Apache Pinot is a columnar OLAP database optimized for real-time analytics on massive datasets with sub-second query latency, while Elasticsearch is a distributed search and analytics engine designed primarily for full-text search, logging, and operational analytics. Pinot excels at numerical aggregations over billions of rows; Elasticsearch excels at text search and log analysis.
Pinot vs Elasticsearch — Side-by-side comparison on A Versus B
Columnar OLAP database for real-time analytics at scale
Open-source distributed search and analytics engine for logs, metrics, and traces with flexible deployment options.
Primary Use Case. Query Latency (typical). Storage Model. Full-Text Search Capability. Typical Cluster Size. Index Size vs Data Size Ratio. Community Adoption (GitHub Stars)
Choose Pinot if you need to run fast aggregation queries (COUNT, SUM, AVG) over billions of events with sub-second latency and have primarily numerical data—ideal for user analytics, ad-tech dashboards, and real-time metrics. Choose Elasticsearch if you prioritize full-text search, log aggregation, or need flexibility to search across text fields with rich query DSL and a larger ecosystem—better for security/compliance logs, application performance monitoring, and general-purpose search.
Apache Pinot: 50-100ms | Elasticsearch: N/A
Apache Pinot: 150-300MB | Elasticsearch: N/A
Apache Pinot: 10K-50K | Elasticsearch: N/A
Apache Pinot: $1500-3000 | Elasticsearch: N/A
Apache Pinot: Full support (INNER, LEFT, RIGHT, FULL) | Elasticsearch: N/A
Apache Pinot: PQL + Limited SQL (60% compatibility) | Elasticsearch: N/A
Apache Pinot: 1000+ (LinkedIn, Uber, etc) | Elasticsearch: N/A
Apache Pinot: 8-10x | Elasticsearch: 2-4x
Apache Pinot: 100-500ms | Elasticsearch: N/A
Apache Pinot: 100-400MB | Elasticsearch: N/A
Apache Pinot: PQL (Custom) + Presto Bridge | Elasticsearch: N/A
Apache Pinot: Native tenant isolation | Elasticsearch: N/A
Apache Pinot: $2000-3500 | Elasticsearch: N/A
Apache Pinot: 500-2000ms | Elasticsearch: N/A
Apache Pinot: 500,000-1,000,000 | Elasticsearch: N/A
Apache Pinot: 5-8 nodes | Elasticsearch: N/A
Apache Pinot: 5,300 | Elasticsearch: N/A
Apache Pinot: ~4-6x columnar | Elasticsearch: N/A
Apache Pinot: Ad-hoc queries (generalized) | Elasticsearch: N/A
Apache Pinot: Full support | Elasticsearch: N/A
Apache Pinot: 1,000,000-2,000,000 events/sec | Elasticsearch: 75,000
Apache Pinot: 100-500ms (real-time) | Elasticsearch: 50-200ms
Apache Pinot: $200-400 | Elasticsearch: N/A
Apache Pinot: 16-64GB | Elasticsearch: N/A
Apache Pinot: 5-7 nodes (3 controllers + 2-4 brokers) | Elasticsearch: N/A
Apache Pinot: 100-500TB (hot data) | Elasticsearch: N/A
Apache Pinot: $0 (infrastructure only) | Elasticsearch: N/A
Apache Pinot: 0.1 (streaming) | Elasticsearch: N/A
Apache Pinot: 12 weeks | Elasticsearch: 40-80 (self-hosted)
Apache Pinot: Limited by infrastructure | Elasticsearch: N/A
Apache Pinot: ~60% (Pinot Query Language variant) | Elasticsearch: N/A
Apache Pinot: 500,000/sec (streaming) | Elasticsearch: N/A
Apache Pinot: $150,000 | Elasticsearch: N/A
Apache Pinot: 50-200ms | Elasticsearch: 100-500ms
Apache Pinot: 500+ (LinkedIn runs 1,000+) | Elasticsearch: 1,000+ (tested to 10,000 with advanced tuning)
Apache Pinot: Limited (phrase queries, basic tokenization) | Elasticsearch: Native (analyzers, relevance tuning, fuzzy matching)
Apache Pinot: 14-30 days (requires schema design and tuning) | Elasticsearch: 1-3 days (schemaless indexing)
Apache Pinot: Native SQL with PQL extensions (ANSI-compliant subset) | Elasticsearch: SQL via plugin (Elastic SQL), primary is Query DSL
Apache Pinot: 16-32GB for analytics workload | Elasticsearch: 32-64GB for equivalent throughput
Apache Pinot: 8,200+ | Elasticsearch: 66,000+
Apache Pinot: 240-480 | Elasticsearch: N/A
Apache Pinot: 100-500 | Elasticsearch: N/A
Apache Pinot: 100,000+ | Elasticsearch: N/A
Apache Pinot: Native (Kafka, S3, MQ) | Elasticsearch: N/A
Apache Pinot: 1000+ | Elasticsearch: N/A
Apache Pinot: Java, Python (limited) | Elasticsearch: Unlimited (via client libraries)
Annual Infrastructure Cost (1TB dataset)
Apache Pinot: 50,000-150,000 | Elasticsearch: N/A
Apache Pinot: 100-500 typical | Elasticsearch: N/A
Apache Pinot: <100ms | Elasticsearch: N/A
Apache Pinot: Sub-second to 1 minute | Elasticsearch: N/A
Apache Pinot: Apache 2.0 Open Source | Elasticsearch: N/A
Apache Pinot: Kubernetes, on-premises, all cloud providers | Elasticsearch: N/A
Apache Pinot: 1M+ eps native | Elasticsearch: N/A
Apache Pinot: Self-hosted infrastructure costs (highly variable) | Elasticsearch: N/A
Apache Pinot: <100 ms | Elasticsearch: N/A
Apache Pinot: <1 second | Elasticsearch: N/A
Apache Pinot: Free (self-hosted) / $0.10-0.50 (managed) | Elasticsearch: N/A
Apache Pinot: Manual cluster management required | Elasticsearch: N/A
Apache Pinot: 10+ billion events/day (proven at LinkedIn, Airbnb) | Elasticsearch: N/A
Apache Pinot: 40-80 hours (cluster provisioning, tuning) | Elasticsearch: N/A
Apache Pinot: 100-500 (depends on cluster config) | Elasticsearch: N/A
Apache Pinot: 1,000,000+ | Elasticsearch: N/A
Apache Pinot: 10,000+ | Elasticsearch: N/A
Apache Pinot: 50-200 | Elasticsearch: N/A
Apache Pinot: Yes (native distributed) | Elasticsearch: N/A
Typical Query Latency (1B rows, GROUP BY)
Apache Pinot: 50-500ms | Elasticsearch: 500-2000ms
Apache Pinot: 0.1-0.3x | Elasticsearch: 0.5-2x
Apache Pinot: Not optimized (secondary feature) | Elasticsearch: Native, highly optimized (primary feature)
Apache Pinot: 9,200+ | Elasticsearch: 68,000+
Apache Pinot: Native (Kafka, S3, HDFS) | Elasticsearch: Via Beats and Logstash (indirect)
Apache Pinot: Medium-High (columnar tuning) | Elasticsearch: Low-Medium (simpler operations)
Apache Pinot: 10+ PB (proven at scale) | Elasticsearch: 5+ PB (operational limit)
Apache Pinot: Native PinotSQL (full ANSI compliance) | Elasticsearch: SQL plugin available (limited JOIN support)
Apache Pinot: N/A | Elasticsearch: $0.10 - $0.20
Apache Pinot: N/A | Elasticsearch: 30+ days (self-hosted)
Apache Pinot: N/A | Elasticsearch: Petabyte-scale (1000+ TB)
Apache Pinot: N/A | Elasticsearch: 7 (Lucene/DSL - steep learning curve)
Apache Pinot: N/A | Elasticsearch: Self-hosted, SaaS (Elastic Cloud), or Kubernetes
Apache Pinot: N/A | Elasticsearch: 30-90 days
Apache Pinot: N/A | Elasticsearch: $0 (self-hosted)
Apache Pinot: N/A | Elasticsearch: $0 (unlimited after infrastructure cost)
Apache Pinot: N/A | Elasticsearch: 50-200ms
Apache Pinot: N/A | Elasticsearch: 0 (requires custom hardening)
Apache Pinot: N/A | Elasticsearch: 3 (anomaly detection, forecasting, outlier detection)
Apache Pinot: N/A | Elasticsearch: Unlimited (enterprise only)
Apache Pinot: N/A | Elasticsearch: 12-48 (community forums)
Apache Pinot: N/A | Elasticsearch: 65,800
Apache Pinot: N/A | Elasticsearch: 2000+
Apache Pinot: N/A | Elasticsearch: $0-$10,000 (self-hosted) or $20,000-$60,000 (managed)
Apache Pinot: N/A | Elasticsearch: 45-120 (manual investigation required)
Apache Pinot: N/A | Elasticsearch: 6-12 weeks
Apache Pinot: N/A | Elasticsearch: 1000+ via community/Beats
Apache Pinot: N/A | Elasticsearch: Unlimited (storage-dependent)
Apache Pinot: N/A | Elasticsearch: 500,000+ EPS per cluster
Apache Pinot: N/A | Elasticsearch: 3-6 months
Apache Pinot: N/A | Elasticsearch: Complex Query DSL requiring technical expertise
Apache Pinot: N/A | Elasticsearch: 512MB
Apache Pinot: N/A | Elasticsearch: 68,000+ stars
Apache Pinot: N/A | Elasticsearch: 5-10 minutes
Apache Pinot: N/A | Elasticsearch: 65 docs/sec per thread
Apache Pinot: N/A | Elasticsearch: $5,000-$50,000
Apache Pinot: N/A | Elasticsearch: Via aggregations (requires custom queries)
Apache Pinot: N/A | Elasticsearch: 14-28 days
Monthly Cost (100GB index, 1M queries/month)
Apache Pinot: N/A | Elasticsearch: $200-500 self-hosted
Apache Pinot: N/A | Elasticsearch: Petabyte-scale (unlimited)
Apache Pinot: N/A | Elasticsearch: Requires DevOps expertise
Apache Pinot: N/A | Elasticsearch: 100-300ms
Apache Pinot: N/A | Elasticsearch: 9/10 (plugins, analyzers, scripting)
Apache Pinot: N/A | Elasticsearch: Community-based or 4+ hours (enterprise)
Apache Pinot: N/A | Elasticsearch: 14-28 days (self-hosted)
Apache Pinot: N/A | Elasticsearch: $3,000-$8,000
Apache Pinot: N/A | Elasticsearch: $0.02-$0.05
Apache Pinot: N/A | Elasticsearch: ~300 integrations
Apache Pinot: N/A | Elasticsearch: Unlimited (configurable)
Apache Pinot: N/A | Elasticsearch: 1.0-2.0 FTE
Apache Pinot: N/A | Elasticsearch: Advanced EQL with full customization
Apache Pinot: N/A | Elasticsearch: 8/10 (requires DevOps expertise)
Apache Pinot: N/A | Elasticsearch: 2-4 GB
Apache Pinot: N/A | Elasticsearch: 120-240 minutes
Apache Pinot: N/A | Elasticsearch: 50-200 ms
Apache Pinot: N/A | Elasticsearch: 1+ Billion (across clusters)
Apache Pinot: N/A | Elasticsearch: 40+ aggregation types
Apache Pinot: N/A | Elasticsearch: Manual configuration required
Apache Pinot: N/A | Elasticsearch: ~60,000 stars
Apache Pinot: N/A | Elasticsearch: $50,000-150,000
Apache Pinot: N/A | Elasticsearch: 14-30 days
Apache Pinot: N/A | Elasticsearch: 30-40 (via Beats)
Apache Pinot: N/A | Elasticsearch: $0 (open-source) or $12,000
Apache Pinot: N/A | Elasticsearch: Requires third-party integrations
Apache Pinot: N/A | Elasticsearch: Full Elasticsearch Query DSL and custom scripts
Apache Pinot: N/A | Elasticsearch: 1M+ active developers
Mean Time to Resolution (MTTR) Improvement
Apache Pinot: N/A | Elasticsearch: Depends on manual investigation
Apache Pinot: N/A | Elasticsearch: 80-200 hours (extensive)
Total Cost of Ownership (3-year, 500GB/month ingestion)
Apache Pinot: N/A | Elasticsearch: $120,000 (self-hosted licensing + 2 FTEs infrastructure)
Apache Pinot: N/A | Elasticsearch: Logs, metrics (via plugins), custom JSON data
Apache Pinot: N/A | Elasticsearch: 100-2000ms (depends on tuning)
Required Infrastructure Team Size (100 users)
Apache Pinot: N/A | Elasticsearch: 2-3 (cluster management + optimization)
Apache Pinot: N/A | Elasticsearch: 180,000+ (open-source)
Apache Pinot: N/A | Elasticsearch: 512 MB
Apache Pinot: N/A | Elasticsearch: 150 ms
Apache Pinot: N/A | Elasticsearch: Unlimited (petabyte-scale)
Apache Pinot: N/A | Elasticsearch: 60-120 minutes
Apache Pinot: N/A | Elasticsearch: 1000+ plugins/integrations
Apache Pinot: N/A | Elasticsearch: 30+ languages
Apache Pinot: N/A | Elasticsearch: Requires configuration/plugin
Apache Pinot: N/A | Elasticsearch: 500,000+ companies worldwide
Apache Pinot: N/A | Elasticsearch: $180,000-$240,000
Apache Pinot: N/A | Elasticsearch: 2-4 full-time engineers
Apache Pinot: N/A | Elasticsearch: 300+ community/official
Apache Pinot: N/A | Elasticsearch: Lucene, KQL, SQL, JavaScript
Apache Pinot: N/A | Elasticsearch: $0.50-$1.50
Apache Pinot: N/A | Elasticsearch: Varies by deployment (self-hosted: customer responsibility)
Apache Pinot: N/A | Elasticsearch: Yes (SSPL/Elastic License)
How to choose between Apache Pinot and Elasticsearch
Pinot vs Elasticsearch — Structured Comparison Data
Machine-readable comparison dataset for Pinot vs Elasticsearch. Contains 169 attribute comparisons, 5 FAQ pairs.
Pinot vs Elasticsearch — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Pinot vs Elasticsearch.
When should I use Pinot instead of Elasticsearch?
Use Pinot when you have numerical, time-series data at scale (100B+ events/day) and need sub-second latency on aggregation queries. Elasticsearch is more general-purpose and simpler to operate, but Pinot's columnar design makes it 5-10x faster and more storage-efficient for pure analytics. If you primarily do full-text search or logging, stick with Elasticsearch.
Can Elasticsearch do real-time analytics like Pinot?
Elasticsearch can perform analytics, but it's not optimized for it. Query latency on billion-row datasets typically ranges 500-2000ms vs. Pinot's 50-500ms. Elasticsearch's strength is real-time log indexing and text search; if you need fast numerical aggregations at massive scale, Pinot is superior. For hybrid workloads, some teams use both.
What are the cost implications of choosing between them?
Pinot's 0.1-0.3x compression ratio means 5-10x lower storage costs at petabyte scale. However, Elasticsearch has lower operational complexity and requires fewer expert DevOps resources. At 10 PB scale, Pinot can save $500K-$1M annually in storage; at 100 GB scale, Elasticsearch is cheaper due to its simplicity and smaller cluster requirements.
Can I migrate from Elasticsearch to Pinot?
Direct migration is not straightforward since they serve different use cases. Elasticsearch data (inverted indices) maps poorly to Pinot's columnar format. Most teams that switch run both systems in parallel: Elasticsearch for logs/search, Pinot for analytics. If considering a switch, build a new Pinot cluster in parallel and backfill from your data source (Kafka, S3) rather than attempting data port.
Which has better community support and documentation?
Elasticsearch has a significantly larger community (68K vs 9.2K GitHub stars), more third-party integrations, and broader vendor support. Pinot's documentation is comprehensive but smaller community means fewer Stack Overflow answers. For enterprise support, Elastic offers commercial licenses; Pinot is backed by Apache and community support through LinkedIn/DoorDash (major users).
How to Choose Between Apache Pinot and Elasticsearch
Primary Use Case: Apache Pinot has Real-time OLAP analytics on numerical data, while Elasticsearch has Full-text search and log/event analytics.
Query Latency (typical): Apache Pinot has 50-500ms for billion-row queries, while Elasticsearch has 100-2000ms depending on query complexity.
Storage Model: Apache Pinot has Columnar (optimized for aggregations), while Elasticsearch has Inverted index (optimized for text search).
Full-Text Search Capability: Apache Pinot has Limited/not primary feature, while Elasticsearch has Native, highly optimized.
Typical Cluster Size: Apache Pinot has 10-100+ nodes for petabyte scale, while Elasticsearch has 3-50+ nodes depending on volume.
Index Size vs Data Size Ratio: Apache Pinot has 0.1-0.3x (highly compressed), while Elasticsearch has 0.5-2x (larger indices).
Consider: Community Adoption (GitHub Stars)
Community Adoption (GitHub Stars): Apache Pinot has 9,200+ stars, while Elasticsearch has 68,000+ stars.