Pinot is optimized for real-time analytics on streaming data with lower latency (sub-second queries), while ClickHouse excels at batch processing and complex analytical queries with superior compression (10-40x) and broader SQL compatibility. Pinot uses a distributed architecture with brokers and servers, whereas ClickHouse employs a simpler peer-to-peer model.
Pinot vs ClickHouse — Side-by-side comparison on A Versus B
Open-source distributed OLAP database for real-time analytics on streaming data
Open-source columnar OLAP database with exceptional compression and SQL compatibility for analytical queries.
Query Latency (P99). Data Compression Ratio. Real-Time Ingestion Speed. SQL Standard Compliance. Typical Storage per GB Data. Deployment Complexity. OLTP Query Support
Choose Pinot if you need sub-100ms query latency on streaming data with 1M+ events per second ingestion (common in ad-tech, user analytics, real-time dashboards). Choose ClickHouse if you prioritize ease of deployment, superior compression, complex analytical queries, and can tolerate 200-500ms latency (ideal for business intelligence, log analytics, metrics aggregation).
Apache Pinot: 50-100ms | ClickHouse: N/A
Apache Pinot: 150-300MB | ClickHouse: N/A
Apache Pinot: $1500-3000 | ClickHouse: N/A
Apache Pinot: Full support (INNER, LEFT, RIGHT, FULL) | ClickHouse: N/A
Apache Pinot: PQL + Limited SQL (60% compatibility) | ClickHouse: MySQL-compatible with ClickHouse extensions
Apache Pinot: 1000+ (LinkedIn, Uber, etc) | ClickHouse: N/A
Apache Pinot: 100-500ms | ClickHouse: 1000-10000ms
Apache Pinot: 100-400MB | ClickHouse: 50-200MB
Apache Pinot: PQL (Custom) + Presto Bridge | ClickHouse: Standard SQL with extensions
Apache Pinot: Native tenant isolation | ClickHouse: Limited/requires custom logic
Apache Pinot: 500-2000ms | ClickHouse: N/A
Apache Pinot: 5-8 nodes | ClickHouse: N/A
Apache Pinot: ~4-6x columnar | ClickHouse: N/A
Apache Pinot: Ad-hoc queries (generalized) | ClickHouse: N/A
Apache Pinot: Full support | ClickHouse: N/A
Apache Pinot: 1,000,000-2,000,000 events/sec | ClickHouse: 100,000-500,000 events/sec
Apache Pinot: 50-200ms | ClickHouse: 200-500ms
Apache Pinot: $120-180 | ClickHouse: $40-60
Apache Pinot: 16-64GB | ClickHouse: 8-32GB
Apache Pinot: 5-7 nodes (3 controllers + 2-4 brokers) | ClickHouse: 3-5 nodes
Apache Pinot: 100-500TB (hot data) | ClickHouse: 1000TB+ (unlimited with tiering)
Apache Pinot: 100-500ms | ClickHouse: N/A
Apache Pinot: $0 (infrastructure only) | ClickHouse: N/A
Apache Pinot: 0.1 (streaming) | ClickHouse: Microseconds to milliseconds
Apache Pinot: 12 weeks | ClickHouse: N/A
Apache Pinot: 85% SQL coverage | ClickHouse: 70% ANSI SQL
Apache Pinot: 500,000/sec (streaming) | ClickHouse: N/A
Apache Pinot: $150,000 | ClickHouse: N/A
Apache Pinot: 50-200ms | ClickHouse: N/A
Apache Pinot: 500+ (LinkedIn runs 1,000+) | ClickHouse: N/A
Apache Pinot: Limited (phrase queries, basic tokenization) | ClickHouse: N/A
Apache Pinot: 14-30 days (requires schema design and tuning) | ClickHouse: N/A
Apache Pinot: Native SQL with PQL extensions (ANSI-compliant subset) | ClickHouse: N/A
Apache Pinot: 16-32GB for analytics workload | ClickHouse: N/A
Apache Pinot: 8,200+ | ClickHouse: 25000+
Apache Pinot: 100,000+ | ClickHouse: N/A
Apache Pinot: Native (Kafka, S3, MQ) | ClickHouse: N/A
Apache Pinot: Java, Python (limited) | ClickHouse: N/A
Annual Infrastructure Cost (1TB dataset)
Apache Pinot: 50,000-150,000 | ClickHouse: N/A
Apache Pinot: 100-500 typical | ClickHouse: N/A
Apache Pinot: Sub-second to 1 minute | ClickHouse: N/A
Apache Pinot: Apache 2.0 Open Source | ClickHouse: N/A
Apache Pinot: Kubernetes, on-premises, all cloud providers | ClickHouse: N/A
Apache Pinot: 1M+ eps native | ClickHouse: N/A
Apache Pinot: Self-hosted infrastructure costs (highly variable) | ClickHouse: N/A
Apache Pinot: <1 second | ClickHouse: N/A
Apache Pinot: Free (self-hosted) / $0.10-0.50 (managed) | ClickHouse: N/A
Apache Pinot: Self-hosted (high DevOps) | ClickHouse: N/A
Apache Pinot: 10+ billion events/day (proven at LinkedIn, Airbnb) | ClickHouse: N/A
Apache Pinot: 40-80 hours (cluster provisioning, tuning) | ClickHouse: N/A
Apache Pinot: 100-500 (depends on cluster config) | ClickHouse: N/A
Apache Pinot: 1,000,000+ | ClickHouse: N/A
Apache Pinot: Yes (native distributed) | ClickHouse: N/A
Typical Query Latency (1B rows, GROUP BY)
Apache Pinot: 50-500ms | ClickHouse: N/A
Apache Pinot: 0.1-0.3x | ClickHouse: N/A
Apache Pinot: Not optimized (secondary feature) | ClickHouse: N/A
Apache Pinot: Native (Kafka, S3, HDFS) | ClickHouse: N/A
Apache Pinot: Medium-High (columnar tuning) | ClickHouse: N/A
Apache Pinot: 10+ PB (proven at scale) | ClickHouse: N/A
Apache Pinot: Native PinotSQL (full ANSI compliance) | ClickHouse: N/A
Apache Pinot: 5-7 components | ClickHouse: 1-2 components
Apache Pinot: 45-60 minutes | ClickHouse: 5-10 minutes
Query Latency (Typical Analytical Query)
Apache Pinot: 1,000,000+ rows/sec | ClickHouse: N/A
Apache Pinot: $0 (open-source) | ClickHouse: N/A
Apache Pinot: 0 (requires external tools) | ClickHouse: N/A
Apache Pinot: Petabyte-scale (distributed) | ClickHouse: N/A
Apache Pinot: On-prem, Kubernetes, multi-cloud | ClickHouse: N/A
Apache Pinot: N/A | ClickHouse: 1.2 seconds
Apache Pinot: N/A | ClickHouse: 240 minutes
Apache Pinot: N/A | ClickHouse: 1,000,000 events/sec
Apache Pinot: N/A | ClickHouse: Not native
Apache Pinot: N/A | ClickHouse: 8:1-12:1
Apache Pinot: N/A | ClickHouse: Open-source (free) + optional support
Apache Pinot: N/A | ClickHouse: 7/10 (moderate-hard)
Apache Pinot: N/A | ClickHouse: 500-2000ms
Apache Pinot: N/A | ClickHouse: 10:1 to 40:1
Apache Pinot: N/A | ClickHouse: 500-2000MB
Apache Pinot: N/A | ClickHouse: 1-5 million rows/sec
Apache Pinot: N/A | ClickHouse: 30-120 minutes
Apache Pinot: N/A | ClickHouse: 10-60 seconds
Apache Pinot: N/A | ClickHouse: 50-500ms
Apache Pinot: N/A | ClickHouse: 1000+ nodes tested
Apache Pinot: N/A | ClickHouse: Limited (Kafka via TableEngine)
Apache Pinot: N/A | ClickHouse: No ACID (eventual consistency)
Apache Pinot: N/A | ClickHouse: ~1,000,000+ GB (1+ PB)
Apache Pinot: N/A | ClickHouse: 500-2000 MB
Apache Pinot: N/A | ClickHouse: 12+ formats (TSV, Native, Avro, Protobuf, etc.)
Query Latency (100M rows, simple aggregation)
Apache Pinot: N/A | ClickHouse: 500-1500ms
Apache Pinot: N/A | ClickHouse: 100TB+ efficiently
Apache Pinot: N/A | ClickHouse: 95% (PostgreSQL-compatible)
Apache Pinot: N/A | ClickHouse: Standard SQL; requires manual time bucketing
Query Latency (1B row scan, 10 column aggregate)
Apache Pinot: N/A | ClickHouse: 50-100ms
Apache Pinot: N/A | ClickHouse: 1 (can run standalone)
Apache Pinot: N/A | ClickHouse: Unlimited (resource-based)
Apache Pinot: N/A | ClickHouse: 5-10 (via third-party)
Query Latency (1 billion rows, simple SELECT)
Apache Pinot: N/A | ClickHouse: 1,000,000
Apache Pinot: N/A | ClickHouse: 40-80 hours
Apache Pinot: N/A | ClickHouse: $500 (ClickHouse Cloud starter)
Apache Pinot: N/A | ClickHouse: 4+ (AWS, Azure, GCP, on-premise)
Apache Pinot: N/A | ClickHouse: 60-300 (manual cluster resize required)
Average Query Latency (1 Billion Row Scan)
Apache Pinot: N/A | ClickHouse: 1440 (self-managed) / 60 (managed)
Apache Pinot: N/A | ClickHouse: 100,000+
Apache Pinot: N/A | ClickHouse: 99.0% (self-managed) / 99.95% (managed)
Apache Pinot: N/A | ClickHouse: 3 (S3, Kinesis via 3rd party, basic)
Apache Pinot: N/A | ClickHouse: No (third-party integration required)
Apache Pinot: N/A | ClickHouse: Native optimization, ideal for billions of events
Apache Pinot: N/A | ClickHouse: 99.5% (self-hosted dependent)
How to choose between Apache Pinot and ClickHouse
Pinot vs ClickHouse — Structured Comparison Data
Machine-readable comparison dataset for Pinot vs ClickHouse. Contains 142 attribute comparisons, 5 FAQ pairs.
Pinot vs ClickHouse — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Pinot vs ClickHouse.
When should I use Pinot over ClickHouse?
Use Pinot when you need real-time analytics with sub-100ms latency on streaming data at scale (1M+ events/sec). Pinot excels at powering live dashboards, real-time fraud detection, and ad-tech analytics where freshness is critical. It's built specifically for these use cases with optimized segment pruning and distributed query execution.
What are the main cost differences between Pinot and ClickHouse?
ClickHouse is 3-5x more cost-efficient due to superior compression (10-40x vs 3-8x), resulting in storage costs of $40-60 per TB/month versus Pinot's $120-180 per TB/month. However, Pinot requires fewer operational hours due to more automated features, while ClickHouse's simplicity reduces DevOps costs by an estimated 70-80%.
Can ClickHouse handle real-time analytics?
ClickHouse can handle real-time analytics but is not optimized for it. Its ingestion speed (100k-500k events/sec) and query latency (200-500ms P99) make it suitable for near-real-time use cases with 1-5 minute refresh intervals. For sub-second freshness requirements, Pinot is the better choice.
Which database is easier to deploy and operate?
ClickHouse is significantly easier to deploy. It requires only a single binary and minimal configuration (5-10 minutes to first query), while Pinot requires managing 5-7 operational components including Zookeeper, brokers, and servers. ClickHouse reduces operational complexity by 80% and is ideal for teams without dedicated database engineers.
ClickHouse offers 95% ANSI SQL compliance, allowing most SQL queries to run unchanged. Pinot supports only 70% of ANSI SQL standard and often requires query rewrites or workarounds. If SQL portability is critical, ClickHouse is the superior choice.
How to Choose Between Apache Pinot and ClickHouse
Query Latency (P99): Apache Pinot has 50-200ms, while ClickHouse has 200-500ms.
Data Compression Ratio: Apache Pinot has 3-8x, while ClickHouse has 10-40x.
Real-Time Ingestion Speed: Apache Pinot has 1M+ events/sec, while ClickHouse has 100k-500k events/sec.
SQL Standard Compliance: Apache Pinot has 70% ANSI SQL, while ClickHouse has 95% ANSI SQL.
Typical Storage per GB Data: Apache Pinot has 125-330MB, while ClickHouse has 25-100MB.
Deployment Complexity: Apache Pinot has High (5+ components), while ClickHouse has Low (single binary).
OLTP Query Support: Apache Pinot has Limited (point lookups only), while ClickHouse has Full OLTP capability.