aggregated metrics to Druid for long-term time-series analytics. This leverages Elasticsearch's search strength for recent logs and Druid's efficiency for historical metric analysis.
What's the cost difference between Druid and Elasticsearch?
Both are open-source and free to self-host. Druid requires more operational expertise (higher DevOps costs), while Elasticsearch has lower setup costs but higher storage/compute at scale. Cloud-hosted versions (Elastic Cloud vs. Imply) typically cost $500-5000+/month depending on data volume.
How do query patterns differ between Druid and Elasticsearch?
Druid optimizes for known, repeated aggregations (COUNT, SUM, GROUP BY) via pre-computation, making these extremely fast. Elasticsearch excels at varied, ad-hoc queries with complex boolean logic and text matching. Druid penalizes unpredictable unique-value queries, while Elasticsearch handles them gracefully at the cost of latency.