SenseOn vs Microsoft Sentinel: Which Fits Your Security Team?

Microsoft Sentinel is Microsoft's cloud-native SIEM (Security Information and Event Management) platform. Microsoft describes it as "a cloud-native SIEM solution that delivers scalable, cost-efficient security across multicloud and multiplatform environments" (Microsoft Learn). It runs on Azure and is deeply integrated with the Microsoft security ecosystem. SenseOn is a detection and response platform: one lightweight Universal Sensor captures endpoint, network and identity telemetry, the Data Fabric connects it with the other signals you already have, and the Intelligence Fabric correlates it into cases. For security teams, the choice between the two comes down to one question more than any feature list: which of your data gets real-time detection, and what you pay for that.

This guide compares the two honestly, using Microsoft's own documentation for every Sentinel fact.

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 enable automated response actions, from enrichment lookups to containment steps. Microsoft's documentation states that "playbooks in Microsoft Sentinel are based on workflows built in Azure Logic Apps" (Microsoft Learn)
• Workbooks provide dashboards and visualisations for operational and compliance reporting

Sentinel's pricing is consumption-based, primarily driven by the volume of data ingested, measured in gigabytes per day. Microsoft's billing documentation describes pay-as-you-go as the default model and notes that "commitment tier pricing starts at 100 GB per day". Microsoft's pricing page lists commitment tiers from 100 GB to 50,000 GB a day with savings of up to 52 percent over pay-as-you-go rates, 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 detection and response platform built to join evidence across the tools you keep. A single lightweight Universal Sensor captures endpoint, network and identity telemetry on the same device, on Windows, macOS and Linux. The Data Fabric connects supported endpoint, network, identity, cloud, email and application signals, including out-of-the-box integrations with Microsoft 365, AWS and identity providers, and normalises them. The Intelligence Fabric correlates them into cases using cross-domain correlation: three independent AI methodologies (supervised learning, unsupervised learning, and deep learning) that cross-validate each potential threat.

SenseOn is priced as an annual pool of Flex Intelligence Credits, with tiered plans on the pricing page. Detection, observability and compliance draw from one credit pool, and you pay for outcomes: detection and response across your data, analytics included, and the investigations SenseOn's AI completes. Data volume is one meter, not the whole bill.

SenseOn deploys alongside your existing SIEM and EDR, including Sentinel. Every AI and analyst action is written to an append-only Decision Trace, which gives chain-of-custody evidence for NIS2, DORA, the EU AI Act and ISO 27001.

Comparison Table

Sentinel's Ingestion Pricing

Sentinel's consumption-based pricing is its most significant consideration, 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. Our Microsoft Sentinel pricing guide works through the list prices and three example bills.

How Sentinel Costs Accumulate

Base ingestion: Sentinel charges per GB of data ingested. At Microsoft's East US list price on 23 September 2026, pay-as-you-go analytics ingestion was $4.30 per GB; the rate varies by region and currency, and Microsoft publishes it through its pricing page and cost estimator. Commitment tiers reduce the per-GB cost but require minimum daily ingestion volumes, and Microsoft only allows the tier to be lowered every 31 days (billing documentation).

Data volume reality: An organisation with 2,000 endpoints, standard network infrastructure, cloud workloads, and Microsoft 365 can generate tens to hundreds of gigabytes of security-relevant log data per day. Run your own volume through Microsoft's cost estimator before you sign.

Retention costs: Microsoft's billing documentation states that Sentinel workspaces "retain all data ingested into the workspace at no charge for the first 90 days" and that "retention beyond 90 days is charged" at standard Log Analytics retention prices (Microsoft Learn). Given that regulations like DORA and NIS2 may require 12+ months of log retention, this adds an ongoing cost.

Automation costs: Sentinel's playbooks run on Azure Logic Apps, which Microsoft lists as a separately charged service (billing documentation). On the Consumption plan, Logic Apps pricing is per action execution, and "connector billing is based on the number of calls".

Connector costs: Microsoft notes that "some of Microsoft Sentinel's data connectors and solutions use Azure Functions for data ingestion, which also has a separate associated cost" (Microsoft Learn).

Microsoft's commitment tiers and free ingestion for certain data types (Office 365 audit logs, Azure Activity logs and Defender security alerts, per the free data sources list) reduce the bill. Microsoft's own note is that "although alerts are free, the raw logs" for several Defender and Entra ID data types are paid.

"We will just use the data lake"

Microsoft's answer to ingestion cost is the data lake tier, at about $0.15 per GB in at East US list prices against $4.30 on the analytics tier. It is a real saving, and it has a real cost in detection. Microsoft's tier comparison (updated 2 July 2026) says data in the data lake tier "isn't available for real-time analytics features and threat hunting", lists "limitations on some features, including analytics rules, hunting queries, parsers, watchlists, workbooks, and playbooks", and warns that when you move a table from analytics to the lake, "all real-time analytics and hunting queries stop working".

Detection on lake data is possible, but only through jobs you build. Microsoft's KQL jobs and summary rules pages set the terms. New lake data has "a typical latency of up to 15 minutes before the data is available for querying". A scheduled job's "start time must be at least 30 minutes after job creation or editing". A tenant can run 5 jobs at once and keep 100 enabled, each with a 1-hour query timeout. To alert, results are written to an analytics-tier table, billed at analytics rates, where an analytics rule runs on them. Lake queries are billed at $0.005 per GB scanned (East US list, 23 September 2026).

So a Sentinel team decides, table by table, which data gets detection. The tables that move to the lake are the noisy, high-volume ones: firewall, DNS, proxy and network flow logs. Those are the records in which lateral movement and command-and-control traffic tend to show.

With SenseOn, you make that choice per log source, not per storage tier. Detection runs at three speeds: in real time on the endpoint (the Universal Sensor), in near real time across every log source you route to Detection & Response, as it streams through SenseOn's message queue, and with behavioural analytics that run about every five minutes across those same sources. SenseOn's AI agents can run queries and threat hunts across all retained data, in every storage tier and for the whole retention period. The tiers are designed for agents: long-horizon queries and hunts run in the background, so a query over older, colder data can take a little longer without anyone waiting on it. Data rolls from one tier to the next automatically, and you choose, per source, which pipeline it goes to: Detection & Response for detection, or Observability and Compliance for search and hunting. So you get data-lake storage economics (compressed storage) with detection as your routed data arrives, and search, hunting, posture management and dashboards over the data you keep.

SenseOn's Pricing Model

SenseOn is priced as an annual pool of Flex Intelligence Credits. Detection, observability and compliance draw from one credit pool, and you pay for outcomes: detection and response across your data, analytics included, and the investigations SenseOn's AI completes. Data volume is one meter, not the whole bill. SenseOn's cost compression combines three things: edge processing removes unnecessary data before the pipeline; intelligent routing down three pipelines, configured by Weave, SenseOn's security engineering agent, separates storage from compute; and ClickHouse compression, with storage priced on the compressed volume. Together these cut costs by about 40% against a traditional SIEM. The saving on SIEM costs alone would be larger, because SenseOn's price also includes the work of its Agentic Cybersecurity Team (ACT), the AI agents that investigate and resolve cases. You pay for the investigations the agents complete, not for an MDR service. Thirty days of hot storage are included in the detection and response pipeline cost; older data stays searchable by humans and AI agents across the Observability and Compliance pipelines, with no restore step, for as long as you keep it.

Detection Capabilities Compared

The detection philosophies of the two platforms are different.

Sentinel's Rule-Based Approach

Sentinel detects threats through analytics rules. Microsoft's documentation says that "by far the most common type of analytics rule, Scheduled rules are based on Kusto queries that are configured to run at regular intervals" (Microsoft Learn). Microsoft provides a library of rule templates 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. Bryant and Saiedian showed the dependency directly: re-engineering SIEM correlation rules improved detection rates, gave more descriptive alerts and lowered false positives, and they attribute much of SIEM's shortfall to insufficient configuration (Bryant and Saiedian, Computers & Security, 2020). CardinalOps' 2025 State of SIEM report found that enterprise SIEMs have detections for 22% of MITRE ATT&CK techniques, leaving 78% uncovered.

Sentinel also includes ML-based anomaly rules and the Fusion correlation engine. The anomaly rules "use machine learning to observe specific types of behaviors over a period of time to determine a baseline", and Microsoft notes that "anomaly rules don't generate their own alerts" but write to an Anomalies table for analysts to query (Microsoft Learn). These operate as additions to rule-based detection rather than a replacement for it.

SenseOn's Cross-Domain Correlation Approach

SenseOn's cross-domain correlation uses three independent AI methodologies: supervised learning (trained on known attack patterns), unsupervised learning (behavioural anomaly detection, an approach shown to detect insider threats from unlabelled data with low false positive rates and to hold up better when several models vote (Le and Zincir-Heywood, IEEE Transactions on Network and Service Management, 2021)), and deep learning (multi-stage attack sequence analysis).

When one methodology flags a potential threat, the other two assess the same data independently. In the AV-Comparatives Business Security Test, March to June 2024, SenseOn recorded 95.9% real-world protection and 98.7% malware protection with zero false alarms; Microsoft Defender Antivirus, tested in the same round, scored 98.2% and 99.4%, also with zero false alarms. That is an endpoint protection test, in which a false alarm is a clean file or website wrongly blocked, so it speaks to the sensor rather than to SIEM detection. Detection engineering does not disappear: Forge drafts and tests detection changes, and a detection engineer reviews, tunes and approves them. The difference is that you start from built-in detection rather than building and tuning the rule set yourself. That matters because the rising volume of incidents has overwhelmed analysts, and the survey literature treats prioritisation and validation of alerts before they reach a human as the answer (Jalalvand et al., ACM Computing Surveys, 2024).

The published numbers show the effect. Across customer environments, about 97% of alert noise is removed before an analyst sees a queue. In one Fortune 500 services environment, 33.4 billion events a month produced 36,000 alerts, 173 cases needing human judgement in 30 days, and about six human escalations a day. At ED&F Man, Cyber Security Analyst John Jordan described the change: "We managed to cut down from 40 cases a day down to about 40 a month… it massively reduces how much time we spend following false leads." That is a measured change in cases handled, not a universal false-positive rate.

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
• Tier and workspace architecture: deciding which tables sit on the analytics tier and which in the lake, and managing retention and access
• 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 security team of 1-3 people, they compete with the primary mission of detecting and responding to threats.

Operating SenseOn

SenseOn's operational model is built for teams that want decisions, not queues:

• Detection built in: analysts start from correlated cases; Hunter prepares hunt queries and evidence for a person to review
• Response from the same agent: remote access, one-click isolation and automatic isolation of a compromised Windows device at a threshold you set
• Governed AI: 92.5% of incidents are resolved by AI under human governance, measured across all customer environments over a rolling 30-day window; agents act inside policy you set, and every decision is recorded and reversible
• Speed: mean time to detect and respond is under 20 minutes across managed-service customer environments over a rolling 30-day window
• Priced on outcomes: data volume is one meter, not the whole bill

When Sentinel Is the Right Choice

Microsoft Sentinel is the stronger choice in specific scenarios. Where SenseOn can still help, we say so.

Largely Microsoft environments: If your organisation runs Microsoft 365, Azure AD, Azure infrastructure, Microsoft Defender for Endpoint and Defender for Cloud, with few non-Microsoft security tools, Sentinel's native integrations give low-friction ingestion with free or reduced-cost telemetry for Microsoft sources. SenseOn's answer: SenseOn has out-of-the-box Microsoft 365 integration and runs beside Defender, so it can add network and identity correlation without moving you off Microsoft.

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 capability.

General-purpose SIEM/SOAR requirement: If your organisation needs one platform ingesting dozens of diverse sources and running complex automation workflows, Sentinel's breadth and Logic Apps integration serve this well.

Regulatory requirement for a named SIEM: Some organisations must show auditors a named SIEM product. SenseOn's answer: deploy SenseOn alongside Sentinel. Decision Trace records every AI and analyst action in an append-only log, which gives chain-of-custody evidence for NIS2, DORA, the EU AI Act and ISO 27001.

When SenseOn Is the Right Choice

Your data lake is hiding your network: If you have moved firewall, DNS, proxy or flow logs to the Sentinel data lake to control cost, those sources no longer drive real-time detection. SenseOn captures network activity on the host, and detects threats on endpoint telemetry and the log sources you route to Detection & Response.

Cost predictability: Sentinel's bill moves with ingestion. SenseOn is an annual pool of Flex Intelligence Credits with committed minimums and storage priced on compressed data. Data volume is one meter, not the whole bill.

Limited analyst capacity: If your security team has 1-3 analysts, time spent on KQL rule authoring, Logic Apps and connector management is time not spent on threats. Across customer environments, about 97% of alert noise is removed before a queue and 92.5% of incidents are resolved by AI under human governance over a rolling 30-day window, so analysts work from far fewer cases.

SIEM replacement without a blind leap: Deploy alongside Sentinel, run both for a 14 to 28 day proof window, then redirect or retire only where the evidence is clear.

Mixed or non-Microsoft environments: If your estate spans several clouds, non-Microsoft tooling or on-premises workloads, the Universal Sensor runs on Windows, macOS and Linux. On Linux, network telemetry needs kernel 5.2 or later with eBPF; older kernels give process telemetry only, and a network probe covers segments such as OT where no agent can run.

Audit and AI accountability: Decision Trace gives 100% audit coverage of AI and analyst actions, as a structural guarantee rather than a sample.

Frequently Asked Questions

Is SenseOn a SIEM replacement for Microsoft Sentinel?

It can be. The recommended route is augment-first: run SenseOn alongside Sentinel before you retire anything. SenseOn provides detection, correlation, case investigation and response across endpoint, network and identity, with out-of-the-box integrations and log ingestion for other sources. Detection runs on endpoint telemetry and the log sources you route to Detection & Response. Customers run both for a 14 to 28 day proof window, then redirect or retire Sentinel sources only where the evidence supports it.

How do SenseOn and Sentinel compare on cost for a 2,000-endpoint deployment?

Sentinel's cost depends primarily on data volume: it is billed per GB ingested, $4.30 per GB pay-as-you-go at the East US list price on 23 September 2026, with commitment tiers from 100 GB a day. A 2,000-endpoint environment can generate enough log data to make ingestion the largest line on the bill, before automation and retention charges. Use Microsoft's cost estimator with your own volumes. SenseOn is priced as an annual pool of Flex Intelligence Credits. You pay for outcomes: detection and response across your data, analytics included, and the investigations its AI completes. Data volume is one meter, not the whole bill. Three levers bring the cost down: edge processing removes unnecessary data before the pipeline, Weave (SenseOn's security engineering agent) routes data down three pipelines with storage separated from compute, and ClickHouse compression means storage is priced on the compressed volume. Together they cut costs by about 40% against a traditional SIEM, and the price also covers the AI agents that investigate and resolve cases.

Why not keep Sentinel and move noisy logs to the data lake?

You can, but Microsoft says data lake tier data "isn't available for real-time analytics features and threat hunting", and moving a table from analytics to the lake means "all real-time analytics and hunting queries stop working". Detection on lake data then needs scheduled KQL jobs or summary rules you build: new data typically takes up to 15 minutes to become queryable, a job must start at least 30 minutes after it is created, a tenant can run 5 jobs at once with 100 enabled, and results are promoted to the analytics tier before a rule can alert. The noisy sources teams move, such as firewall, DNS and proxy logs, are often where lateral movement and command-and-control show. SenseOn runs behavioural analytics about every five minutes across every log source you route to Detection & Response, and its AI agents can query and hunt across every storage tier for the whole retention period.

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; non-Microsoft sources need data connectors, some of which run on separately billed Azure Functions. SenseOn does not need Azure: its sensor runs on Windows, macOS and Linux, and its edge processing runs in SenseOn cloud, a customer-hosted container, on-premise or via API.

Can SenseOn integrate with Microsoft 365 Defender?

Yes. SenseOn has out-of-the-box integrations with Microsoft 365. If you keep Defender for Endpoint, set SenseOn's antimalware to dual-running mode and add exclusions for SenseOn's install paths in Defender, and the two run side by side.

Which platform requires less analyst expertise to operate?

For a small team, usually SenseOn. Sentinel asks for KQL, Azure Logic Apps for automation playbooks, and ongoing management of analytics rules and data tiers, which suits teams with detection engineers. SenseOn starts from built-in detection and correlated cases. Across all customer environments, 92.5% of incidents are resolved by AI under human governance over a rolling 30-day window, and people keep the consequential decisions.

Sources

• Blake D. Bryant, Hossein Saiedian, "Improving SIEM alert metadata aggregation with a novel kill-chain based classification model", Computers & Security, 2020, https://doi.org/10.1016/j.cose.2020.101817
• Duc C. Le, Nur Zincir-Heywood, "Anomaly Detection for Insider Threats Using Unsupervised Ensembles", IEEE Transactions on Network and Service Management, 2021, https://doi.org/10.1109/TNSM.2021.3071928
• Fateneh Jalalvand, Mohan Baruwal Chhetri, Surya Nepal, Cécile Paris, "Alert Prioritisation in Security Operations Centres: A Systematic Survey on Criteria and Methods", ACM Computing Surveys, 2024, https://doi.org/10.1145/3695462 (open access)
• Microsoft, What is Microsoft Sentinel SIEM?, updated 28 January 2026, accessed 20 September 2026
• Microsoft, Plan costs and understand pricing and billing for Microsoft Sentinel, updated 1 April 2026, accessed 20 September 2026
• Microsoft Learn, Manage data tiers and retention in Microsoft Sentinel, updated 2 July 2026, accessed 23 September 2026
• Microsoft Learn, Create jobs in the Microsoft Sentinel data lake, updated 1 July 2026, accessed 23 September 2026
• Microsoft Learn, Aggregate Microsoft Sentinel data with summary rules, updated 2 July 2026, accessed 23 September 2026
• Microsoft, Microsoft Sentinel pricing, accessed 20 September 2026
• Microsoft, Azure Retail Prices API, Sentinel meters, East US, read 23 September 2026
• Microsoft, Threat detection in Microsoft Sentinel, accessed 20 September 2026
• Microsoft, Azure Logic Apps pricing, accessed 20 September 2026
• AV-Comparatives, Business Security Test 2024 (March - June), published 15 July 2024, accessed 23 September 2026
• CardinalOps, State of SIEM Detection Risk, 2025 edition, June 2025, accessed 23 September 2026

Related: Microsoft Sentinel Pricing Explained: Tiers, Meters, Examples

How SenseOn writes, checks and corrects its content: editorial standards.