Druid vs Pinot 2026: Real-Time OLAP Comparison

A Versus B

software

Updated July 9, 2026

Druid is a real-time OLAP database optimized for time-series analytics with sub-second query latency, while Pinot is a columnar OLAP datastore designed for real-time analytics at scale with higher throughput on aggregation queries. Druid excels in streaming ingestion and time-series use cases, whereas Pinot performs better for high-cardinality dimension queries and massive fan-out aggregations.

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Apache Druid

Real-time OLAP database optimized for time-series analytics and streaming ingestion.

Real-time monitoring, time-series analytics, metrics dashboards, streaming data ingestion, DevOps and infrastructure monitoring platforms

Score63%

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VS

Apache Pinot

Real-time OLAP engine optimized for low-latency dimensional analytics on streaming data.

User analytics, ad-hoc OLAP queries, high-cardinality dimension analysis, large-scale data warehousing, businesses with 100M+ daily events and complex aggregation patterns

Score63%

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Quick Answer

AI Summary

Druid is a real-time OLAP database optimized for time-series analytics with sub-second query latency, while Pinot is a columnar OLAP datastore designed for real-time analytics at scale with higher throughput on aggregation queries. Druid excels in streaming ingestion and time-series use cases, whereas Pinot performs better for high-cardinality dimension queries and massive fan-out aggregations.

Our Verdict

AI-assisted

Choose Druid if you need real-time streaming analytics with sub-second latency on time-series data, monitoring dashboards, and can tolerate moderate operational overhead. Choose Pinot if you prioritize high-cardinality dimension analysis, massive query throughput at scale, and need better storage efficiency with simpler operational management.

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A

Apache Druid

7.1/10

Apache Pinot

7.9/10

A

Choose Apache Druid if

Real-time monitoring, time-series analytics, metrics dashboards, streaming data ingestion, DevOps and infrastructure monitoring platforms

Choose Apache Pinot if

Best pick

User analytics, ad-hoc OLAP queries, high-cardinality dimension analysis, large-scale data warehousing, businesses with 100M+ daily events and complex aggregation patterns

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Key Differences at a Glance

Query Latency (p99):✓ Apache Druid wins(100-500ms vs 500-2000ms)
Ingestion Rate:✓ Apache Druid wins(1M+ events/second vs 500K events/second)
High-Cardinality Query Performance:✓ Apache Pinot wins(Excellent vs Moderate)

See all 7 differences

Key Facts & Figures

92 numeric metrics compared

Metric	Apache Druid	Apache Pinot	Ratio
Query Latency (1B rows, 100 dimensions)(milliseconds)	50-150ms	50-100ms
Memory Footprint per 1GB Data(MB)	600-900MB	150-300MB
Maximum Events/Sec per Node(events/sec)	100K-500K	10K-50K
Typical Cluster Setup Cost(USD/month (3-node))	$2500-5000	$1500-3000
Enterprise Deployments(thousands)	500+ (Airbnb, Netflix, etc)	1000+ (LinkedIn, Uber, etc)
Query Latency (p95 on Real-Time Data)(milliseconds)	100-500ms	500-2000ms
Minimum Cluster Size for 1TB Dataset(nodes)	3-5 nodes	5-8 nodes
GitHub Stars (Community Activity)(count)	15,800	5,300
Max Ingestion Throughput(events/second)	500,000	1,000,000-2,000,000 events/sec
Query Latency (50th percentile)(milliseconds)	150	—	—
Data Compression Ratio (metrics)(ratio)	10:1	—	—
GitHub Stars(stars)	15,200	—	—
Minimum Cluster Node Count(nodes)	3	—	—
Third-Party Integrations(native integrations)	300+	—	—
Memory Overhead (1M events)(MB per node)	120	—	—
Query Latency (p99)(milliseconds)	500ms	50-200ms
Data Ingestion Rate(GB/sec)	1,000,000	500,000-1,000,000
Maximum Cluster Size(nodes)	Unlimited (distributed)	1000+	—
Typical Query Cost (per TB scanned)(USD)	$0.10-$0.50	—	—
Setup Time (to production)(days)	14-30	—	—
SQL Standard Compliance(% compatibility)	~60% ANSI SQL	85% SQL coverage
Typical Memory Per Node(GB)	16-64	16-32GB for analytics workload
P99 Query Latency(milliseconds)	100-500ms	500-2000ms
Median Query Latency(milliseconds)	10-100ms	—	—
Data Ingestion Latency(seconds)	1-5 seconds (streaming)	0.1 (streaming)
Maximum Dataset Size Supported(GB)	Petabyte+ (with cluster scaling)	—	—
Query Latency (P99 percentile)(milliseconds)	250ms	—	—
Maximum Ingestion Rate(events/second)	1,000,000+	—	—
Storage Cost(USD per TB per month)	$5 (self-hosted avg)	—	—
Concurrent Query Capacity(concurrent users)	300	—	—
Time to First Query(minutes)	45 (self-hosted setup)	45-60 minutes
Minimum Cluster Size(nodes)	3 (recommended)	3-5
Streaming Ingestion Rate(events/second)	1M+	500K
Aggregation Query Throughput(queries/second)	50K-100K	100K-500K
Storage Compression Ratio(ratio)	10:1 to 20:1	15:1 to 25:1
Operational Components(count)	12+ (Coordinator, Broker, Historical, MiddleManager, Overlord, Router)	8+ (Broker, Server, Controller, Minion)
High-Cardinality Dimension Support(unique values)	1M-10M practical limit	100M+ with dictionary encoding
Segment Size (typical)(GB)	100-500MB segments	1-5GB segments
Learning Curve (Complexity)(months to production)	2-3 months	1-2 months
P99 Query Latency (1B rows, aggregation)(milliseconds)	100-500ms	100-500ms
Ingestion Latency (Kafka to query-ready)(seconds)	0.5-2 seconds	0.5-2 seconds
Maximum Recommended Node Storage(TB)	0.5-2TB per node	0.5-2TB per node
GitHub Stars (2026)(stars)	15,000+	15,000+
Data Compression Ratio(x)	3:1 to 10:1 (avg 6:1)	3:1 to 10:1 (avg 6:1)
Ingestion Latency (end-to-end)(milliseconds)	100-500ms	100-500ms
Memory Usage per Query(MB)	100-400MB	100-400MB
Typical Cost per TB/year(USD)	$2000-3500	$2000-3500
Storage Cost per TB/month(USD)	$120-180	$120-180
Typical Node Memory(GB)	16-64GB	16-64GB
Minimum Recommended Cluster Size(nodes)	5-7 nodes (3 controllers + 2-4 brokers)	5-7 nodes (3 controllers + 2-4 brokers)
Max Dataset Size (Practical)(TB)	100-500TB (hot data)	100-500TB (hot data)
Query Latency (P95)(milliseconds)	100-500ms	100-500ms
Per-Query Cost (1TB scan)(USD)	$0 (infrastructure only)	$0 (infrastructure only)
Events/Second Ingestion(events/sec)	500,000/sec (streaming)	500,000/sec (streaming)
Annual TCO (100TB dataset)(USD)	$150,000	$150,000
Maximum Recommended Cluster Size(nodes)	500+ (LinkedIn runs 1,000+)	500+ (LinkedIn runs 1,000+)
Time to First Production Query(days)	14-30 days (requires schema design and tuning)	14-30 days (requires schema design and tuning)
GitHub Stars (as of 2026)(stars)	8,200+	8,200+
Setup Time (minutes)(minutes)	240-480	240-480
Query Latency on 1GB Dataset(milliseconds)	100-500	100-500
Maximum Scalable Dataset Size(GB)	100,000+	100,000+
Minimum Cluster Nodes Required(nodes)	5-7	5-7
Concurrent Queries Supported(queries)	1000+	1000+
Supported Programming Languages(languages)	Java, Python (limited)	Java, Python (limited)
Annual Infrastructure Cost (1TB dataset)(USD)	50,000-150,000	50,000-150,000
Concurrent User Support(users)	100-500 typical	100-500 typical
Query Latency (Average)(milliseconds)	<100ms	<100ms
Data Freshness(seconds)	Sub-second to 1 minute	Sub-second to 1 minute
Ingestion Streaming Support(events per second)	1M+ eps native	1M+ eps native
Query Latency (Median)(milliseconds)	<100 ms	<100 ms
Streaming Ingestion Latency(seconds)	<1 second	<1 second
On-Demand Query Pricing(USD per TB scanned)	Free (self-hosted) / $0.10-0.50 (managed)	Free (self-hosted) / $0.10-0.50 (managed)
Maximum Daily Event Throughput(billion events/day)	10+ billion events/day (proven at LinkedIn, Airbnb)	10+ billion events/day (proven at LinkedIn, Airbnb)
Time to Deploy(seconds (average))	40-80 hours (cluster provisioning, tuning)	40-80 hours (cluster provisioning, tuning)
Concurrent Users Supported(users)	100-500 (depends on cluster config)	100-500 (depends on cluster config)
Ingestion Rate (events/second)(events/sec)	1,000,000+	1,000,000+
Query Latency (1B rows)(seconds)	2-5	2-5
Maximum Recommended Dataset Size(rows)	10,000+	10,000+
Deployment Time(seconds)	6	6
Memory Per Node(GB per 1M events/sec)	50-200	50-200
Typical Query Latency (1B rows, GROUP BY)(milliseconds)	50-500ms	50-500ms
Index Size to Data Ratio(multiplier)	0.1-0.3x	0.1-0.3x
GitHub Stars (Community Size Proxy)(stars)	9,200+	9,200+
Typical Deployment Complexity(relative score)	Medium-High (columnar tuning)	Medium-High (columnar tuning)
Maximum Practical Dataset Size(petabytes)	10+ PB (proven at scale)	10+ PB (proven at scale)
Peak Ingestion Speed(events per second)	1,000,000+	1,000,000+
ANSI SQL Compliance(percentage)	70%	70%
Deployment Components(count)	5-7 components	5-7 components
Query Latency (Typical Analytical Query)(seconds)	<1 second	<1 second
Data Ingestion Throughput(rows/second)	1,000,000+ rows/sec	1,000,000+ rows/sec
Per-Query Cost (1 TB scanned)(USD)	$0 (open-source)	$0 (open-source)
Built-in ML Models(count)	0 (requires external tools)	0 (requires external tools)

Sourced from publicly available data · Jul 2026

Key Differences

7 attributes compared head-to-head

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3Apache Druid

Apache Pinot leads

4Apache Pinot

Apache Druid

Attribute

Apache Pinot

100-500ms(winner)
Query Latency (p99)
500-2000ms
1M+ events/second(winner)
Ingestion Rate
500K events/second
Moderate
High-Cardinality Query Performance
Excellent(winner)
50K-100K QPS
Aggregation Throughput
100K-500K QPS(winner)
Native support with roll-up(winner)
Time-Series Optimization
Good but secondary focus
10:1 to 20:1
Storage Compression Ratio
15:1 to 25:1(winner)
High (12+ components)
Operational Complexity
Medium (8+ components)(winner)

Query Latency (p99)
Apache Druid
100-500ms(winner)
Apache Pinot
500-2000ms
Ingestion Rate
Apache Druid
1M+ events/second(winner)
Apache Pinot
500K events/second
High-Cardinality Query Performance
Apache Druid
Moderate
Apache Pinot
Excellent(winner)
Aggregation Throughput
Apache Druid
50K-100K QPS
Apache Pinot
100K-500K QPS(winner)
Time-Series Optimization
Apache Druid
Native support with roll-up(winner)
Apache Pinot
Good but secondary focus
Storage Compression Ratio
Apache Druid
10:1 to 20:1
Apache Pinot
15:1 to 25:1(winner)
Operational Complexity
Apache Druid
High (12+ components)
Apache Pinot
Medium (8+ components)(winner)

Full Comparison

Apache Druid vs Apache Pinot — attribute comparison table
Attribute	Apache Druid	Apache Pinot

Query Latency (1B rows, 100 dimensions)(milliseconds)	50-150ms	50-100ms(winner)
Query Latency (p95 on Real-Time Data)(milliseconds)	100-500ms(winner)	500-2000ms
Max Ingestion Throughput(events/second)	500,000	1,000,000-2,000,000 events/sec(winner)
Query Latency (50th percentile)(milliseconds)	150	—
Query Latency (p99)(milliseconds)	500ms	50-200ms(winner)
Show 21 more attributes Data Ingestion Rate(GB/sec) 1,000,000 500,000-1,000,000 P99 Query Latency(milliseconds) 100-500ms 500-2000ms Median Query Latency(milliseconds) 10-100ms — Data Ingestion Latency(seconds) 1-5 seconds (streaming) 0.1 (streaming) Maximum Dataset Size Supported(GB) Petabyte+ (with cluster scaling) — Query Latency (P99 percentile)(milliseconds) 250ms — Aggregation Query Throughput(queries/second) 50K-100K 100K-500K P99 Query Latency (1B rows, aggregation)(milliseconds) 100-500ms — Query Latency (P95)(milliseconds) 100-500ms — Query Latency on 1GB Dataset(milliseconds) 100-500 — Concurrent Queries Supported(queries) 1000+ — Query Latency (Average)(milliseconds) <100ms — Query Latency (Median)(milliseconds) <100 ms — Time to Deploy(seconds (average)) 40-80 hours (cluster provisioning, tuning) — Ingestion Rate (events/second)(events/sec) 1,000,000+ — Query Latency (1B rows)(seconds) 2-5 — Maximum Recommended Dataset Size(rows) 10,000+ — Deployment Time(seconds) 6 — Typical Query Latency (1B rows, GROUP BY)(milliseconds) 50-500ms — Query Latency (Typical Analytical Query)(seconds) <1 second — Data Ingestion Throughput(rows/second) 1,000,000+ rows/sec —

Memory Footprint per 1GB Data(MB)	600-900MB	150-300MB(winner)
Typical Memory Per Node(GB)	16-64	16-32GB for analytics workload(winner)
Memory Per Node(GB per 1M events/sec)	50-200	—

Maximum Events/Sec per Node(events/sec)	100K-500K(winner)	10K-50K
Events/Second Ingestion(events/sec)	500,000/sec (streaming)	—

Typical Cluster Setup Cost(USD/month (3-node))	$2500-5000	$1500-3000(winner)
Typical Cost per TB/year(USD)	$2000-3500	—

Multi-table JOIN Support(capability level)	Limited (requires denormalization)	Full support (INNER, LEFT, RIGHT, FULL)

SQL Compatibility(percentage)	Custom JSON/Druid QL	PQL + Limited SQL (60% compatibility)
Typical Use Case Flexibility	Real-time metrics (specialized)	Ad-hoc queries (generalized)
JOIN Operation Support	Limited (basic)	Full support
Full-Text Search Capability	Basic (limited analyzers)	Limited (phrase queries, basic tokenization)
High-Cardinality Dimension Support(unique values)	1M-10M practical limit	100M+ with dictionary encoding(winner)
Show 3 more attributes Real-time Streaming Ingestion Native (Kafka, S3, MQ) — Real-time Upsert Support(boolean) Yes — Built-in ML Models(count) 0 (requires external tools) —

Enterprise Deployments(thousands)	500+ (Airbnb, Netflix, etc)	1000+ (LinkedIn, Uber, etc)(winner)

Minimum Cluster Size for 1TB Dataset(nodes)	3-5 nodes(winner)	5-8 nodes
Deployment Options	Self-hosted, cloud, hybrid, open-source	On-prem, Kubernetes, multi-cloud
Minimum Cluster Size(nodes)	3 (recommended)(winner)	3-5
Typical Node Memory(GB)	16-64GB	—
Minimum Cluster Nodes Required(nodes)	5-7	—

Native SQL Support	Druid SQL (Full)	PQL (Custom) + Presto Bridge

GitHub Stars (Community Activity)(count)	15,800(winner)	5,300
GitHub Stars (2026)(stars)	15,000+	—

Data Compression Ratio (metrics)(ratio)	10:1	—
Storage Compression Ratio(ratio)	10:1 to 20:1	15:1 to 25:1(winner)
Segment Size (typical)(GB)	100-500MB segments(winner)	1-5GB segments
Data Compression Ratio(x)	3:1 to 10:1 (avg 6:1)	—

GitHub Stars(stars)	15,200	—
GitHub Stars (as of 2026)(stars)	8,200+	—

Minimum Cluster Node Count(nodes)	3	—
Setup Time (to production)(days)	14-30	—
Operational Management Overhead(text)	High (cluster tuning, scaling, monitoring)	—
Operational Components(count)	12+ (Coordinator, Broker, Historical, MiddleManager, Overlord, Router)	8+ (Broker, Server, Controller, Minion)(winner)
Learning Curve (Complexity)(months to production)	2-3 months	1-2 months(winner)
Show 2 more attributes Infrastructure Management Self-hosted (high DevOps) — Typical Deployment Complexity(relative score) Medium-High (columnar tuning) —

Third-Party Integrations(native integrations)	300+	—

Memory Overhead (1M events)(MB per node)	120	—

Maximum Cluster Size(nodes)	Unlimited (distributed)	1000+
Concurrent Query Capacity(concurrent users)	300	—
Maximum Recommended Node Storage(TB)	0.5-2TB per node	—
Max Dataset Size (Practical)(TB)	100-500TB (hot data)	—
Maximum Recommended Cluster Size(nodes)	500+ (LinkedIn runs 1,000+)	—
Show 5 more attributes Maximum Scalable Dataset Size(GB) 100,000+ — Maximum Daily Event Throughput(billion events/day) 10+ billion events/day (proven at LinkedIn, Airbnb) — Concurrent Users Supported(users) 100-500 (depends on cluster config) — Maximum Practical Dataset Size(petabytes) 10+ PB (proven at scale) — Maximum Table Size(TB) Petabyte-scale (distributed) —

Typical Query Cost (per TB scanned)(USD)	$0.10-$0.50	—
Query Cost (On-Demand)(USD per TB scanned)	Included in storage/infrastructure	—
Annual Infrastructure Cost (1TB dataset)(USD)	50,000-150,000	—
Base Monthly Cost (Small Cluster)(USD)	Self-hosted infrastructure costs (highly variable)	—
On-Demand Query Pricing(USD per TB scanned)	Free (self-hosted) / $0.10-0.50 (managed)	—

Supported Data Retention(duration)	Time-window based (7 days to 3 years typical)	—

SQL Standard Compliance(% compatibility)	~60% ANSI SQL	85% SQL coverage(winner)
SQL Compliance	Proprietary Druid SQL with extensions	—

Maximum Ingestion Rate(events/second)	1,000,000+	—
Real-Time Ingestion Support	Native (Kafka, S3, HDFS)	—
Peak Ingestion Speed(events per second)	1,000,000+	—

Storage Cost(USD per TB per month)	$5 (self-hosted avg)	—
Storage Cost per TB/month(USD)	$120-180	—
Per-Query Cost (1TB scan)(USD)	$0 (infrastructure only)	—
Per-Query Cost (1 TB scanned)(USD)	$0 (open-source)	—

Time to First Query(minutes)	45 (self-hosted setup)(winner)	45-60 minutes

Streaming Ingestion Rate(events/second)	1M+(winner)	500K

Ingestion Latency (Kafka to query-ready)(seconds)	0.5-2 seconds	—
Streaming Ingestion Latency(seconds)	<1 second	—

SQL Compliance Level(null)	Standard ANSI SQL	—

Native Streaming Sources(null)	Kafka, Pulsar, Kinesis, S3	—

Enterprise Support Availability(null)	StarTree (24/7 SLA options)	—

Ingestion Latency (end-to-end)(milliseconds)	100-500ms	—

Memory Usage per Query(MB)	100-400MB	—

Multi-tenancy Isolation	Native tenant isolation	—
Deployment Flexibility(options)	Kubernetes, on-premises, all cloud providers	—
Multi-node Support(boolean)	Yes (native distributed)	—

Minimum Recommended Cluster Size(nodes)	5-7 nodes (3 controllers + 2-4 brokers)	—
Setup Time to Production(minutes)	12 weeks	—

Annual TCO (100TB dataset)(USD)	$150,000	—

Time to First Production Query(days)	14-30 days (requires schema design and tuning)	—
Setup Time (minutes)(minutes)	240-480	—

SQL Support	Native SQL with PQL extensions (ANSI-compliant subset)	—

Supported Programming Languages(languages)	Java, Python (limited)	—

Concurrent User Support(users)	100-500 typical	—

Data Freshness(seconds)	Sub-second to 1 minute	—
Ingestion Streaming Support(events per second)	1M+ eps native	—

License Type	Apache 2.0 Open Source	—

Index Size to Data Ratio(multiplier)	0.1-0.3x	—

Full-Text Search Optimization	Not optimized (secondary feature)	—

GitHub Stars (Community Size Proxy)(stars)	9,200+	—

SQL Query Support	Native PinotSQL (full ANSI compliance)	—

ANSI SQL Compliance(percentage)	70%	—

Deployment Components(count)	5-7 components	—

AApache Druid

Apache Pinot

Query Latency (1B rows, 100 dimensions)(milliseconds)

50-150ms

50-100ms

Query Latency (p95 on Real-Time Data)(milliseconds)

100-500ms

500-2000ms

Max Ingestion Throughput(events/second)

500,000

1,000,000-2,000,000 events/sec

Query Latency (50th percentile)(milliseconds)

150

—

Query Latency (p99)(milliseconds)

500ms

50-200ms

Show 21 more attributes

Data Ingestion Rate(GB/sec)

1,000,000

500,000-1,000,000

P99 Query Latency(milliseconds)

100-500ms

500-2000ms

Median Query Latency(milliseconds)

10-100ms

—

Data Ingestion Latency(seconds)

1-5 seconds (streaming)

0.1 (streaming)

Maximum Dataset Size Supported(GB)

Petabyte+ (with cluster scaling)

—

Query Latency (P99 percentile)(milliseconds)

250ms

—

Aggregation Query Throughput(queries/second)

50K-100K

100K-500K

P99 Query Latency (1B rows, aggregation)(milliseconds)

100-500ms

—

Query Latency (P95)(milliseconds)

100-500ms

—

Query Latency on 1GB Dataset(milliseconds)

100-500

—

Concurrent Queries Supported(queries)

1000+

—

Query Latency (Average)(milliseconds)

<100ms

—

Query Latency (Median)(milliseconds)

<100 ms

—

Time to Deploy(seconds (average))

40-80 hours (cluster provisioning, tuning)

—

Ingestion Rate (events/second)(events/sec)

1,000,000+

—

Query Latency (1B rows)(seconds)

2-5

—

Maximum Recommended Dataset Size(rows)

10,000+

—

Deployment Time(seconds)

6

—

Typical Query Latency (1B rows, GROUP BY)(milliseconds)

50-500ms

—

Query Latency (Typical Analytical Query)(seconds)

<1 second

—

Data Ingestion Throughput(rows/second)

1,000,000+ rows/sec

—

Memory Footprint per 1GB Data(MB)

600-900MB

150-300MB

Typical Memory Per Node(GB)

16-64

16-32GB for analytics workload

Memory Per Node(GB per 1M events/sec)

50-200

—

Maximum Events/Sec per Node(events/sec)

100K-500K

10K-50K

Events/Second Ingestion(events/sec)

500,000/sec (streaming)

—

Typical Cluster Setup Cost(USD/month (3-node))

$2500-5000

$1500-3000

Typical Cost per TB/year(USD)

$2000-3500

—

Multi-table JOIN Support(capability level)

Limited (requires denormalization)

Full support (INNER, LEFT, RIGHT, FULL)

SQL Compatibility(percentage)

Custom JSON/Druid QL

PQL + Limited SQL (60% compatibility)

Typical Use Case Flexibility

Real-time metrics (specialized)

Ad-hoc queries (generalized)

JOIN Operation Support

Limited (basic)

Full support

Full-Text Search Capability

Basic (limited analyzers)

Limited (phrase queries, basic tokenization)

High-Cardinality Dimension Support(unique values)

1M-10M practical limit

100M+ with dictionary encoding

Show 3 more attributes

Real-time Streaming Ingestion

Native (Kafka, S3, MQ)

—

Real-time Upsert Support(boolean)

Yes

—

Built-in ML Models(count)

0 (requires external tools)

—

Enterprise Deployments(thousands)

500+ (Airbnb, Netflix, etc)

1000+ (LinkedIn, Uber, etc)

Minimum Cluster Size for 1TB Dataset(nodes)

3-5 nodes

5-8 nodes

Deployment Options

Self-hosted, cloud, hybrid, open-source

On-prem, Kubernetes, multi-cloud

Minimum Cluster Size(nodes)

3 (recommended)

3-5

Typical Node Memory(GB)

16-64GB

—

Minimum Cluster Nodes Required(nodes)

5-7

—

Native SQL Support

Druid SQL (Full)

PQL (Custom) + Presto Bridge

GitHub Stars (Community Activity)(count)

15,800

5,300

GitHub Stars (2026)(stars)

15,000+

—

Data Compression Ratio (metrics)(ratio)

10:1

—

Storage Compression Ratio(ratio)

10:1 to 20:1

15:1 to 25:1

Segment Size (typical)(GB)

100-500MB segments

1-5GB segments

Data Compression Ratio(x)

3:1 to 10:1 (avg 6:1)

—

GitHub Stars(stars)

15,200

—

GitHub Stars (as of 2026)(stars)

8,200+

—

Minimum Cluster Node Count(nodes)

3

—

Setup Time (to production)(days)

14-30

—

Operational Management Overhead(text)

High (cluster tuning, scaling, monitoring)

—

Operational Components(count)

12+ (Coordinator, Broker, Historical, MiddleManager, Overlord, Router)

8+ (Broker, Server, Controller, Minion)

Learning Curve (Complexity)(months to production)

2-3 months

1-2 months

Show 2 more attributes

Infrastructure Management

Self-hosted (high DevOps)

—

Typical Deployment Complexity(relative score)

Medium-High (columnar tuning)

—

Third-Party Integrations(native integrations)

300+

—

Memory Overhead (1M events)(MB per node)

120

—

Maximum Cluster Size(nodes)

Unlimited (distributed)

1000+

Concurrent Query Capacity(concurrent users)

300

—

Maximum Recommended Node Storage(TB)

0.5-2TB per node

—

Max Dataset Size (Practical)(TB)

100-500TB (hot data)

—

Maximum Recommended Cluster Size(nodes)

500+ (LinkedIn runs 1,000+)

—

Show 5 more attributes

Maximum Scalable Dataset Size(GB)

100,000+

—

Maximum Daily Event Throughput(billion events/day)

10+ billion events/day (proven at LinkedIn, Airbnb)

—

Concurrent Users Supported(users)

100-500 (depends on cluster config)

—

Maximum Practical Dataset Size(petabytes)

10+ PB (proven at scale)

—

Maximum Table Size(TB)

Petabyte-scale (distributed)

—

Typical Query Cost (per TB scanned)(USD)

$0.10-$0.50

—

Query Cost (On-Demand)(USD per TB scanned)

Included in storage/infrastructure

—

Annual Infrastructure Cost (1TB dataset)(USD)

50,000-150,000

—

Base Monthly Cost (Small Cluster)(USD)

Self-hosted infrastructure costs (highly variable)

—

On-Demand Query Pricing(USD per TB scanned)

Free (self-hosted) / $0.10-0.50 (managed)

—

Supported Data Retention(duration)

Time-window based (7 days to 3 years typical)

—

SQL Standard Compliance(% compatibility)

~60% ANSI SQL

85% SQL coverage

SQL Compliance

Proprietary Druid SQL with extensions

—

Maximum Ingestion Rate(events/second)

1,000,000+

—

Real-Time Ingestion Support

Native (Kafka, S3, HDFS)

—

Peak Ingestion Speed(events per second)

1,000,000+

—

Storage Cost(USD per TB per month)

$5 (self-hosted avg)

—

Storage Cost per TB/month(USD)

$120-180

—

Per-Query Cost (1TB scan)(USD)

$0 (infrastructure only)

—

Per-Query Cost (1 TB scanned)(USD)

$0 (open-source)

—

Time to First Query(minutes)

45 (self-hosted setup)

45-60 minutes

Streaming Ingestion Rate(events/second)

1M+

500K

Ingestion Latency (Kafka to query-ready)(seconds)

0.5-2 seconds

—

Streaming Ingestion Latency(seconds)

<1 second

—

SQL Compliance Level(null)

Standard ANSI SQL

—

Native Streaming Sources(null)

Kafka, Pulsar, Kinesis, S3

—

Enterprise Support Availability(null)

StarTree (24/7 SLA options)

—

Ingestion Latency (end-to-end)(milliseconds)

100-500ms

—

Memory Usage per Query(MB)

100-400MB

—

Multi-tenancy Isolation

Native tenant isolation

—

Deployment Flexibility(options)

Kubernetes, on-premises, all cloud providers

—

Multi-node Support(boolean)

Yes (native distributed)

—

Minimum Recommended Cluster Size(nodes)

5-7 nodes (3 controllers + 2-4 brokers)

—

Setup Time to Production(minutes)

12 weeks

—

Annual TCO (100TB dataset)(USD)

$150,000

—

Time to First Production Query(days)

14-30 days (requires schema design and tuning)

—

Setup Time (minutes)(minutes)

240-480

—

SQL Support

Native SQL with PQL extensions (ANSI-compliant subset)

—

Supported Programming Languages(languages)

Java, Python (limited)

—

Concurrent User Support(users)

100-500 typical

—

Data Freshness(seconds)

Sub-second to 1 minute

—

Ingestion Streaming Support(events per second)

1M+ eps native

—

License Type

Apache 2.0 Open Source

—

Index Size to Data Ratio(multiplier)

0.1-0.3x

—

Full-Text Search Optimization

Not optimized (secondary feature)

—

GitHub Stars (Community Size Proxy)(stars)

9,200+

—

SQL Query Support

Native PinotSQL (full ANSI compliance)

—

ANSI SQL Compliance(percentage)

70%

—

Deployment Components(count)

5-7 components

—

Pros & Cons

10 pros·6 cons across both

AD

Apache Druid

+5-3

Pros

Sub-second query latency (p99: 100-500ms) for real-time dashboards
Supports 1M+ events/second ingestion with native streaming from Kafka
Built-in time-series roll-up and downsampling for efficient storage
Excellent for metrics and monitoring use cases with automatic data retention policies
Segment-level filtering and indexing optimized for time-range queries

Cons

Operational complexity requires managing Coordinator, Broker, Historical, and MiddleManager nodes
Limited performance on high-cardinality dimension queries compared to Pinot
Steeper learning curve with segment management and tiering strategies

Apache Pinot

+5-3

Pros

Exceptional performance on high-cardinality dimensions (100M+ unique values) with dictionary encoding
100K-500K QPS aggregation throughput, 5-10x higher than Druid on fan-out queries
Superior compression (15:1 to 25:1 ratio) reduces storage costs by 30-40%
Simplified deployment with broker-server architecture (fewer moving parts than Druid)
Better multi-tenancy isolation and query prioritization at scale

Cons

Higher query latency (p99: 500-2000ms), not suitable for sub-100ms SLA requirements
Ingestion throughput capped at 500K events/second, half of Druid's capacity
Weaker built-in support for time-series specific operations like roll-up and retention policies

Frequently Asked Questions

5 questions

Druid is significantly better for real-time dashboards due to its sub-second query latency (p99: 100-500ms vs Pinot's 500-2000ms). If you need dashboard refreshes every 1-5 seconds with <500ms response times, Druid is the clear choice. Pinot's latency makes it unsuitable for interactive real-time use cases.

Resources & Learn More

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Last updated: July 9, 2026AI generated

Druid vs Pinot 2026: Real-Time OLAP Comparison

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