Microsoft Sentinel is Microsoft's cloud-native SIEM (Security Information and Event Management) platform, built on Azure and deeply integrated with the Microsoft security ecosystem. SenseOn is a unified detection platform that consolidates SIEM, EDR (Endpoint Detection and Response), NDR (Network Detection and Response), SOAR (Security Orchestration, Automation, and Response), and UEBA (User and Entity Behaviour Analytics) into a single agent. For mid-market security teams, the choice between the two affects cost predictability, operational complexity, and detection outcomes in ways that are not immediately obvious from feature checklists.
This guide provides a detailed, honest comparison to help security leaders make the right decision for their organisation.
What Is Microsoft Sentinel?
Microsoft Sentinel is a cloud-native SIEM and SOAR platform built on Azure Monitor and Log Analytics. It ingests security data from across an organisation's environment, applies detection analytics rules, and provides investigation and response capabilities through integration with Azure Logic Apps.
Sentinel's core architecture works as follows:
- Data connectors ingest logs from Microsoft services (Microsoft 365, Azure AD, Defender) and third-party sources (firewalls, endpoint tools, cloud platforms) into a Log Analytics workspace
- Analytics rules, both built-in and custom, process ingested data to generate alerts based on KQL (Kusto Query Language) queries
- Incidents group related alerts and enrich them with entity information (users, hosts, IPs) for investigation
- Automation rules and playbooks (powered by Azure Logic Apps) enable automated response actions, from enrichment lookups to containment steps
- Workbooks provide dashboards and visualisations for operational and compliance reporting
Sentinel's pricing is consumption-based, primarily driven by the volume of data ingested into the Log Analytics workspace, measured in gigabytes per day. Microsoft offers commitment tiers (100 GB/day, 200 GB/day, etc.) that provide per-GB discounts, plus free ingestion for certain Microsoft data sources.
For organisations already invested in the Microsoft ecosystem, running Microsoft 365, Azure AD, Microsoft Defender for Endpoint, and Azure infrastructure, Sentinel offers native integrations that are genuinely smooth. The depth of the Microsoft-to-Microsoft data flow is a real advantage.
What Is SenseOn?
SenseOn is a unified security platform that consolidates the multi-tool stack most mid-market organisations have assembled. A single lightweight agent collects endpoint telemetry, network traffic metadata, cloud API activity, and identity events. The cross-domain correlation, three independent AI methodologies (supervised learning, unsupervised learning, and deep learning) that cross-validate every potential threat, performs detection directly on this telemetry.
SenseOn's pricing is based on Flexible Intelligence Credits (FIC): a consumption-based credit model where credits are consumed by outcome: detection, investigation, compliance, and AI-accelerated resolution. An annual credit commitment covers all capabilities, with no data ingestion charges, no per-GB fees, and no separate module costs for NDR, SOAR, or UEBA.
The platform includes built-in automated response actions, case management, compliance reporting for frameworks including DORA and NIS2, and a unified investigation console. It is designed for mid-market security teams with 500 to 7,500 employees.
Comparison Table
The following table compares the two platforms across the dimensions that most affect mid-market security operations:
| Capability | Microsoft Sentinel | SenseOn | |---|---|---| | Data Ingestion Costs | Pay-per-GB ingested; commitment tiers available; costs scale with data volume | No ingestion costs. Flexible Intelligence Credits cover all telemetry | | Deployment Complexity | Moderate to high: requires Azure subscription, connector configuration, KQL rule authoring | Low: single agent deployment, days to operational | | Query Language | KQL (Kusto Query Language), powerful but requires specialist knowledge | No query language required. Cross-domain correlation automates detection | | Detection Approach | Analytics rules (built-in + custom KQL queries) + scheduled ML models | Cross-domain correlation: triple cross-validation (behavioural baselines + anomaly detection + sequence-aware classification) | | Built-in EDR | No: requires Microsoft Defender for Endpoint (separate licence) | Yes: endpoint detection included in single agent | | Built-in NDR | No: requires third-party NDR or custom network log ingestion | Yes: network traffic metadata captured by single agent | | Automation | Azure Logic Apps playbooks, powerful but require Azure expertise to build | Built-in automated response actions; no playbook authoring required | | Analyst Skill Required | High: KQL proficiency, Azure Logic Apps, connector management | Moderate: unified console designed for lean teams | | Azure Dependency | Full: requires Azure subscription; best with Microsoft-centric environment | None: infrastructure-agnostic | | Compliance Reporting | Workbooks and custom dashboards; requires configuration | Built-in: DORA, NIS2, and regulatory reporting included |
The Cost Trap: Sentinel's Ingestion Pricing
Sentinel's consumption-based pricing is its most significant consideration for mid-market teams, and it deserves detailed examination. The hidden costs of SIEM platforms apply directly to Sentinel, despite Microsoft's positioning as a more affordable alternative to Splunk.
How Sentinel Costs Accumulate
Base ingestion: Sentinel charges per GB of data ingested into the Log Analytics workspace. At pay-as-you-go rates, this is approximately £2.30-£2.80 per GB (pricing varies by region). Commitment tiers reduce the per-GB cost but require minimum daily ingestion volumes.
Data volume reality: A mid-market organisation with 2,000 endpoints, standard network infrastructure, cloud workloads, and Microsoft 365 generates 50-200 GB of security-relevant log data per day. At 100 GB/day, annual ingestion costs alone reach £84,000-£102,000, before any other charges.
Retention costs: Sentinel includes 90 days of interactive retention in the ingestion price. Beyond that, organisations pay for additional retention. Given that regulations like DORA and NIS2 may require 12+ months of log retention, this adds a meaningful ongoing cost.
Automation costs: Sentinel's playbooks run on Azure Logic Apps, which are billed separately per execution. A busy SOC running automated enrichment and response playbooks can generate thousands of Logic Apps executions per month, each carrying compute and connector charges.
Connector costs: While many Microsoft-to-Microsoft connectors are free or included, third-party data connectors, for firewalls, endpoint tools, cloud platforms, and identity providers, often require Azure Functions or custom log ingestion pipelines that carry their own compute costs.
The data tax dynamic: Like all per-GB SIEMs, Sentinel creates a perverse incentive. To control costs, organisations reduce data ingestion. Reducing data ingestion creates visibility blind spots. Blind spots lead to missed detections. This is the fundamental tension of consumption-based SIEM pricing: the tool designed to improve visibility financially incentivises reduced visibility.
Microsoft's commitment tiers and free ingestion for certain data types (Microsoft 365 audit logs, Azure Activity logs) partially mitigate this, but they do not eliminate it. Third-party data sources, which are critical for complete detection, carry full ingestion costs.
SenseOn's Pricing Model
SenseOn uses Flexible Intelligence Credits (FIC): an annual credit commitment that covers all capabilities. All telemetry (endpoint, network, cloud, identity) is collected, processed, and retained within the credit pool. Credits are consumed by outcome (detection, investigation, compliance, AI-accelerated resolution), not by data volume. There are no per-GB ingestion charges, no overage fees, and no per-automation execution costs. With Resolve, credits are consumed only on autonomous completion; human escalations are free.
For budgeting purposes, this means a mid-market CISO can predict annual security platform costs with certainty: the more you commit, the lower the unit rate. No surprise invoices from data volume spikes. No difficult conversations about which log sources to exclude to stay within budget.
Detection Capabilities Compared
The detection philosophies of the two platforms are different.
Sentinel's Rule-Based Approach
Sentinel detects threats through analytics rules: KQL queries that run on a schedule against ingested data. Microsoft provides a library of built-in rules covering common threat scenarios, and organisations can write custom rules to address their specific environment.
This approach has clear strengths. KQL is an expressive query language that allows skilled analysts to construct highly specific detection logic. For organisations with dedicated detection engineers, Sentinel's rule authoring provides granular control over what gets detected and how.
The limitation is that detection quality is directly proportional to rule quality. Out-of-the-box rules provide baseline coverage, but real-world effectiveness requires continuous tuning: adjusting thresholds, adding exclusions for legitimate business activity, and writing custom rules for environment-specific threats. This demands KQL expertise and ongoing analyst time.
Sentinel also includes scheduled ML-based anomaly detection, but this operates as an addition to rule-based detection rather than a replacement for it. The ML models identify deviations from baseline behaviour, but they do not cross-validate findings with other methodologies.
SenseOn's Cross-Domain Correlation Approach
SenseOn's cross-domain correlation replaces rule authoring with automated detection. Three independent AI methodologies, supervised learning (trained on known attack patterns), unsupervised learning (behavioural anomaly detection), and deep learning (multi-stage attack sequence analysis), each independently assess every event.
When a methodology flags a potential threat, the other two independently evaluate the same data. Only threats validated across multiple methodologies are escalated as cases. This cross-validation produced zero false positives in independent AV-Comparatives testing.
For mid-market teams, the operational difference is significant. Sentinel requires organisations to invest in detection engineering: writing, testing, tuning, and maintaining KQL rules. SenseOn's detection is built-in and continuously improving through its AI models. There are no rules to write, no thresholds to tune, and no KQL queries to debug.
The Kingspan deployment illustrates this difference. After consolidating onto SenseOn, Kingspan's daily case volume dropped from 40 to 40 per month, a 97.5% reduction, because the cross-domain correlation engine eliminated the false positives that rule-based detection inevitably generates.
Operational Complexity
Beyond detection and cost, the day-to-day operational experience differs substantially.
Operating Sentinel
Running Sentinel effectively requires:
- KQL proficiency: analysts must write and modify queries for investigation, threat hunting, and custom detection rules
- Azure Logic Apps expertise: building and maintaining automation playbooks requires understanding of Azure's workflow engine, connectors, and error handling
- Connector management: ensuring data sources remain connected, parsing correctly, and not generating excessive costs
- Workspace architecture: managing Log Analytics workspace configuration, data retention policies, and access controls
- Cost monitoring: actively tracking ingestion volumes and costs to avoid budget overruns
For organisations with a dedicated SecOps team of 4+ analysts and existing Azure expertise, these requirements are manageable. For a mid-market security team of 1-3 people, they represent a significant operational burden that competes with the primary mission of detecting and responding to threats.
Operating SenseOn
SenseOn's operational model is designed for lean teams:
- No query language: the cross-domain correlation engine handles detection; analysts focus on investigating pre-correlated cases
- No playbook authoring: automated response actions are built-in and configured through the platform's interface
- No connector management: the single agent collects all telemetry directly
- No cost monitoring: the FIC credit model eliminates data volume concerns
- Single console: investigation, response, and reporting happen in one interface
ED&F Man's experience demonstrates the impact: after deploying SenseOn, their incident response speed improved by 3x, driven by pre-correlated evidence and a unified investigation workflow rather than pivoting between Sentinel, Defender, and third-party tools.
When Sentinel Is the Right Choice
Microsoft Sentinel is the stronger choice in specific scenarios:
100% Microsoft environments: If your organisation runs Microsoft 365, Azure AD, Azure infrastructure, Microsoft Defender for Endpoint, and Microsoft Defender for Cloud, and has minimal non-Microsoft security tools, Sentinel's native integrations provide smooth, low-friction data ingestion with free or reduced-cost telemetry for Microsoft sources.
Enterprise with dedicated SecOps team: Organisations with 5+ security analysts, dedicated detection engineers proficient in KQL, and Azure expertise can use Sentinel's flexibility to build a highly customised detection and response capability. The investment in rule authoring and playbook development pays off when you have the team to sustain it.
General-purpose SIEM/SOAR requirement: If your organisation needs a platform that serves as both a SIEM and a SOAR, ingesting data from dozens of diverse sources, running complex automation workflows, and providing a central investigation hub, Sentinel's breadth and Azure Logic Apps integration serve this use case well.
Regulatory requirement for traditional SIEM: Some regulated industries and compliance frameworks specifically require a SIEM platform. While SenseOn provides equivalent or superior detection and reporting capabilities, organisations with prescriptive regulatory requirements may need to demonstrate they operate a named SIEM product.
When SenseOn Is the Right Choice
SenseOn delivers better outcomes for the majority of mid-market security teams:
Cost predictability: Sentinel's per-GB consumption pricing creates budget uncertainty. SenseOn's Flexible Intelligence Credit model provides total cost visibility for the fiscal year through an annual credit commitment. No per-GB charges, no data-tax negotiations, no difficult trade-offs between visibility and budget.
Limited analyst capacity: If your security team has 1-3 analysts, investing their time in KQL rule authoring, Logic Apps configuration, and connector management is not an efficient use of scarce resources. SenseOn automates detection and triage, letting analysts focus on investigating and responding to genuine threats.
Detection without rule-writing: SenseOn's cross-domain correlation provides detection out of the box: no KQL queries to author, no thresholds to tune, no detection-rule maintenance. For organisations that want strong detection from day one, this is a fundamental operational advantage.
SIEM replacement: If your current SIEM (Sentinel or otherwise) is generating more operational overhead than security value, SenseOn provides a path to better detection with lower operational cost. The FIC model eliminates the data tax that constrains visibility in all per-GB SIEMs.
Mixed or non-Microsoft environments: If your infrastructure spans multiple cloud providers, includes significant non-Microsoft tooling, or runs on-premises workloads, Sentinel's Microsoft-centric integration advantage diminishes. SenseOn's single agent is infrastructure-agnostic and provides consistent visibility regardless of the underlying technology stack.
DORA and NIS2 compliance: Both regulations are now in enforcement. SenseOn includes built-in compliance reporting for these frameworks. Sentinel can produce equivalent reports, but they require custom workbook development and KQL query authoring.
Frequently Asked Questions
Is SenseOn a SIEM replacement for Microsoft Sentinel?
Yes. SenseOn provides the detection, correlation, and compliance reporting capabilities that organisations use Sentinel for, plus built-in EDR, NDR, and automated response. Unlike Sentinel, SenseOn generates its own telemetry from a single agent rather than ingesting logs from third-party tools, which eliminates data ingestion costs and the need to manage connectors.
How do SenseOn and Sentinel compare on cost for a 2,000-endpoint deployment?
Sentinel's cost depends entirely on data volume. A 2,000-endpoint environment generating 100 GB/day of logs can cost £150,000-£300,000+ per year in ingestion alone, before automation rules and retention charges. SenseOn's Flexible Intelligence Credit (FIC) model covers all capabilities through an annual credit commitment with no data-volume variables: credits are consumed by outcome, not by gigabytes ingested.
Do I need Azure to use Sentinel?
Yes. Microsoft Sentinel is built on Azure and requires an Azure subscription. It works best in Microsoft-centric environments. Ingesting data from non-Microsoft sources requires custom connectors that vary in quality and maintenance burden. SenseOn is infrastructure-agnostic and works across any environment.
Can SenseOn integrate with Microsoft 365 Defender?
SenseOn operates as a complete detection and response platform that collects its own telemetry rather than relying on signals from Microsoft 365 Defender. For organisations that want to retain Defender for endpoint antivirus, SenseOn can run alongside it while providing the detection, correlation, and response layer that Sentinel would otherwise handle.
Which platform requires less analyst expertise to operate?
SenseOn requires significantly less specialist knowledge. Sentinel demands proficiency in KQL query language, Azure Logic Apps for automation playbooks, and ongoing management of detection analytics rules. SenseOn's cross-domain correlation handles detection and triage automatically, and its unified console is designed for lean teams without SIEM-specialist expertise.
