The best AIOps platform depends on where your incident workflow slows down. Some tools reduce alert noise before responders are paged; others detect anomalies within an observability suite or investigate root cause across an existing stack. These six AIOps software options address different parts of that workflow.
AIOps platforms compared
| Platform | Best fit | Main strength |
|---|---|---|
| Sherlocks AI | Cross-tool SRE investigation | Slack-native investigation with service and historical context |
| BigPanda | Enterprise IT operations and ServiceNow | Event correlation and context-rich incidents |
| PagerDuty AIOps | On-call alert noise | Alert grouping, suppression, and routing |
| APEX AIOps Incident Management | Multiple monitoring tools | Event deduplication and alert-to-incident correlation |
| Datadog AIOps | Datadog-based observability | Anomaly detection and investigation in one platform |
| Selector AI | Network and hybrid infrastructure | Topology-aware anomaly and event correlation |
1. Sherlocks AI — cross-tool investigation for SRE teams
- Best for: Teams with an existing monitoring stack that need faster incident investigation across tools.
- Starting points: Sherlocks AI can investigate from an alert, a Slack request, or a support ticket.
- Signals analyzed: Logs, metrics, traces, service dependencies, deployments, code changes, and prior incidents.
- AIOps capabilities: Alert classification, related-signal analysis, anomaly detection, and gradual degradation detection.
- Responder output: A Slack investigation with affected services, a timeline, likely root cause, supporting evidence, and recommended next steps.
- Deployment fit: In-VPC options are available for teams with tighter data-control requirements.
- Consideration: Evaluate paging, bulk event management, and full ITSM needs separately.
2. BigPanda — enterprise event correlation and ITSM
- Best for: Enterprise IT operations teams receiving high alert volumes from fragmented monitoring systems.
- Signals analyzed: BigPanda combines monitoring alerts with change and topology data.
- Core capability: Correlates related signals into fewer incidents and enriches them with operational context.
- ITSM fit: Its ServiceNow integration helps teams send fewer duplicate tickets and more useful incident information to the service desk.
- Additional use: Incident intelligence and L1 triage across a complex, multi-tool environment.
- Consideration: Connected monitoring and observability products still supply the underlying telemetry, including deep APM, logs, and traces.
3. PagerDuty AIOps — alert noise reduction for on-call teams
- Best for: Teams dealing with duplicate pages, alert fatigue, and difficult incident routing.
- Core capability: PagerDuty AIOps groups related alerts within and across services.
- Noise controls: Suppression and event orchestration help determine which signals create incidents and where those incidents go.
- Responder context: Related incidents and recent changes help on-call teams triage a page.
- Workflow fit: Particularly useful when the team already coordinates on-call response in PagerDuty.
- Consideration: Check what diagnostic evidence your monitoring integrations provide if responders need investigation across logs, traces, code, and infrastructure.
4. APEX AIOps Incident Management — deduplication across monitoring tools
- Best for: Teams that want event correlation over several existing monitoring products.
- Core capability: APEX AIOps Incident Management, formerly Moogsoft, deduplicates repeated events into alerts and groups related alerts into incidents.
- Correlation controls: Teams can configure the fields, similarity requirements, and time windows used to form incidents.
- Other capabilities: Metric anomaly detection, event enrichment, incident views, and outbound workflows.
- Workflow fit: Incident information can be shared with tools such as ServiceNow, PagerDuty, and Slack.
- Consideration: Test correlation with real alert patterns, including noisy periods and incidents spanning multiple sources.
5. Datadog AIOps — anomaly detection inside an observability suite
- Best for: Teams whose metrics, logs, traces, and monitors already live in Datadog.
- Detection: Datadog uses Watchdog to surface unusual behavior across the data available in its platform.
- Event workflow: Event management connects operational signals with alerting and incident response.
- Investigation: Bits Investigation can examine supported alerts using available Datadog metrics, logs, traces, events, and other context.
- Why it fits: Teams can investigate anomalies alongside the observability data they already collect.
- Consideration: If important evidence lives in other products, verify which third-party sources the investigation can access.
6. Selector AI — network AIOps for hybrid infrastructure
- Best for: Network and infrastructure teams operating across devices, links, data centers, services, and cloud environments.
- Signals analyzed: Selector AI works across operational telemetry, configurations, flows, and topology.
- Detection and correlation: Identifies anomalies and groups related events using network and infrastructure relationships.
- Incident context: Helps distinguish an underlying device or link fault from alerts generated by dependent systems.
- Investigation fit: Topology context and natural-language tools help responders narrow an alert storm to affected components and a likely cause.
- Consideration: Teams focused mainly on application-level incidents may find a closer fit in application observability or SRE investigation software.