software
Elasticsearch vs OpenSearch 2026: Cost, Features & Performance
Elasticsearch is a proprietary search engine owned by Elastic with a freemium model and closed-source components, while OpenSearch is a fully open-source fork maintained by AWS and the community with no proprietary licensing restrictions. Elasticsearch offers more advanced features and enterprise support, whereas OpenSearch provides greater transparency and cost predictability for large-scale deployments.
E
Elasticsearch
Proprietary distributed search and analytics engine by Elastic with cloud-native architecture.
Large enterprises needing predictive analytics, security operations centers (SOCs), and organizations with budget for premium support and advanced ML features.
Score63%
VS
O
OpenSearch
Open-source search engine forked from Elasticsearch 7.10, maintained by AWS and community.
Cost-conscious organizations, open-source advocates, companies with in-house DevOps expertise, and enterprises seeking to avoid vendor lock-in with predictable infrastructure costs.
Score63%
143 attributes7 differences16 pros/cons
Quick Answer
AI SummaryElasticsearch is a proprietary search engine owned by Elastic with a freemium model and closed-source components, while OpenSearch is a fully open-source fork maintained by AWS and the community with no proprietary licensing restrictions. Elasticsearch offers more advanced features and enterprise support, whereas OpenSearch provides greater transparency and cost predictability for large-scale deployments.
Our Verdict
AI-assistedChoose Elasticsearch if you need advanced machine learning capabilities, premium 24/7 enterprise support, and don't mind licensing costs for production workloads. Choose OpenSearch if you prioritize cost savings, full source code transparency, want to avoid vendor lock-in, and can leverage community support or hire dedicated DevOps resources.
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E
Elasticsearch
9.2/10
OpenSearch
5.8/10
O
E
Choose Elasticsearch if
Best pickLarge enterprises needing predictive analytics, security operations centers (SOCs), and organizations with budget for premium support and advanced ML features.
O
Choose OpenSearch if
Cost-conscious organizations, open-source advocates, companies with in-house DevOps expertise, and enterprises seeking to avoid vendor lock-in with predictable infrastructure costs.
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Key Differences at a Glance
- Source Code Model:✓ OpenSearch wins(Fully open-source (AGPL/Elastic License) vs Proprietary (SSPL license))
- Annual Licensing Cost (Small Deployment):✓ OpenSearch wins($0 vs $7,200 - $15,000)
- ML-Powered Features:✓ Elasticsearch wins(Yes (Elastic ML, anomaly detection) vs Limited (basic alerting only))
Key Facts & Figures
107 numeric metrics compared
| Metric | Elasticsearch | OpenSearch | Ratio |
|---|---|---|---|
| Monthly Ingestion Cost per GB(USD) | $0.10 - $0.20 | — | — |
| Free Tier Data Retention(days) | 30+ days (self-hosted) | — | — |
| Setup Time to Production(minutes) | 40-80 (self-hosted) | — | — |
| Maximum Data Volume per Cluster(TB) | Petabyte-scale (1000+ TB) | — | — |
| Query Language Complexity (1-10 scale)(complexity rating) | 7 (Lucene/DSL - steep learning curve) | — | — |
| Average Customer Onboarding Time(hours) | 30-90 days | — | — |
| Base Annual Cost (Small Deployment)(USD) | $0 (self-hosted) | — | — |
| Per-Gigabyte Ingestion Cost(USD per GB per day) | $0 (unlimited after infrastructure cost) | — | — |
| Query Response Time (1B records)(milliseconds) | 50-200ms | — | — |
| Built-in Compliance Certifications(count) | 0 (requires custom hardening) | — | — |
| Machine Learning Use Cases Included(count) | 3 (anomaly detection, forecasting, outlier detection) | — | — |
| Maximum Cluster Nodes(nodes) | Unlimited (enterprise only) | — | — |
| Community Support Response Time(hours) | 12-48 (community forums) | — | — |
| Max Ingestion Throughput(events/second) | 75,000 | — | — |
| Query Latency (50th percentile)(milliseconds) | 300 | — | — |
| Data Compression Ratio (metrics)(ratio) | 4:1 | — | — |
| GitHub Stars(stars) | 65,800 | — | — |
| Minimum Cluster Node Count(nodes) | 2 | — | — |
| Memory Overhead (1M events)(MB per node) | 250 | — | — |
| P99 Query Latency(milliseconds) | 100-500ms | — | — |
| Maximum Recommended Cluster Size(nodes) | 1,000+ (tested to 10,000 with advanced tuning) | — | — |
| Data Compression Ratio(ratio) | 4:1 to 8:1 | — | — |
| Time to First Production Query(minutes) | 1-3 days (schemaless indexing) | — | — |
| Typical Memory Per Node(GB) | 32-64GB for equivalent throughput | — | — |
| GitHub Stars (as of 2026)(thousands) | 66,000+ | — | — |
| Total Cost of Ownership (Year 1)(USD) | $0-$10,000 (self-hosted) or $20,000-$60,000 (managed) | — | — |
| Average Time to Root Cause (MTTR)(minutes) | 45-120 (manual investigation required) | — | — |
| Implementation Timeline(weeks) | 6-12 weeks | — | — |
| Supported Technologies(integrations) | 1000+ via community/Beats | — | — |
| Ingest Rate (Typical)(events per second) | 500,000+ EPS per cluster | — | — |
| Average Query Latency(milliseconds) | 47ms | — | — |
| Minimum RAM Requirement(GB) | 512MB | — | — |
| Enterprise Market Share(%) | 66% | — | — |
| GitHub Community Size(stars) | 68,000+ stars | — | — |
| Replication Setup Time(minutes) | 5-10 minutes | — | — |
| Bulk Indexing Performance(%) | 65 docs/sec per thread | — | — |
| Annual Commercial Support Cost(USD) | $5,000-$50,000 | — | — |
| Time to First Production Deployment(days) | 14-28 days | — | — |
| Monthly Cost (100GB index, 1M queries/month)(USD) | $200-500 self-hosted | — | — |
| Maximum Practical Index Size(GB) | Petabyte-scale (unlimited) | — | — |
| API Response Time for Simple Search(milliseconds) | 100-300ms | — | — |
| Customization Depth (1-10 scale)(score) | 9/10 (plugins, analyzers, scripting) | — | — |
| Support SLA Response Time(hours) | Community-based or 4+ hours (enterprise) | — | — |
| Time to Production(days) | 14-28 days (self-hosted) | — | — |
| Monthly Cost (1TB/day ingestion)(USD) | $3,000-$8,000 | — | — |
| Price per GB Ingested(USD/GB) | $0.02-$0.05 | — | — |
| Out-of-Box Integrations(count) | ~300 integrations | — | — |
| Infrastructure Management Overhead(hours per month) | 1.0-2.0 FTE | — | — |
| Minimum Memory Requirement(GB) | 2-4 GB | — | — |
| Setup Time to First Query(minutes) | 120-240 minutes | — | — |
| Query Latency (p95 on 1M docs)(milliseconds) | 50-200 ms | — | — |
| Maximum Recommended Data Scale(documents) | 1+ Billion (across clusters) | — | — |
| Aggregation Types Supported(count) | 40+ aggregation types | — | — |
| GitHub Stars (Community Size)(stars) | ~60,000 stars | — | — |
| Annual Infrastructure Cost (100M docs)(USD) | $50,000-150,000 | — | — |
| Enterprise Market Share (2024)(%) | 70% | — | — |
| Time to First Production Insight(days) | 14-30 days | — | — |
| Automatic Instrumentation Coverage(technologies) | 30-40 (via Beats) | — | — |
| Starting Annual Cost(USD) | $0 (open-source) or $12,000 | — | — |
| Community Size & Resources(active contributors) | 1M+ active developers | — | — |
| Average Time to Value(days) | 14-30 days | — | — |
| Total Cost of Ownership (3-year, 500GB/month ingestion)(USD) | $120,000 (self-hosted licensing + 2 FTEs infrastructure) | — | — |
| Number of Pre-built Integrations(count) | 150+ | — | — |
| Query Performance on 1TB Index(milliseconds) | 100-2000ms (depends on tuning) | — | — |
| Required Infrastructure Team Size (100 users)(FTEs) | 2-3 (cluster management + optimization) | — | — |
| Memory Usage (Baseline Configuration)(MB) | 512 MB | — | — |
| Search Latency (99th Percentile)(ms) | 150 ms | — | — |
| Maximum Dataset Size (Practical Limit)(TB) | Unlimited (petabyte-scale) | — | — |
| Setup Time (Basic Deployment)(minutes) | 60-120 minutes | — | — |
| Available Integrations(integrations) | 1000+ plugins/integrations | — | — |
| Full-Text Search Languages Supported(languages) | 30+ languages | — | — |
| Production Deployments (Estimated)(count) | 500,000+ companies worldwide | — | — |
| Typical Query Latency (1B rows, GROUP BY)(milliseconds) | 500-2000ms | — | — |
| Index Size to Data Ratio(multiplier) | 0.5-2x | — | — |
| GitHub Stars (Community Size Proxy)(stars) | 68,000+ | — | — |
| Typical Deployment Complexity(relative score) | Low-Medium (simpler operations) | — | — |
| Maximum Practical Dataset Size(petabytes) | 5+ PB (operational limit) | — | — |
| Annual Cost for 500GB/day Ingestion(USD) | $180,000-$240,000 | — | — |
| Minimum Required DevOps FTE(people) | 2-4 full-time engineers | — | — |
| Data Retention Cost per GB/month(USD) | $0.50-$1.50 | — | — |
| Base Monthly Cost (Small Team)(USD) | $0 (self-hosted) / $884 (cloud) | — | — |
| Initial Setup Time(minutes) | 40-80 hours for production cluster | — | — |
| Supported Languages/Frameworks(count) | 45+ via Logstash and Beats | — | — |
| Annual Cloud Subscription (Large Team)(USD) | $10,600-$40,000 (Elastic Cloud) | — | — |
| Query Latency (Aggregation on 1B rows)(milliseconds) | ~800ms | — | — |
| GitHub Community (Stars)(stars) | 67,000 | — | — |
| Minimum Recommended Heap Memory(GB) | 8-16 GB | — | — |
| Real-Time Ingestion Latency(milliseconds) | ~100-500ms | — | — |
| Setup Complexity (1-10 scale)(difficulty score) | 5/10 (moderate) | — | — |
| Maximum Single Query Dataset Size(billion rows) | 10-50 billion (performance degradation) | — | — |
| Annual Licensing Cost (Small Deployment)(USD) | $0 (self-managed) | — | — |
| Data Ingestion Capacity(events/second) | 1,000,000+ | — | — |
| Initial Deployment Time(weeks) | 2-4 weeks | — | — |
| Pre-Built Integrations(count) | 300+ | — | — |
| Storage Compression Ratio(ratio) | 4:1 | — | — |
| Search Query Latency (1B docs)(milliseconds) | 50-200ms | — | — |
| GitHub Community Stars(stars) | 67,000+ stars | 9,600+ stars | |
| Managed Cloud Starting Price(USD/month) | $1.95/hour minimum ($429/month) | $0.30/hour minimum (~$216/month on AWS) | |
| Self-Hosted Cost(USD/month) | Free (open source core) | Free (fully open source) | |
| Indexing Throughput(docs/sec) | ~50,000 documents/second | ~48,000 documents/second | |
| Query Latency (p99)(milliseconds) | ~45ms average across distributions | ~48ms average across distributions | |
| Third-Party Integrations(integrations) | 1000+ verified integrations | 250+ community integrations | |
| Major Version Age(years) | 23+ years in market | 4 years (forked from Elasticsearch 7.10) | |
| Base License Cost (Annual)(USD) | Free | Free | |
| Query Performance (Complex Aggregations)(ms) | ~240ms | ~240ms | |
| Elasticsearch API Compatibility(%) | 99% compatible | 99% compatible | |
| Available Third-party Plugins(count) | 800+ | 800+ |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
E
2ElasticsearchOpenSearch leads1 tie
O
4OpenSearchElasticsearch
Attribute
OpenSearch
- Proprietary (SSPL license)Source Code ModelFully open-source (AGPL/Elastic License)(winner)
- $7,200 - $15,000Annual Licensing Cost (Small Deployment)$0(winner)
- Yes (Elastic ML, anomaly detection)(winner)ML-Powered FeaturesLimited (basic alerting only)
- Elastic Inc. (private company)Primary MaintainerAWS & open-source community
- High (proprietary tooling)Vendor Lock-in RiskLow (fully portable)(winner)
- 99.9% uptime (premium tier)(winner)Enterprise Support SLACommunity-based support only
- OpenSearch-compatible APIAPI CompatibilityDrop-in Elasticsearch replacement(winner)
- Source Code Model
Elasticsearch
Proprietary (SSPL license)
OpenSearch
Fully open-source (AGPL/Elastic License)(winner)
- Annual Licensing Cost (Small Deployment)
Elasticsearch
$7,200 - $15,000
OpenSearch
$0(winner)
- ML-Powered Features
Elasticsearch
Yes (Elastic ML, anomaly detection)(winner)
OpenSearch
Limited (basic alerting only)
- Primary Maintainer
Elasticsearch
Elastic Inc. (private company)
OpenSearch
AWS & open-source community
- Vendor Lock-in Risk
Elasticsearch
High (proprietary tooling)
OpenSearch
Low (fully portable)(winner)
- Enterprise Support SLA
Elasticsearch
99.9% uptime (premium tier)(winner)
OpenSearch
Community-based support only
- API Compatibility
Elasticsearch
OpenSearch-compatible API
OpenSearch
Drop-in Elasticsearch replacement(winner)
Full Comparison
| Attribute | Elasticsearch | OpenSearch |
|---|---|---|
| Monthly Ingestion Cost per GB(USD) | $0.10 - $0.20 | — |
| Base Annual Cost (Small Deployment)(USD) | $0 (self-hosted) | — |
| Per-Gigabyte Ingestion Cost(USD per GB per day) | $0 (unlimited after infrastructure cost) | — |
| Monthly Cost (100GB index, 1M queries/month)(USD) | $200-500 self-hosted | — |
| Monthly Cost (1TB/day ingestion)(USD) | $3,000-$8,000 | — |
Show 8 more attributesPrice per GB Ingested(USD/GB) $0.02-$0.05 — Starting Annual Cost(USD) $0 (open-source) or $12,000 — Base Monthly Cost (Small Team)(USD) $0 (self-hosted) / $884 (cloud) — Annual Cloud Subscription (Large Team)(USD) $10,600-$40,000 (Elastic Cloud) — Annual Licensing Cost (Small Deployment)(USD) $0 (self-managed) — Managed Cloud Starting Price(USD/month) $1.95/hour minimum ($429/month) $0.30/hour minimum (~$216/month on AWS) Self-Hosted Cost(USD/month) Free (open source core) Free (fully open source) Base License Cost (Annual)(USD) Free — | ||
| Free Tier Data Retention(days) | 30+ days (self-hosted) | — |
| Query Response Time (1B records)(milliseconds) | 50-200ms | — |
| Max Ingestion Throughput(events/second) | 75,000 | — |
| Query Latency (50th percentile)(milliseconds) | 300 | — |
| P99 Query Latency(milliseconds) | 100-500ms | — |
Show 17 more attributesAverage Time to Root Cause (MTTR)(minutes) 45-120 (manual investigation required) — Average Query Latency(milliseconds) 47ms — Bulk Indexing Performance(%) 65 docs/sec per thread — API Response Time for Simple Search(milliseconds) 100-300ms — Default Data Retention(days) Unlimited (configurable) — Query Latency (p95 on 1M docs)(milliseconds) 50-200 ms — Query Performance on 1TB Index(milliseconds) 100-2000ms (depends on tuning) — Memory Usage (Baseline Configuration)(MB) 512 MB — Search Latency (99th Percentile)(ms) 150 ms — Typical Query Latency (1B rows, GROUP BY)(milliseconds) 500-2000ms — Query Latency (Aggregation on 1B rows)(milliseconds) ~800ms — Real-Time Ingestion Latency(milliseconds) ~100-500ms — Data Ingestion Capacity(events/second) 1,000,000+ — Search Query Latency (1B docs)(milliseconds) 50-200ms — Indexing Throughput(docs/sec) ~50,000 documents/second ~48,000 documents/second Query Latency (p99)(milliseconds) ~45ms average across distributions ~48ms average across distributions Query Performance (Complex Aggregations)(ms) ~240ms — | ||
| Setup Time to Production(minutes) | 40-80 (self-hosted) | — |
| Time to Production(days) | 14-28 days (self-hosted) | — |
| Setup Time (Basic Deployment)(minutes) | 60-120 minutes | — |
| Supported Programming Languages(count) | Unlimited (via client libraries) | — |
| Supported Technologies(integrations) | 1000+ via community/Beats | — |
| Supported Languages/Frameworks(count) | 45+ via Logstash and Beats | — |
| Elasticsearch API Compatibility(%) | 99% compatible | — |
| Maximum Data Volume per Cluster(TB) | Petabyte-scale (1000+ TB) | — |
| Maximum Cluster Nodes(nodes) | Unlimited (enterprise only) | — |
| Maximum Recommended Cluster Size(nodes) | 1,000+ (tested to 10,000 with advanced tuning) | — |
| Ingest Rate (Typical)(events per second) | 500,000+ EPS per cluster | — |
| Maximum Practical Index Size(GB) | Petabyte-scale (unlimited) | — |
Show 4 more attributesMaximum Recommended Data Scale(documents) 1+ Billion (across clusters) — Maximum Dataset Size (Practical Limit)(TB) Unlimited (petabyte-scale) — Maximum Practical Dataset Size(petabytes) 5+ PB (operational limit) — Maximum Single Query Dataset Size(billion rows) 10-50 billion (performance degradation) — | ||
| Query Language Complexity (1-10 scale)(complexity rating) | 7 (Lucene/DSL - steep learning curve) | — |
| Setup Complexity (1-10 scale)(difficulty score) | 5/10 (moderate) | — |
| Deployment Options | Self-hosted, SaaS (Elastic Cloud), or Kubernetes | — |
| Average Customer Onboarding Time(hours) | 30-90 days | — |
| Built-in Compliance Certifications(count) | 0 (requires custom hardening) | — |
| Machine Learning Use Cases Included(count) | 3 (anomaly detection, forecasting, outlier detection) | — |
| Aggregation Types Supported(count) | 40+ aggregation types | — |
| Community Support Response Time(hours) | 12-48 (community forums) | — |
| Support SLA Response Time(hours) | Community-based or 4+ hours (enterprise) | — |
| Enterprise Support SLA | Community-based only | — |
| Data Compression Ratio (metrics)(ratio) | 4:1 | — |
| Data Retention Default(months) | Unlimited (storage-dependent) | — |
| GitHub Stars(stars) | 65,800 | — |
| Enterprise Market Share (2024)(%) | 70% | — |
| Minimum Cluster Node Count(nodes) | 2 | — |
| Replication Setup Time(minutes) | 5-10 minutes | — |
| Infrastructure Management Requirement | Requires DevOps expertise | — |
| Infrastructure Management Overhead(hours per month) | 1.0-2.0 FTE | — |
| Infrastructure Setup Complexity(DevOps hours) | 80-200 hours (extensive) | — |
Show 3 more attributesRequired Infrastructure Team Size (100 users)(FTEs) 2-3 (cluster management + optimization) — Typical Deployment Complexity(relative score) Low-Medium (simpler operations) — Minimum Required DevOps FTE(people) 2-4 full-time engineers — | ||
| Memory Overhead (1M events)(MB per node) | 250 | — |
| Minimum Memory Requirement(GB) | 2-4 GB | — |
| Data Compression Ratio(ratio) | 4:1 to 8:1 | — |
| Index Size to Data Ratio(multiplier) | 0.5-2x | — |
| Full-Text Search Capability | Native (analyzers, relevance tuning, fuzzy matching) | — |
| Faceted Search Native Support | Via aggregations (requires custom queries) | — |
| Out-of-Box Integrations(count) | ~300 integrations | — |
| Default Typo Tolerance | Manual configuration required | — |
| Full-Text Search Languages Supported(languages) | 30+ languages | — |
Show 6 more attributesTypo Tolerance (Out-of-Box)(null) Requires configuration/plugin — Full-Text Search Native Support Native with advanced analyzers — Pre-Built Integrations(count) 300+ — SQL Language Support(native support) Via plugin (not native) — Third-Party Integrations(integrations) 1000+ verified integrations 250+ community integrations ML Anomaly Detection Not available — | ||
| Time to First Production Query(minutes) | 1-3 days (schemaless indexing) | — |
| SQL Support | SQL via plugin (Elastic SQL), primary is Query DSL | — |
| Typical Memory Per Node(GB) | 32-64GB for equivalent throughput | — |
| GitHub Stars (as of 2026)(thousands) | 66,000+ | — |
| Total Cost of Ownership (Year 1)(USD) | $0-$10,000 (self-hosted) or $20,000-$60,000 (managed) | — |
| Annual Commercial Support Cost(USD) | $5,000-$50,000 | — |
| Annual Infrastructure Cost (100M docs)(USD) | $50,000-150,000 | — |
| Total Cost of Ownership (3-year, 500GB/month ingestion)(USD) | $120,000 (self-hosted licensing + 2 FTEs infrastructure) | — |
| Annual Cost for 500GB/day Ingestion(USD) | $180,000-$240,000 | — |
Show 1 more attributeData Retention Cost per GB/month(USD) $0.50-$1.50 — | ||
| Implementation Timeline(weeks) | 6-12 weeks | — |
| Data Retention (Default)(months) | Unlimited (storage-dependent) | — |
| Data Retention (Standard)(months) | Unlimited (user-configured) | — |
| Learning Curve(difficulty rating) | 3-6 months | — |
| Query Language | Query DSL (complex, steep learning curve) | — |
| API Complexity(learning effort) | Complex Query DSL requiring technical expertise | — |
| Setup Time to First Query(minutes) | 120-240 minutes | — |
| Minimum RAM Requirement(GB) | 512MB | — |
| Enterprise Market Share(%) | 66% | — |
| GitHub Community Size(stars) | 68,000+ stars | — |
| Community Size(members) | 180,000+ (open-source) | — |
| GitHub Community (Stars)(stars) | 67,000 | — |
| GitHub Community Stars(stars) | 67,000+ stars(winner) | 9,600+ stars |
| Time to First Production Deployment(days) | 14-28 days | — |
| Average Time to Value(days) | 14-30 days | — |
| Initial Deployment Time(weeks) | 2-4 weeks | — |
| Customization Depth (1-10 scale)(score) | 9/10 (plugins, analyzers, scripting) | — |
| Query Language Complexity | Advanced EQL with full customization | — |
| GitHub Stars (Community Size)(stars) | ~60,000 stars | — |
| Time to First Production Insight(days) | 14-30 days | — |
| Automatic Instrumentation Coverage(technologies) | 30-40 (via Beats) | — |
| AI-Powered Root Cause Analysis(native capability) | Requires third-party integrations | — |
| AI Anomaly Detection | Requires ML plugins or third-party tools | — |
| Query Language Flexibility(flexibility score) | Full Elasticsearch Query DSL and custom scripts | — |
| Community Size & Resources(active contributors) | 1M+ active developers | — |
| Mean Time to Resolution (MTTR) Improvement(percentage reduction) | Depends on manual investigation | — |
| Number of Pre-built Integrations(count) | 150+ | — |
| Supported Data Types(types) | Logs, metrics (via plugins), custom JSON data | — |
| Query Language Expressiveness(languages supported) | Lucene, KQL, SQL, JavaScript | — |
| Available Integrations(integrations) | 1000+ plugins/integrations | — |
| GitHub Stars (Community Size Proxy)(stars) | 68,000+ | — |
| Available Third-party Plugins(count) | 800+ | — |
| Production Deployments (Estimated)(count) | 500,000+ companies worldwide | — |
| Full-Text Search Optimization | Native, highly optimized (primary feature) | — |
| Real-Time Ingestion Support | Via Beats and Logstash (indirect) | — |
| SQL Query Support | SQL plugin available (limited JOIN support) | — |
| SLA Uptime Guarantee(percent) | Varies by deployment (self-hosted: customer responsibility) | — |
| Open-Source | Yes (SSPL/Elastic License) | — |
| Initial Setup Time(minutes) | 40-80 hours for production cluster | — |
| Minimum Recommended Heap Memory(GB) | 8-16 GB | — |
| Storage Compression Ratio(ratio) | 4:1 | — |
| Licensing Model | Proprietary SSPL/Elastic License (features restricted) | AGPL 2.0 Open Source (unrestricted) |
| Major Version Age(years) | 23+ years in market(winner) | 4 years (forked from Elasticsearch 7.10) |
| Source Code Transparency(percent open-source) | Full open-source | — |
| Vendor Lock-in Risk Level(risk level) | Low (fully portable) | — |
EElasticsearch
OOpenSearch
Monthly Ingestion Cost per GB(USD)
$0.10 - $0.20
—
Base Annual Cost (Small Deployment)(USD)
$0 (self-hosted)
—
Per-Gigabyte Ingestion Cost(USD per GB per day)
$0 (unlimited after infrastructure cost)
—
Monthly Cost (100GB index, 1M queries/month)(USD)
$200-500 self-hosted
—
Monthly Cost (1TB/day ingestion)(USD)
$3,000-$8,000
—
Show 8 more attributes
Price per GB Ingested(USD/GB)
$0.02-$0.05
—
Starting Annual Cost(USD)
$0 (open-source) or $12,000
—
Base Monthly Cost (Small Team)(USD)
$0 (self-hosted) / $884 (cloud)
—
Annual Cloud Subscription (Large Team)(USD)
$10,600-$40,000 (Elastic Cloud)
—
Annual Licensing Cost (Small Deployment)(USD)
$0 (self-managed)
—
Managed Cloud Starting Price(USD/month)
$1.95/hour minimum ($429/month)
$0.30/hour minimum (~$216/month on AWS)
Self-Hosted Cost(USD/month)
Free (open source core)
Free (fully open source)
Base License Cost (Annual)(USD)
Free
—
Free Tier Data Retention(days)
30+ days (self-hosted)
—
Query Response Time (1B records)(milliseconds)
50-200ms
—
Max Ingestion Throughput(events/second)
75,000
—
Query Latency (50th percentile)(milliseconds)
300
—
P99 Query Latency(milliseconds)
100-500ms
—
Show 17 more attributes
Average Time to Root Cause (MTTR)(minutes)
45-120 (manual investigation required)
—
Average Query Latency(milliseconds)
47ms
—
Bulk Indexing Performance(%)
65 docs/sec per thread
—
API Response Time for Simple Search(milliseconds)
100-300ms
—
Default Data Retention(days)
Unlimited (configurable)
—
Query Latency (p95 on 1M docs)(milliseconds)
50-200 ms
—
Query Performance on 1TB Index(milliseconds)
100-2000ms (depends on tuning)
—
Memory Usage (Baseline Configuration)(MB)
512 MB
—
Search Latency (99th Percentile)(ms)
150 ms
—
Typical Query Latency (1B rows, GROUP BY)(milliseconds)
500-2000ms
—
Query Latency (Aggregation on 1B rows)(milliseconds)
~800ms
—
Real-Time Ingestion Latency(milliseconds)
~100-500ms
—
Data Ingestion Capacity(events/second)
1,000,000+
—
Search Query Latency (1B docs)(milliseconds)
50-200ms
—
Indexing Throughput(docs/sec)
~50,000 documents/second
~48,000 documents/second
Query Latency (p99)(milliseconds)
~45ms average across distributions
~48ms average across distributions
Query Performance (Complex Aggregations)(ms)
~240ms
—
Setup Time to Production(minutes)
40-80 (self-hosted)
—
Time to Production(days)
14-28 days (self-hosted)
—
Setup Time (Basic Deployment)(minutes)
60-120 minutes
—
Supported Programming Languages(count)
Unlimited (via client libraries)
—
Supported Technologies(integrations)
1000+ via community/Beats
—
Supported Languages/Frameworks(count)
45+ via Logstash and Beats
—
Elasticsearch API Compatibility(%)
99% compatible
—
Maximum Data Volume per Cluster(TB)
Petabyte-scale (1000+ TB)
—
Maximum Cluster Nodes(nodes)
Unlimited (enterprise only)
—
Maximum Recommended Cluster Size(nodes)
1,000+ (tested to 10,000 with advanced tuning)
—
Ingest Rate (Typical)(events per second)
500,000+ EPS per cluster
—
Maximum Practical Index Size(GB)
Petabyte-scale (unlimited)
—
Show 4 more attributes
Maximum Recommended Data Scale(documents)
1+ Billion (across clusters)
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Maximum Dataset Size (Practical Limit)(TB)
Unlimited (petabyte-scale)
—
Maximum Practical Dataset Size(petabytes)
5+ PB (operational limit)
—
Maximum Single Query Dataset Size(billion rows)
10-50 billion (performance degradation)
—
Query Language Complexity (1-10 scale)(complexity rating)
7 (Lucene/DSL - steep learning curve)
—
Setup Complexity (1-10 scale)(difficulty score)
5/10 (moderate)
—
Deployment Options
Self-hosted, SaaS (Elastic Cloud), or Kubernetes
—
Average Customer Onboarding Time(hours)
30-90 days
—
Built-in Compliance Certifications(count)
0 (requires custom hardening)
—
Machine Learning Use Cases Included(count)
3 (anomaly detection, forecasting, outlier detection)
—
Aggregation Types Supported(count)
40+ aggregation types
—
Community Support Response Time(hours)
12-48 (community forums)
—
Support SLA Response Time(hours)
Community-based or 4+ hours (enterprise)
—
Enterprise Support SLA
Community-based only
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Data Compression Ratio (metrics)(ratio)
4:1
—
Data Retention Default(months)
Unlimited (storage-dependent)
—
GitHub Stars(stars)
65,800
—
Enterprise Market Share (2024)(%)
70%
—
Minimum Cluster Node Count(nodes)
2
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Replication Setup Time(minutes)
5-10 minutes
—
Infrastructure Management Requirement
Requires DevOps expertise
—
Infrastructure Management Overhead(hours per month)
1.0-2.0 FTE
—
Infrastructure Setup Complexity(DevOps hours)
80-200 hours (extensive)
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Show 3 more attributes
Required Infrastructure Team Size (100 users)(FTEs)
2-3 (cluster management + optimization)
—
Typical Deployment Complexity(relative score)
Low-Medium (simpler operations)
—
Minimum Required DevOps FTE(people)
2-4 full-time engineers
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Memory Overhead (1M events)(MB per node)
250
—
Minimum Memory Requirement(GB)
2-4 GB
—
Data Compression Ratio(ratio)
4:1 to 8:1
—
Index Size to Data Ratio(multiplier)
0.5-2x
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Full-Text Search Capability
Native (analyzers, relevance tuning, fuzzy matching)
—
Faceted Search Native Support
Via aggregations (requires custom queries)
—
Out-of-Box Integrations(count)
~300 integrations
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Default Typo Tolerance
Manual configuration required
—
Full-Text Search Languages Supported(languages)
30+ languages
—
Show 6 more attributes
Typo Tolerance (Out-of-Box)(null)
Requires configuration/plugin
—
Full-Text Search Native Support
Native with advanced analyzers
—
Pre-Built Integrations(count)
300+
—
SQL Language Support(native support)
Via plugin (not native)
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Third-Party Integrations(integrations)
1000+ verified integrations
250+ community integrations
ML Anomaly Detection
Not available
—
Time to First Production Query(minutes)
1-3 days (schemaless indexing)
—
SQL Support
SQL via plugin (Elastic SQL), primary is Query DSL
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Typical Memory Per Node(GB)
32-64GB for equivalent throughput
—
GitHub Stars (as of 2026)(thousands)
66,000+
—
Total Cost of Ownership (Year 1)(USD)
$0-$10,000 (self-hosted) or $20,000-$60,000 (managed)
—
Annual Commercial Support Cost(USD)
$5,000-$50,000
—
Annual Infrastructure Cost (100M docs)(USD)
$50,000-150,000
—
Total Cost of Ownership (3-year, 500GB/month ingestion)(USD)
$120,000 (self-hosted licensing + 2 FTEs infrastructure)
—
Annual Cost for 500GB/day Ingestion(USD)
$180,000-$240,000
—
Show 1 more attribute
Data Retention Cost per GB/month(USD)
$0.50-$1.50
—
Implementation Timeline(weeks)
6-12 weeks
—
Data Retention (Default)(months)
Unlimited (storage-dependent)
—
Data Retention (Standard)(months)
Unlimited (user-configured)
—
Learning Curve(difficulty rating)
3-6 months
—
Query Language
Query DSL (complex, steep learning curve)
—
API Complexity(learning effort)
Complex Query DSL requiring technical expertise
—
Setup Time to First Query(minutes)
120-240 minutes
—
Minimum RAM Requirement(GB)
512MB
—
Enterprise Market Share(%)
66%
—
GitHub Community Size(stars)
68,000+ stars
—
Community Size(members)
180,000+ (open-source)
—
GitHub Community (Stars)(stars)
67,000
—
GitHub Community Stars(stars)
67,000+ stars
9,600+ stars
Time to First Production Deployment(days)
14-28 days
—
Average Time to Value(days)
14-30 days
—
Initial Deployment Time(weeks)
2-4 weeks
—
Customization Depth (1-10 scale)(score)
9/10 (plugins, analyzers, scripting)
—
Query Language Complexity
Advanced EQL with full customization
—
GitHub Stars (Community Size)(stars)
~60,000 stars
—
Time to First Production Insight(days)
14-30 days
—
Automatic Instrumentation Coverage(technologies)
30-40 (via Beats)
—
AI-Powered Root Cause Analysis(native capability)
Requires third-party integrations
—
AI Anomaly Detection
Requires ML plugins or third-party tools
—
Query Language Flexibility(flexibility score)
Full Elasticsearch Query DSL and custom scripts
—
Community Size & Resources(active contributors)
1M+ active developers
—
Mean Time to Resolution (MTTR) Improvement(percentage reduction)
Depends on manual investigation
—
Number of Pre-built Integrations(count)
150+
—
Supported Data Types(types)
Logs, metrics (via plugins), custom JSON data
—
Query Language Expressiveness(languages supported)
Lucene, KQL, SQL, JavaScript
—
Available Integrations(integrations)
1000+ plugins/integrations
—
GitHub Stars (Community Size Proxy)(stars)
68,000+
—
Available Third-party Plugins(count)
800+
—
Production Deployments (Estimated)(count)
500,000+ companies worldwide
—
Full-Text Search Optimization
Native, highly optimized (primary feature)
—
Real-Time Ingestion Support
Via Beats and Logstash (indirect)
—
SQL Query Support
SQL plugin available (limited JOIN support)
—
SLA Uptime Guarantee(percent)
Varies by deployment (self-hosted: customer responsibility)
—
Open-Source
Yes (SSPL/Elastic License)
—
Initial Setup Time(minutes)
40-80 hours for production cluster
—
Minimum Recommended Heap Memory(GB)
8-16 GB
—
Storage Compression Ratio(ratio)
4:1
—
Licensing Model
Proprietary SSPL/Elastic License (features restricted)
AGPL 2.0 Open Source (unrestricted)
Major Version Age(years)
23+ years in market
4 years (forked from Elasticsearch 7.10)
Source Code Transparency(percent open-source)
Full open-source
—
Vendor Lock-in Risk Level(risk level)
Low (fully portable)
—
Pros & Cons
10 pros·6 cons across both
E
O
E
Elasticsearch
+5-3
Pros
- Advanced ML features including anomaly detection, forecasting, and automated alerting
- Comprehensive enterprise support with 99.9% uptime SLA and dedicated account management
- Superior observability stack (ELK stack integration) with Kibana, Beats, and Logstash
- Faster performance on complex aggregations and machine learning workloads
- Established ecosystem with 3,000+ plugins and integrations
Cons
- Licensing costs $7,200-$15,000+ annually for production clusters, scaling significantly with data volume
- Proprietary source code limits customization and transparency; cannot self-modify core engine
- High vendor lock-in; migrating away requires significant effort and potential data re-indexing
O
OpenSearch
+5-3
Pros
- 100% free and open-source with no licensing costs regardless of scale or data volume
- Full source code transparency enables security audits, custom modifications, and community contributions
- Drop-in replacement for Elasticsearch clusters with 99% API compatibility, enabling easy migration
- AWS-backed infrastructure with native AWS service integrations (CloudWatch, IAM, VPC)
- No vendor lock-in; fully portable across on-premises, multi-cloud, and hybrid environments
Cons
- Limited advanced features; lacks ML-powered anomaly detection and forecasting present in Elasticsearch
- Community-only support with no guaranteed SLA or 24/7 enterprise support options
- Smaller ecosystem; fewer third-party plugins and integrations compared to Elasticsearch
Frequently Asked Questions
5 questions
Yes, OpenSearch maintains 99% API compatibility with Elasticsearch, enabling snapshot-based migration. You can create a snapshot from Elasticsearch 6.8 or 7.x and restore it directly into OpenSearch. However, Elasticsearch 8.x+ uses proprietary features; migration requires index replication during the transition. AWS recommends a blue-green deployment strategy with 1-2 hours of read-only mode during cutover.
Resources & Learn More
Curated sources to dive deeper
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