software
Elasticsearch vs Splunk 2026: Cost, ML & Compliance
Elasticsearch is a free, open-source search and analytics engine ideal for cost-conscious organizations with technical expertise, while Splunk is a proprietary enterprise platform with advanced machine learning features and dedicated support, commanding premium pricing starting at $3,000+ annually. The choice depends on budget constraints and required feature complexity.
E
Elasticsearch
Proprietary distributed search and analytics engine by Elastic with cloud-native architecture.
DevOps teams, startups, and enterprises with technical depth seeking cost control and customization flexibility.
Score63%
VS
Splunk
Data analytics and SIEM platform for log aggregation, monitoring, and operational intelligence.
Fortune 500 enterprises, regulated industries, and security operations centers prioritizing support, compliance, and AI-powered insights.
Score63%
215 attributes7 differences16 pros/cons
Quick Answer
AI SummaryElasticsearch is a free, open-source search and analytics engine ideal for cost-conscious organizations with technical expertise, while Splunk is a proprietary enterprise platform with advanced machine learning features and dedicated support, commanding premium pricing starting at $3,000+ annually. The choice depends on budget constraints and required feature complexity.
Our Verdict
AI-assistedChoose Elasticsearch if you have in-house technical teams, need cost-effective log aggregation at scale, and can invest time in maintenance and configuration. Choose Splunk if you require advanced machine learning analytics, prefer minimal setup overhead, need guaranteed enterprise support, and have regulatory compliance requirements with built-in certifications.
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E
Elasticsearch
7.2/10
Splunk
7.8/10
E
Choose Elasticsearch if
DevOps teams, startups, and enterprises with technical depth seeking cost control and customization flexibility.
Choose Splunk if
Best pickFortune 500 enterprises, regulated industries, and security operations centers prioritizing support, compliance, and AI-powered insights.
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Key Differences at a Glance
- Licensing Model:✓ Elasticsearch wins(Free and open-source (SSPL for some features) vs Proprietary with subscription pricing)
- Starting Annual Cost:✓ Elasticsearch wins($0 (self-hosted) to $15,000+ (managed) vs $3,000 - $50,000+ per year)
- Machine Learning Capabilities:✓ Splunk wins(Advanced ML Toolkit with predictive analytics vs Basic anomaly detection with Elastic ML)
Key Facts & Figures
164 numeric metrics compared
| Metric | Elasticsearch | Splunk | 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) | 4-8 (managed cloud) | |
| 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) | $3,000 - $5,000 | |
| Per-Gigabyte Ingestion Cost(USD per GB per day) | $0 (unlimited after infrastructure cost) | $0.80 - $1.50 | |
| Query Response Time (1B records)(milliseconds) | 50-200ms | 100-300ms | |
| Built-in Compliance Certifications(count) | 0 (requires custom hardening) | 6 (HIPAA, SOC2, PCI-DSS, FedRAMP, GDPR, ISO27001) | |
| Machine Learning Use Cases Included(count) | 3 (anomaly detection, forecasting, outlier detection) | 15+ (threat detection, predictive analytics, correlation, clustering) | |
| Maximum Cluster Nodes(nodes) | Unlimited (enterprise only) | Unlimited (license-dependent) | |
| Community Support Response Time(hours) | 12-48 (community forums) | 1 (24/7 enterprise SLA) | |
| 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 | 14-42 days | |
| 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 | 700+ integrations | |
| Default Data Retention(days) | Unlimited (configurable) | 30 days included | — |
| 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 | $4,500 | |
| 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+ | 600+ | |
| 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) | $36,000 (minimum SaaS) | |
| Data Ingestion Capacity(events/second) | 1,000,000+ | 10,000 | |
| Initial Deployment Time(weeks) | 2-4 weeks | 1-2 weeks | |
| Pre-Built Integrations(count) | 300+ | 800+ | |
| Storage Compression Ratio(ratio) | 4:1 | 10:1 | |
| Search Query Latency (1B docs)(milliseconds) | 50-200ms | 100-500ms | |
| GitHub Community Stars(stars) | 67,000+ stars | N/A (proprietary) | — |
| Managed Cloud Starting Price(USD/month) | $1.95/hour minimum ($429/month) | — | — |
| Self-Hosted Cost(USD/month) | Free (open source core) | — | — |
| Indexing Throughput(docs/sec) | ~50,000 documents/second | — | — |
| Query Latency (p99)(milliseconds) | ~45ms average across distributions | — | — |
| Third-Party Integrations(integrations) | 1000+ verified integrations | 2,000+ apps | |
| Major Version Age(years) | 23+ years in market | — | — |
| Base Monthly Cost (100GB/day)(USD) | $3,500-$5,500 | $3,500-$5,500 | |
| Annual TCO (1TB/day ingestion)(USD) | $450,000-$750,000 | $450,000-$750,000 | |
| Deployment Time(seconds) | 21-30 days | 21-30 days | |
| Query Performance (5TB dataset)(seconds) | 8-12 seconds | 8-12 seconds | |
| Gartner SIEM Market Share(percent) | 28% (2024) | 28% (2024) | |
| Machine Learning Models(count) | 50+ algorithms | 50+ algorithms | |
| Mean Time to Detection (MTTD)(minutes) | 15-60 minutes (depends on alert configuration) | 15-60 minutes (depends on alert configuration) | |
| Starting Annual Cost (single user/endpoint)(USD) | $3,000-6,000 | $3,000-6,000 | |
| Pre-built Integrations/Apps(count) | 800+ apps in Splunkbase | 800+ apps in Splunkbase | |
| Agent Size/System Footprint(MB) | Heavy (varies, 500MB+) | Heavy (varies, 500MB+) | |
| Average Cost per GB Ingested(USD) | $1.50 | $1.50 | |
| Typical Deployment Timeline(weeks) | 7 weeks | 7 weeks | |
| Default Data Retention Period(months) | 120 months (10 years) | 120 months (10 years) | |
| APM Real-Time Metric Resolution(seconds) | 10-30 seconds | 10-30 seconds | |
| Uptime SLA Guarantee(percent) | 99.95% | 99.95% | |
| Learning Curve (Time to Productivity)(weeks) | 4-6 weeks | 4-6 weeks | |
| Monthly Cost (100GB/day ingestion)(USD) | $10,000-25,000 | $10,000-25,000 | |
| Time to First Alert(minutes) | 15-30 minutes | 15-30 minutes | |
| Default Data Retention (included in pricing)(months) | 12 months | 12 months | |
| SIEM Compliance Modules (pre-built)(count) | 12 (HIPAA, PCI, SOC2, NIST) | 12 (HIPAA, PCI, SOC2, NIST) | |
| Enterprise Customers(millions) | 10,000+ (2024) | 10,000+ (2024) | |
| Base Monthly Cost (Single User Cloud)(USD) | $4,500+ (minimum commitment) | $4,500+ (minimum commitment) | |
| Annual Cost (1GB/day Log Ingestion)(USD) | $54,000-$108,000 | $54,000-$108,000 | |
| Log Query Response Time(milliseconds) | <100 | <100 | |
| Event Ingestion Rate (Single Indexer)(events/sec) | 10,000+ | 10,000+ | |
| Native Data Source Integrations(count) | 400+ | 400+ | |
| Built-In Machine Learning Algorithms(count) | 12+ algorithms | 12+ algorithms | |
| Minimum RAM Requirement (Self-Hosted)(GB) | 8+ | 8+ | |
| Base Annual Cost(USD) | $2,400 | $2,400 | |
| Additional Data Ingestion Cost(USD per 10GB/day) | $250-350 | $250-350 | |
| Number of Integrations(integrations) | 900+ | 900+ | |
| Base Monthly Pricing (1GB/day ingestion)(USD) | $1,500-$2,000 | $1,500-$2,000 | |
| Query Response Time (1GB dataset)(milliseconds) | 2,000-5,000ms | 2,000-5,000ms | |
| Standard Data Retention(months) | 30-365 days | 30-365 days | |
| SIEM Use Cases Supported(count) | 400+ built-in detections | 400+ built-in detections | |
| Base License Cost (Annual)(USD) | $5,000 minimum | $5,000 minimum | |
| Typical Enterprise Implementation Cost(USD) | $50,000-$200,000 | $50,000-$200,000 | |
| Data Sources Supported(integrations) | 150+ (broader enterprise data ecosystem) | 150+ (broader enterprise data ecosystem) | |
| Average Search Query Time (1GB dataset)(seconds) | 1-5 seconds | 1-5 seconds | |
| Built-in Machine Learning Models(count) | 20+ (anomaly detection, forecasting, UEBA) | 20+ (anomaly detection, forecasting, UEBA) | |
| Typical Deployment Time(hours) | 8-16 weeks | 8-16 weeks | |
| GitHub Stars (Community Adoption)(count) | 2,500+ (proprietary, less open) | 2,500+ (proprietary, less open) | |
| Minimum Monthly Commitment(USD) | $3,600 ($120/day minimum) | $3,600 ($120/day minimum) | |
| Log Ingestion at $10k/Month Spend(GB per day) | ~200 | ~200 | |
| Default Metrics Retention(days) | 30 (upgradeable to unlimited) | 30 (upgradeable to unlimited) | |
| Enterprise Customer Count (2025)(organizations) | 12,000 | 12,000 | |
| Cost per 100 Users (Annual)(USD) | $36,000-$180,000 | $36,000-$180,000 | |
| Data Ingestion Capacity (Standard Plan)(GB/day) | 500GB | 500GB | |
| Average Implementation Timeline(months) | 8-12 weeks | 8-12 weeks | |
| Maximum Daily Data Ingestion(GB/day) | 1,024 GB/day (1TB) | 1,024 GB/day (1TB) | |
| Starting Annual License Cost(USD) | $35,000+ | $35,000+ | |
| User Training Requirement Rate(%) | 75% of new users need training | 75% of new users need training | |
| Fortune 500 Adoption Rate(%) | 85% adoption | 85% adoption | |
| Third-Party Integration Count(integrations) | 500+ integrations | 500+ integrations | |
| Mean Time to Detect (MTTD)(seconds) | Hours to days (after log ingestion/analysis) | Hours to days (after log ingestion/analysis) | |
| Mean Time to Respond (MTTR)(minutes) | 30-120 minutes (manual investigation) | 30-120 minutes (manual investigation) | |
| Number of Platform Integrations(integrations) | 500+ | 500+ | |
| Starting Price (Annual, 100 Endpoints/1TB Data)(USD) | $50,000-$75,000 | $50,000-$75,000 | |
| Compliance Frameworks Supported(frameworks) | 100+ (PCI-DSS, HIPAA, SOC 2, GDPR, NIST, CIS, FedRAMP) | 100+ (PCI-DSS, HIPAA, SOC 2, GDPR, NIST, CIS, FedRAMP) | |
| Data Retention Period (Standard Tier)(days) | Configurable (30-2000+ days) | Configurable (30-2000+ days) |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
E
2ElasticsearchSplunk leads1 tie
Elasticsearch
Attribute
Splunk
- Free and open-source (SSPL for some features)(winner)Licensing ModelProprietary with subscription pricing
- $0 (self-hosted) to $15,000+ (managed)(winner)Starting Annual Cost$3,000 - $50,000+ per year
- Basic anomaly detection with Elastic MLMachine Learning CapabilitiesAdvanced ML Toolkit with predictive analytics(winner)
- High (requires Java, Kubernetes knowledge)Setup ComplexityLower (managed cloud deployment)(winner)
- Up to 500GB+ (unlimited potential)Data Ingestion Rate (per day)Up to 1TB+ (license-dependent)
- Community-driven + paid support optionalEnterprise Support24/7 dedicated enterprise support included(winner)
- Limited; requires custom configurationCompliance Features (HIPAA, SOC2, etc.)Full certifications included (HIPAA, SOC2, PCI-DSS)(winner)
- Licensing Model
Elasticsearch
Free and open-source (SSPL for some features)(winner)
Splunk
Proprietary with subscription pricing
- Starting Annual Cost
Elasticsearch
$0 (self-hosted) to $15,000+ (managed)(winner)
Splunk
$3,000 - $50,000+ per year
- Machine Learning Capabilities
Elasticsearch
Basic anomaly detection with Elastic ML
Splunk
Advanced ML Toolkit with predictive analytics(winner)
- Setup Complexity
Elasticsearch
High (requires Java, Kubernetes knowledge)
Splunk
Lower (managed cloud deployment)(winner)
- Data Ingestion Rate (per day)
Elasticsearch
Up to 500GB+ (unlimited potential)
Splunk
Up to 1TB+ (license-dependent)
- Enterprise Support
Elasticsearch
Community-driven + paid support optional
Splunk
24/7 dedicated enterprise support included(winner)
- Compliance Features (HIPAA, SOC2, etc.)
Elasticsearch
Limited; requires custom configuration
Splunk
Full certifications included (HIPAA, SOC2, PCI-DSS)(winner)
Full Comparison
| Attribute | Elasticsearch | Splunk |
|---|---|---|
| Monthly Ingestion Cost per GB(USD) | $0.10 - $0.20 | — |
| Base Annual Cost (Small Deployment)(USD) | $0 (self-hosted)(winner) | $3,000 - $5,000 |
| Per-Gigabyte Ingestion Cost(USD per GB per day) | $0 (unlimited after infrastructure cost)(winner) | $0.80 - $1.50 |
| Monthly Cost (100GB index, 1M queries/month)(USD) | $200-500 self-hosted | — |
| Monthly Cost (1TB/day ingestion)(USD) | $3,000-$8,000 | — |
Show 23 more attributesPrice per GB Ingested(USD/GB) $0.02-$0.05 — Starting Annual Cost(USD) $0 (open-source) or $12,000 $4,500 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) $36,000 (minimum SaaS) Managed Cloud Starting Price(USD/month) $1.95/hour minimum ($429/month) — Self-Hosted Cost(USD/month) Free (open source core) — Base Monthly Cost (100GB/day)(USD) $3,500-$5,500 — Annual TCO (1TB/day ingestion)(USD) $450,000-$750,000 — Starting Annual Cost (single user/endpoint)(USD) $3,000-6,000 — Average Cost per GB Ingested(USD) $1.50 — Monthly Cost (100GB/day ingestion)(USD) $10,000-25,000 — Base Monthly Cost (Single User Cloud)(USD) $4,500+ (minimum commitment) — Annual Cost (1GB/day Log Ingestion)(USD) $54,000-$108,000 — Base Annual Cost(USD) $2,400 — Additional Data Ingestion Cost(USD per 10GB/day) $250-350 — Base Monthly Pricing (1GB/day ingestion)(USD) $1,500-$2,000 — Base License Cost (Annual)(USD) $5,000 minimum — Typical Enterprise Implementation Cost(USD) $50,000-$200,000 — Minimum Monthly Commitment(USD) $3,600 ($120/day minimum) — Cost per 100 Users (Annual)(USD) $36,000-$180,000 — Starting Annual License Cost(USD) $35,000+ — Starting Price (Annual, 100 Endpoints/1TB Data)(USD) $50,000-$75,000 — | ||
| Free Tier Data Retention(days) | 30+ days (self-hosted) | — |
| Query Response Time (1B records)(milliseconds) | 50-200ms(winner) | 100-300ms |
| Max Ingestion Throughput(events/second) | 75,000 | — |
| Query Latency (50th percentile)(milliseconds) | 300 | — |
| P99 Query Latency(milliseconds) | 100-500ms | — |
Show 27 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) 30 days included 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+ 10,000 Search Query Latency (1B docs)(milliseconds) 50-200ms 100-500ms Indexing Throughput(docs/sec) ~50,000 documents/second — Query Latency (p99)(milliseconds) ~45ms average across distributions — Deployment Time(seconds) 21-30 days — Query Performance (5TB dataset)(seconds) 8-12 seconds — APM Real-Time Metric Resolution(seconds) 10-30 seconds — Log Query Response Time(milliseconds) <100 — Event Ingestion Rate (Single Indexer)(events/sec) 10,000+ — Time to Incident Creation from Alert(seconds) Not applicable (generates alerts) — Query Response Time (1GB dataset)(milliseconds) 2,000-5,000ms — Average Search Query Time (1GB dataset)(seconds) 1-5 seconds — Log Ingestion at $10k/Month Spend(GB per day) ~200 — Data Ingestion Capacity (Standard Plan)(GB/day) 500GB — Maximum Alerts Per Minute Capacity(alerts/min) Limited by ingestion — | ||
| Setup Time to Production(minutes) | 40-80 (self-hosted) | 4-8 (managed cloud)(winner) |
| Time to Production(days) | 14-28 days (self-hosted) | — |
| Setup Time (Basic Deployment)(minutes) | 60-120 minutes | — |
| Agent Size/System Footprint(MB) | Heavy (varies, 500MB+) | — |
| Average Implementation Timeline(months) | 8-12 weeks | — |
| Supported Programming Languages(count) | Unlimited (via client libraries) | — |
| Supported Technologies(integrations) | 1000+ via community/Beats | — |
| Supported Languages/Frameworks(count) | 45+ via Logstash and Beats | — |
| Number of Platform Integrations(integrations) | 500+ | — |
| Supported Operating Systems(platforms) | Windows, Mac, Linux, cloud-native environments | — |
| Maximum Data Volume per Cluster(TB) | Petabyte-scale (1000+ TB) | — |
| Maximum Cluster Nodes(nodes) | Unlimited (enterprise only) | Unlimited (license-dependent) |
| 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 5 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) — Maximum Data Ingestion Per Day (Enterprise)(GB) Unlimited (licensing dependent) — | ||
| Query Language Complexity (1-10 scale)(complexity rating) | 7 (Lucene/DSL - steep learning curve) | — |
| Setup Complexity (1-10 scale)(difficulty score) | 5/10 (moderate) | — |
| User Training Requirement Rate(%) | 75% of new users need training | — |
| Deployment Options | Self-hosted, SaaS (Elastic Cloud), or Kubernetes | — |
| Minimum RAM Requirement (Self-Hosted)(GB) | 8+ | — |
| Cloud Deployment Option | Yes (Splunk Cloud, on-premise, hybrid) | — |
| Average Customer Onboarding Time(hours) | 30-90 days | — |
| Built-in Compliance Certifications(count) | 0 (requires custom hardening) | 6 (HIPAA, SOC2, PCI-DSS, FedRAMP, GDPR, ISO27001)(winner) |
| SIEM Compliance Modules (pre-built)(count) | 12 (HIPAA, PCI, SOC2, NIST) | — |
| Enterprise Security Features(count) | Enterprise Security module, threat detection, compliance dashboards | — |
| SIEM Use Cases Supported(count) | 400+ built-in detections | — |
| Machine Learning Use Cases Included(count) | 3 (anomaly detection, forecasting, outlier detection) | 15+ (threat detection, predictive analytics, correlation, clustering)(winner) |
| Aggregation Types Supported(count) | 40+ aggregation types | — |
| Machine Learning Capabilities(availability) | Full ML for anomaly detection, forecasting, root cause analysis | — |
| Machine Learning Sophistication(capability level) | Advanced: forecasting, clustering, outlier detection | — |
| Community Support Response Time(hours) | 12-48 (community forums) | 1 (24/7 enterprise SLA)(winner) |
| Support SLA Response Time(hours) | Community-based or 4+ hours (enterprise) | — |
| 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 | 700+ integrations(winner) |
| Default Typo Tolerance | Manual configuration required | — |
| Full-Text Search Languages Supported(languages) | 30+ languages | — |
Show 14 more attributesTypo Tolerance (Out-of-Box)(null) Requires configuration/plugin — Full-Text Search Native Support Native with advanced analyzers — Pre-Built Integrations(count) 300+ 800+ SQL Language Support(native support) Via plugin (not native) Native (SPL is SQL-like) Third-Party Integrations(integrations) 1000+ verified integrations 2,000+ apps Default Data Retention (included in pricing)(months) 12 months — Native Data Source Integrations(count) 400+ — Mobile App Incident Management(availability) Limited (dashboards only) — APM Distributed Tracing(languages supported) Limited (add-on required) — Data Sources Supported(integrations) 150+ (broader enterprise data ecosystem) — Full-Text Log Indexing Yes (native) — Built-in Machine Learning Models(count) 20+ (anomaly detection, forecasting, UEBA) — Native APM Capability Requires add-on license ($X additional) — Third-Party Integration Count(integrations) 500+ integrations — | ||
| 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(winner) | 14-42 days |
| Data Retention (Default)(months) | Unlimited (storage-dependent) | — |
| Data Retention (Standard)(months) | Unlimited (user-configured) | — |
| Default Data Retention Period(months) | 120 months (10 years) | — |
| Standard Data Retention(months) | 30-365 days | — |
| Learning Curve(difficulty rating) | 3-6 months | — |
| Query Language | Query DSL (complex, steep learning curve) | — |
| Query Language Learning Curve(complexity rating) | High (SPL requires training) | — |
| API Complexity(learning effort) | Complex Query DSL requiring technical expertise | — |
| Setup Time to First Query(minutes) | 120-240 minutes | — |
| Learning Curve (Time to Productivity)(weeks) | 4-6 weeks | — |
| Minimum RAM Requirement(GB) | 512MB | — |
| Enterprise Market Share(%) | 66% | — |
| Gartner SIEM Market Share(percent) | 28% (2024) | — |
| Enterprise Customer Count (2025)(organizations) | 12,000 | — |
| 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 | N/A (proprietary) |
| GitHub Stars (Community Adoption)(count) | 2,500+ (proprietary, less open) | — |
| Time to First Production Deployment(days) | 14-28 days | — |
| Average Time to Value(days) | 14-30 days | — |
| Initial Deployment Time(weeks) | 2-4 weeks | 1-2 weeks(winner) |
| Typical Deployment Timeline(weeks) | 7 weeks | — |
| Typical Deployment Time(hours) | 8-16 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) | — |
| APM Trace Sampling Depth(percent) | Sampling-dependent (configurable) | — |
| 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+ | 600+(winner) |
| Supported Data Types(types) | Logs, metrics (via plugins), custom JSON data | — |
| Query Language Expressiveness(languages supported) | Lucene, KQL, SQL, JavaScript | — |
| Number of Integrations(integrations) | 900+ | — |
| Available Integrations(integrations) | 1000+ plugins/integrations | — |
| GitHub Stars (Community Size Proxy)(stars) | 68,000+ | — |
| Pre-built Integrations/Apps(count) | 800+ apps in Splunkbase | — |
| 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) | — |
| Data Retention Period (Standard Tier)(days) | Configurable (30-2000+ days) | — |
| SLA Uptime Guarantee(percent) | Varies by deployment (self-hosted: customer responsibility) | — |
| Uptime SLA Guarantee(percent) | 99.95% | — |
| 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 | 10:1(winner) |
| Licensing Model | Proprietary SSPL/Elastic License (features restricted) | — |
| Major Version Age(years) | 23+ years in market | — |
| Machine Learning Models(count) | 50+ algorithms | — |
| Mean Time to Detection (MTTD)(minutes) | 15-60 minutes (depends on alert configuration) | — |
| Malware Detection Rate(%) | Varies by threat rules configured | — |
| Mean Time to Detect (MTTD)(seconds) | Hours to days (after log ingestion/analysis) | — |
| Mean Time to Respond (MTTR)(minutes) | 30-120 minutes (manual investigation) | — |
| Automated Response Actions(native actions) | Via SOAR integration (external) | — |
| Gartner Magic Quadrant Position (2024)(text) | Leader | — |
| Time to First Alert(minutes) | 15-30 minutes | — |
| Enterprise Customers(millions) | 10,000+ (2024) | — |
| Built-In Machine Learning Algorithms(count) | 12+ algorithms | — |
| FedRAMP Authorization(Yes/No) | FedRAMP Authorized | — |
| Compliance Frameworks Supported(frameworks) | 100+ (PCI-DSS, HIPAA, SOC 2, GDPR, NIST, CIS, FedRAMP) | — |
| Default Metrics Retention(days) | 30 (upgradeable to unlimited) | — |
| Default Log Retention (free tier)(days) | Not offered | — |
| Maximum Daily Data Ingestion(GB/day) | 1,024 GB/day (1TB) | — |
| Deployment Model | Hybrid (on-premise, cloud, multi-cloud) | — |
| Fortune 500 Adoption Rate(%) | 85% adoption | — |
EElasticsearch
SplunkMonthly Ingestion Cost per GB(USD)
$0.10 - $0.20
—
Base Annual Cost (Small Deployment)(USD)
$0 (self-hosted)
$3,000 - $5,000
Per-Gigabyte Ingestion Cost(USD per GB per day)
$0 (unlimited after infrastructure cost)
$0.80 - $1.50
Monthly Cost (100GB index, 1M queries/month)(USD)
$200-500 self-hosted
—
Monthly Cost (1TB/day ingestion)(USD)
$3,000-$8,000
—
Show 23 more attributes
Price per GB Ingested(USD/GB)
$0.02-$0.05
—
Starting Annual Cost(USD)
$0 (open-source) or $12,000
$4,500
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)
$36,000 (minimum SaaS)
Managed Cloud Starting Price(USD/month)
$1.95/hour minimum ($429/month)
—
Self-Hosted Cost(USD/month)
Free (open source core)
—
Base Monthly Cost (100GB/day)(USD)
$3,500-$5,500
—
Annual TCO (1TB/day ingestion)(USD)
$450,000-$750,000
—
Starting Annual Cost (single user/endpoint)(USD)
$3,000-6,000
—
Average Cost per GB Ingested(USD)
$1.50
—
Monthly Cost (100GB/day ingestion)(USD)
$10,000-25,000
—
Base Monthly Cost (Single User Cloud)(USD)
$4,500+ (minimum commitment)
—
Annual Cost (1GB/day Log Ingestion)(USD)
$54,000-$108,000
—
Base Annual Cost(USD)
$2,400
—
Additional Data Ingestion Cost(USD per 10GB/day)
$250-350
—
Base Monthly Pricing (1GB/day ingestion)(USD)
$1,500-$2,000
—
Base License Cost (Annual)(USD)
$5,000 minimum
—
Typical Enterprise Implementation Cost(USD)
$50,000-$200,000
—
Minimum Monthly Commitment(USD)
$3,600 ($120/day minimum)
—
Cost per 100 Users (Annual)(USD)
$36,000-$180,000
—
Starting Annual License Cost(USD)
$35,000+
—
Starting Price (Annual, 100 Endpoints/1TB Data)(USD)
$50,000-$75,000
—
Free Tier Data Retention(days)
30+ days (self-hosted)
—
Query Response Time (1B records)(milliseconds)
50-200ms
100-300ms
Max Ingestion Throughput(events/second)
75,000
—
Query Latency (50th percentile)(milliseconds)
300
—
P99 Query Latency(milliseconds)
100-500ms
—
Show 27 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)
30 days included
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+
10,000
Search Query Latency (1B docs)(milliseconds)
50-200ms
100-500ms
Indexing Throughput(docs/sec)
~50,000 documents/second
—
Query Latency (p99)(milliseconds)
~45ms average across distributions
—
Deployment Time(seconds)
21-30 days
—
Query Performance (5TB dataset)(seconds)
8-12 seconds
—
APM Real-Time Metric Resolution(seconds)
10-30 seconds
—
Log Query Response Time(milliseconds)
<100
—
Event Ingestion Rate (Single Indexer)(events/sec)
10,000+
—
Time to Incident Creation from Alert(seconds)
Not applicable (generates alerts)
—
Query Response Time (1GB dataset)(milliseconds)
2,000-5,000ms
—
Average Search Query Time (1GB dataset)(seconds)
1-5 seconds
—
Log Ingestion at $10k/Month Spend(GB per day)
~200
—
Data Ingestion Capacity (Standard Plan)(GB/day)
500GB
—
Maximum Alerts Per Minute Capacity(alerts/min)
Limited by ingestion
—
Setup Time to Production(minutes)
40-80 (self-hosted)
4-8 (managed cloud)
Time to Production(days)
14-28 days (self-hosted)
—
Setup Time (Basic Deployment)(minutes)
60-120 minutes
—
Agent Size/System Footprint(MB)
Heavy (varies, 500MB+)
—
Average Implementation Timeline(months)
8-12 weeks
—
Supported Programming Languages(count)
Unlimited (via client libraries)
—
Supported Technologies(integrations)
1000+ via community/Beats
—
Supported Languages/Frameworks(count)
45+ via Logstash and Beats
—
Number of Platform Integrations(integrations)
500+
—
Supported Operating Systems(platforms)
Windows, Mac, Linux, cloud-native environments
—
Maximum Data Volume per Cluster(TB)
Petabyte-scale (1000+ TB)
—
Maximum Cluster Nodes(nodes)
Unlimited (enterprise only)
Unlimited (license-dependent)
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 5 more attributes
Maximum 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)
—
Maximum Data Ingestion Per Day (Enterprise)(GB)
Unlimited (licensing dependent)
—
Query Language Complexity (1-10 scale)(complexity rating)
7 (Lucene/DSL - steep learning curve)
—
Setup Complexity (1-10 scale)(difficulty score)
5/10 (moderate)
—
User Training Requirement Rate(%)
75% of new users need training
—
Deployment Options
Self-hosted, SaaS (Elastic Cloud), or Kubernetes
—
Minimum RAM Requirement (Self-Hosted)(GB)
8+
—
Cloud Deployment Option
Yes (Splunk Cloud, on-premise, hybrid)
—
Average Customer Onboarding Time(hours)
30-90 days
—
Built-in Compliance Certifications(count)
0 (requires custom hardening)
6 (HIPAA, SOC2, PCI-DSS, FedRAMP, GDPR, ISO27001)
SIEM Compliance Modules (pre-built)(count)
12 (HIPAA, PCI, SOC2, NIST)
—
Enterprise Security Features(count)
Enterprise Security module, threat detection, compliance dashboards
—
SIEM Use Cases Supported(count)
400+ built-in detections
—
Machine Learning Use Cases Included(count)
3 (anomaly detection, forecasting, outlier detection)
15+ (threat detection, predictive analytics, correlation, clustering)
Aggregation Types Supported(count)
40+ aggregation types
—
Machine Learning Capabilities(availability)
Full ML for anomaly detection, forecasting, root cause analysis
—
Machine Learning Sophistication(capability level)
Advanced: forecasting, clustering, outlier detection
—
Community Support Response Time(hours)
12-48 (community forums)
1 (24/7 enterprise SLA)
Support SLA Response Time(hours)
Community-based or 4+ hours (enterprise)
—
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 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
—
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
700+ integrations
Default Typo Tolerance
Manual configuration required
—
Full-Text Search Languages Supported(languages)
30+ languages
—
Show 14 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+
800+
SQL Language Support(native support)
Via plugin (not native)
Native (SPL is SQL-like)
Third-Party Integrations(integrations)
1000+ verified integrations
2,000+ apps
Default Data Retention (included in pricing)(months)
12 months
—
Native Data Source Integrations(count)
400+
—
Mobile App Incident Management(availability)
Limited (dashboards only)
—
APM Distributed Tracing(languages supported)
Limited (add-on required)
—
Data Sources Supported(integrations)
150+ (broader enterprise data ecosystem)
—
Full-Text Log Indexing
Yes (native)
—
Built-in Machine Learning Models(count)
20+ (anomaly detection, forecasting, UEBA)
—
Native APM Capability
Requires add-on license ($X additional)
—
Third-Party Integration Count(integrations)
500+ integrations
—
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 attribute
Data Retention Cost per GB/month(USD)
$0.50-$1.50
—
Implementation Timeline(weeks)
6-12 weeks
14-42 days
Data Retention (Default)(months)
Unlimited (storage-dependent)
—
Data Retention (Standard)(months)
Unlimited (user-configured)
—
Default Data Retention Period(months)
120 months (10 years)
—
Standard Data Retention(months)
30-365 days
—
Learning Curve(difficulty rating)
3-6 months
—
Query Language
Query DSL (complex, steep learning curve)
—
Query Language Learning Curve(complexity rating)
High (SPL requires training)
—
API Complexity(learning effort)
Complex Query DSL requiring technical expertise
—
Setup Time to First Query(minutes)
120-240 minutes
—
Learning Curve (Time to Productivity)(weeks)
4-6 weeks
—
Minimum RAM Requirement(GB)
512MB
—
Enterprise Market Share(%)
66%
—
Gartner SIEM Market Share(percent)
28% (2024)
—
Enterprise Customer Count (2025)(organizations)
12,000
—
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
N/A (proprietary)
GitHub Stars (Community Adoption)(count)
2,500+ (proprietary, less open)
—
Time to First Production Deployment(days)
14-28 days
—
Average Time to Value(days)
14-30 days
—
Initial Deployment Time(weeks)
2-4 weeks
1-2 weeks
Typical Deployment Timeline(weeks)
7 weeks
—
Typical Deployment Time(hours)
8-16 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)
—
APM Trace Sampling Depth(percent)
Sampling-dependent (configurable)
—
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+
600+
Supported Data Types(types)
Logs, metrics (via plugins), custom JSON data
—
Query Language Expressiveness(languages supported)
Lucene, KQL, SQL, JavaScript
—
Number of Integrations(integrations)
900+
—
Available Integrations(integrations)
1000+ plugins/integrations
—
GitHub Stars (Community Size Proxy)(stars)
68,000+
—
Pre-built Integrations/Apps(count)
800+ apps in Splunkbase
—
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)
—
Data Retention Period (Standard Tier)(days)
Configurable (30-2000+ days)
—
SLA Uptime Guarantee(percent)
Varies by deployment (self-hosted: customer responsibility)
—
Uptime SLA Guarantee(percent)
99.95%
—
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
10:1
Licensing Model
Proprietary SSPL/Elastic License (features restricted)
—
Major Version Age(years)
23+ years in market
—
Machine Learning Models(count)
50+ algorithms
—
Mean Time to Detection (MTTD)(minutes)
15-60 minutes (depends on alert configuration)
—
Malware Detection Rate(%)
Varies by threat rules configured
—
Mean Time to Detect (MTTD)(seconds)
Hours to days (after log ingestion/analysis)
—
Mean Time to Respond (MTTR)(minutes)
30-120 minutes (manual investigation)
—
Automated Response Actions(native actions)
Via SOAR integration (external)
—
Gartner Magic Quadrant Position (2024)(text)
Leader
—
Time to First Alert(minutes)
15-30 minutes
—
Enterprise Customers(millions)
10,000+ (2024)
—
Built-In Machine Learning Algorithms(count)
12+ algorithms
—
FedRAMP Authorization(Yes/No)
FedRAMP Authorized
—
Compliance Frameworks Supported(frameworks)
100+ (PCI-DSS, HIPAA, SOC 2, GDPR, NIST, CIS, FedRAMP)
—
Default Metrics Retention(days)
30 (upgradeable to unlimited)
—
Default Log Retention (free tier)(days)
Not offered
—
Maximum Daily Data Ingestion(GB/day)
1,024 GB/day (1TB)
—
Deployment Model
Hybrid (on-premise, cloud, multi-cloud)
—
Fortune 500 Adoption Rate(%)
85% adoption
—
Pros & Cons
10 pros·6 cons across both
E
E
Elasticsearch
+5-3
Pros
- Free open-source core with unlimited scalability
- Horizontal scaling across multiple nodes with no licensing penalties
- RESTful API enables custom integrations and automation
- Fast full-text search with sub-100ms response times at scale
- Vibrant community with extensive plugins and third-party tools
Cons
- Steep learning curve requiring Java, Linux, and distributed systems knowledge
- No built-in advanced machine learning beyond basic anomaly detection
- Requires manual cluster management, backups, and security hardening
Splunk
+5-3
Pros
- Enterprise-grade machine learning with predictive analytics and AI-driven insights
- Fully managed cloud deployment (Splunk Cloud) eliminates infrastructure overhead
- Out-of-the-box compliance certifications (HIPAA, SOC2, PCI-DSS, FedRAMP)
- 24/7 expert support with guaranteed SLAs and on-call engineers
- Advanced threat detection with pre-built security use cases
Cons
- Licensing costs $3,000–$50,000+ annually depending on data volume and features
- Per-gigabyte pricing model penalizes high-volume data ingestion
- Limited customization compared to open-source alternatives
Frequently Asked Questions
5 questions
Elasticsearch is free open-source (self-hosted) but requires infrastructure and operational costs of $5,000–$15,000 annually for a small team. Splunk Cloud starts at $3,000–$5,000 annually for entry-level deployments but scales to $50,000+ for large organizations with high data volume. For 100GB/day ingestion, Elasticsearch with infrastructure typically costs $12,000/year, while Splunk costs $30,000+/year in licensing alone.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
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Wikipedia
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