The SIEM market has evolved significantly. Traditional rule-based detection cannot keep pace with modern threats. Cloud-only solutions exclude organizations with data sovereignty requirements. Pricing models based on data volume create unpredictable costs.
This guide provides evaluation criteria for organizations selecting SIEM or log management solutions in 2026.
Market Evolution
The SIEM landscape has shifted:
Traditional SIEM Limitations
- Rule-based detection misses novel threats
- Manual correlation requires skilled analysts
- Query languages create barriers
- Scaling costs grow with data volume
Modern Requirements
- AI-powered threat detection
- Natural language investigation
- Flexible deployment options
- Predictable pricing models
Evaluation Criteria
1. AI and Analytics Capability
Modern threats require AI-powered detection:
| Capability | Essential | Nice to Have |
|---|---|---|
| Automated threat detection | ✓ | |
| Natural language queries | ✓ | |
| MITRE ATT&CK mapping | ✓ | |
| Remediation guidance | ✓ | |
| Behavioral analytics | ✓ | |
| User entity analytics | ✓ |
Evaluation questions:
- Does the solution include AI capability or require add-ons?
- Can analysts query using natural language?
- Does AI provide actionable remediation guidance?
- Is AI available for on-premises deployment?
2. Deployment Flexibility
Organizations have varying requirements:
| Deployment Option | Use Case |
|---|---|
| Cloud-hosted | Organizations without data sovereignty requirements |
| On-premises | Regulated industries, government, defense |
| Hybrid | Flexibility for different data types |
| Air-gapped | Classified networks, critical infrastructure |
Evaluation questions:
- Does the vendor offer on-premises deployment?
- Is air-gapped deployment supported?
- Can deployment model change over time?
- Are features equivalent across deployment options?
3. Pricing Model
Pricing significantly impacts total cost:
| Pricing Model | Predictability | Risk |
|---|---|---|
| Per-GB ingestion | Low | Cost spikes during incidents |
| Per-host | Medium | Scales with infrastructure growth |
| Capacity-based | High | Predictable annual cost |
| Unlimited | Highest | Fixed cost regardless of volume |
Evaluation questions:
- How does pricing scale with data volume?
- Are there per-host or per-user charges?
- What happens during event storms?
- Can annual costs be predicted accurately?
4. Integration Capability
SIEM must integrate with existing infrastructure:
| Integration Type | Examples |
|---|---|
| Log Sources | Syslog, API, agents, cloud services |
| Security Tools | EDR, firewall, IDS/IPS, vulnerability scanners |
| IT Operations | Ticketing, CMDB, automation platforms |
| Identity | Active Directory, LDAP, SSO providers |
Evaluation questions:
- What log sources are supported natively?
- How are custom integrations handled?
- Is API access available for automation?
- What ticketing systems integrate?
5. Operational Complexity
Ongoing operations impact total cost:
| Factor | Low Complexity | High Complexity |
|---|---|---|
| Cluster Management | Not required | Required |
| Tuning | Minimal | Extensive |
| Upgrades | Automated | Manual |
| Scaling | Linear | Complex |
Evaluation questions:
- What ongoing maintenance is required?
- How are upgrades handled?
- What skills are needed for operations?
- How does the solution scale?
6. Compliance Support
Regulatory requirements drive many deployments:
| Framework | Key Requirements |
|---|---|
| PCI DSS | Log collection, daily review, retention |
| HIPAA | Audit controls, access monitoring |
| SOX | IT general controls, change management |
| GDPR | Data protection, breach detection |
| CMMC | Access control, audit logging |
Evaluation questions:
- What compliance frameworks are supported?
- Is compliance reporting automated?
- How is evidence collected for audits?
- Does the solution support required retention periods?
Vendor Comparison Matrix
Major Vendors
| Vendor | AI Capability | On-Premises | Pricing Model |
|---|---|---|---|
| Splunk | Add-on | Yes | Per-GB |
| Elastic | Limited | Yes | Per-node |
| Microsoft Sentinel | Yes | No | Per-GB |
| Datadog | Limited | No | Per-host + Per-GB |
| CrowdStrike | Yes | No | Per-endpoint |
| LogZilla | Included | Yes | Capacity-based |
Detailed Comparison
Splunk
Strengths: Mature platform, extensive ecosystem, flexible deployment
Weaknesses: High cost at scale, complex pricing, AI requires add-ons
Best for: Organizations with existing Splunk investment and budget
Elastic/ELK
Strengths: Open-source option, flexible architecture
Weaknesses: Operational complexity, limited native AI, cluster management
Best for: Organizations with Elasticsearch expertise
Microsoft Sentinel
Strengths: Azure integration, AI capability, Microsoft ecosystem
Weaknesses: Cloud-only, Azure dependency, complex pricing
Best for: Microsoft-centric organizations without on-premises requirements
Datadog
Strengths: Modern interface, broad observability, easy deployment
Weaknesses: Cloud-only, expensive at scale, no on-premises option
Best for: Cloud-native organizations without data sovereignty requirements
CrowdStrike
Strengths: Strong endpoint focus, threat intelligence, AI capability
Weaknesses: Cloud-only, endpoint-centric, limited log management
Best for: Organizations prioritizing endpoint security
LogZilla
Strengths: AI included, on-premises option, predictable pricing, high performance
Weaknesses: Smaller ecosystem than Splunk
Best for: Organizations requiring on-premises deployment, AI capability, or cost reduction
Evaluation Process
Phase 1: Requirements Definition
- Document use cases and requirements
- Identify deployment constraints
- Establish budget parameters
- Define success criteria
Phase 2: Vendor Shortlist
- Apply evaluation criteria
- Eliminate non-compliant vendors
- Request detailed pricing
- Schedule demonstrations
Phase 3: Proof of Concept
- Deploy in test environment
- Ingest representative data
- Test key use cases
- Measure performance
Phase 4: Selection
- Score vendors against criteria
- Calculate total cost of ownership
- Assess vendor viability
- Make selection decision
RFP Template Sections
Technical Requirements
- Log collection capabilities
- Storage and retention
- Search and investigation
- AI and analytics
- Alerting and notification
- Reporting and dashboards
Deployment Requirements
- On-premises support
- Cloud deployment options
- Air-gapped capability
- High availability
- Disaster recovery
Integration Requirements
- Log source support
- Security tool integration
- IT operations integration
- API availability
- Custom integration support
Compliance Requirements
- Framework support
- Automated reporting
- Evidence collection
- Retention capabilities
- Audit support
Commercial Requirements
- Pricing model
- Licensing terms
- Support options
- Training availability
- Professional services
Common Pitfalls
Underestimating Total Cost
- Consider infrastructure, operations, and training
- Account for growth over contract term
- Include professional services
- Factor in opportunity cost of complexity
Ignoring Deployment Constraints
- Verify on-premises capability if required
- Confirm air-gapped support
- Validate data residency compliance
- Test actual deployment process
Overlooking Operational Burden
- Assess ongoing maintenance requirements
- Evaluate upgrade complexity
- Consider staffing implications
- Test scaling procedures
Focusing Only on Features
- Evaluate actual usability
- Test with real analysts
- Measure time to value
- Assess learning curve
Proof of Concept Best Practices
A well-designed POC validates vendor claims and reveals operational realities:
POC Success Criteria
Define measurable criteria before starting:
| Criterion | Target | Measurement Method |
|---|---|---|
| Ingestion rate | Handle peak volume | Load test with production data |
| Query latency | <2 seconds for common queries | Benchmark standard queries |
| AI accuracy | Relevant findings in 90%+ of queries | Analyst evaluation |
| Alert latency | <30 seconds from event to alert | End-to-end timing |
| Uptime | 99.9% during POC | Monitoring |
Data Requirements
Use representative data for meaningful evaluation:
- Include all log source types
- Capture peak volume periods
- Include security incidents if available
- Test compliance reporting scenarios
Evaluation Team
Include stakeholders from all affected teams:
- Security operations (primary users)
- IT operations (infrastructure owners)
- Compliance (regulatory requirements)
- Finance (cost validation)
- Procurement (contract terms)
Duration
Typical POC timeline:
| Phase | Duration | Activities |
|---|---|---|
| Setup | 1 week | Deployment, configuration |
| Data ingestion | 1 week | Source configuration, validation |
| Use case testing | 2 weeks | Security, operations, compliance |
| Evaluation | 1 week | Scoring, documentation |
Four to six weeks provides adequate time for meaningful evaluation.
Micro-FAQ
What should organizations look for in a SIEM in 2026?
Key criteria include AI-powered analysis, flexible deployment options (cloud and on-premises), predictable pricing, and integration capabilities. Avoid solutions that lock organizations into specific deployment models or pricing structures.
Is AI capability essential for modern SIEM?
AI capability significantly reduces analyst workload and improves detection accuracy. Solutions without AI require more manual effort for threat hunting, correlation, and investigation.
Should organizations choose cloud or on-premises SIEM?
The choice depends on data sovereignty requirements, regulatory constraints, and operational preferences. Many organizations benefit from solutions offering both deployment options.
How should organizations evaluate SIEM pricing?
Evaluate total cost of ownership including licensing, infrastructure, operations, and training. Beware of pricing models that scale unpredictably with data volume or host count.
Next Steps
Organizations evaluating SIEM solutions should prioritize AI capability, deployment flexibility, and predictable pricing. Request demonstrations from shortlisted vendors and conduct proof-of-concept deployments with representative data.
Download LogZilla Use Cases (PDF)
Watch AI-powered log analysis demos to evaluate LogZilla's natural language query capability.