Le 10 migliori piattaforme AIOps del 2026

Robin Autonomous IT is Atera's autonomous IT service agent that diagnoses and resolves device and cloud issues end-to-end, with AI-driven triage, escalation summaries, and continuous learning from past tickets to support IT technicians.

Who Is Robin Autonomous IT Best For?

Robin Autonomous IT is a good fit for IT teams and managed service providers that handle high volumes of recurring service desk requests and need to reduce manual resolution workload.

Why I Picked Robin Autonomous IT

I've included Robin Autonomous IT in my top picks because of how it handles conversational IT resolution end to end. When an employee submits a request via Teams or Slack, Robin detects the intent, asks follow-up questions to gather full context, and immediately confirms the request is in progress. What I find genuinely impressive is that it doesn't just triage; it verifies the user against identity providers like Okta and Azure AD, then executes the fix directly on the device or in the cloud without waiting for a technician to step in.

Robin Autonomous IT Key Features

• Playbook-driven resolution engine: Robin follows predefined playbooks to select and execute the correct resolution path based on the issue type detected.
• Escalation with context handoff: When Robin can't resolve an issue autonomously, it escalates to a technician with a full diagnostic summary already attached to the ticket.
• Performance reporting dashboard: Track resolution rates, total actions executed, and time saved across all autonomous interactions in a dedicated reporting view.
• Continuous learning engine: Robin analyzes resolved tickets over time and surfaces suggestions for new scripts, playbooks, and knowledge base articles based on recurring patterns.

Robin Autonomous IT Integrations

Robin offers built-in integrations with identity providers like Google, Azure AD, and Okta, and supports request intake through Microsoft Teams, Slack, and WhatsApp. It also connects to CRM tools like HubSpot and third-party ITSM platforms, with additional connectivity available through Cloud Actions, MCP, and Zapier.

Dataiku is an AI development and operations platform that covers the full lifecycle from data preparation and model training through to deployment, monitoring, and multi-agent orchestration.

Who Is Dataiku Best For?

Dataiku is a strong fit for enterprise data science and AI engineering teams that need a shared platform for managing the full model lifecycle across large, cross-functional organizations.

Why I Picked Dataiku

I've included Dataiku in my top picks because it's one of the few AIOps platforms that puts collaboration at the center of the data science workflow. Analysts, data scientists, and engineers can all work inside the same project environment, using no-code, low-code, or full-code approaches, without fragmenting the work across separate tools. I particularly like the shared Flow, which gives every team member visibility into the full data and model pipeline. Built-in experiment tracking and lineage documentation mean my team isn't losing context as projects move from exploration to production.

Dataiku Key Features

• Model monitoring and drift detection: Track live model performance and receive automated alerts when predictions deviate from expected behavior.
• LLM Mesh: Centrally manage LLM access across providers, with built-in cost controls, usage monitoring, and safety guardrails.
• Scenario automation: Schedule and trigger automated workflows for data pipelines, model retraining, and deployment steps without manual intervention.
• AI governance controls: Apply automated documentation, stress testing, and audit trails to models across their full production lifecycle.

Dataiku Integrations

Dataiku offers native connectors for Salesforce, Jira, Zendesk, Splunk, ServiceNow, Snowflake, Databricks, Google BigQuery, Amazon S3, and SharePoint Online, along with connectors for many additional SQL, NoSQL, and cloud storage platforms. Zapier support is not built in, but Dataiku provides a public REST API and a plugin framework for custom integrations.

Site24x7 is a cloud-based monitoring solution that leverages artificial intelligence to oversee your entire IT infrastructure. It provides real-time insights into the performance of websites, servers, networks, and applications.

Why I picked Site24x7: One thing I like is its advanced anomaly detection capabilities. The system continuously monitors critical performance metrics and identifies deviations from normal behavior, alerting you to potential issues before they escalate. This proactive approach ensures that your IT operations remain smooth and uninterrupted. It also offers predictive analysis. By analyzing historical data, the platform forecasts future trends in resource utilization, such as disk and memory space.

Site24x7 Standout Features and Integrations:

Features include automated incident remediation, which uses simple scripts to resolve issues without manual intervention, reducing downtime and operational costs. The platform also offers data visualization and business intelligence tools, organizing critical data into charts, dashboards, and reports for quick analysis and troubleshooting.

Integrations include ServiceNow, PagerDuty, Opsgenie, Jira, ManageEngine AlarmsOne, ManageEngine ServiceDesk Plus, Slack, Microsoft Teams, Zoho Cliq, Amazon EventBridge, Zapier, and Webhooks.

Coralogix is a log management platform that helps organizations analyze their data at scale and ensure end-to-end security with automated incident response.

Why I picked Coralogix: I put Coralogix on this list because it automates posture assessments to ensure compliance with data security standards like SOC, ISO, and HIPAA. The TCO Optimizer feature lets you designate certain types of logs as compliance data.

Coralogix Standout Features and Integrations:

Features that differentiate Coralogix from other AIOps tools, in my opinion, are its automated vulnerability assessments. This feature uses AI to continuously monitor your log data and detect known security vulnerabilities; if it detects a vulnerability, it’ll display a description of the issue and estimate its severity so you can prioritize and remediate appropriately. I also like that it has 24/7 support built right into the app.

Integrations include over 100 native options to platforms and services like AWS, CircleCI, Jenkins, Microsoft Azure, PagerDuty, Perimeter 81, Cloudflare, and NXLog. You can use its REST APIs to connect to more applications.

PagerDuty is an infrastructure monitoring platform that leverages AI and ML to help companies stay ahead of critical issues and reduce manual processes.

Why I picked PagerDuty: Aggregating data across all the platforms and services you use isn’t easy, which is why I put PagerDuty on this list. It includes native integrations with over 700 tools and step-by-step instructions for popular platforms like AWS. You can also use its Events API v2 to ingest and process different types of event data.

PagerDuty Standout Features and Integrations:

Features that I feel make PagerDuty stand out from other AIOps tools include its intelligent alert grouping, which uses ML to group related alerts into a single incident. This helps reduce alert noise and speed up resolution times. Automation Actions is another feature I found helpful as it allows you to create automated incident response workflows to common issues; however, I should point out that this feature is only available as a paid-on.

Integrations are available natively for over 700 platforms, including AWS, ServiceNow, Salesforce, Zendesk, Atlassian, and Datadog.

LogicMonitor is a cloud-based infrastructure monitoring platform with AIOps capabilities that help organizations prevent outages and streamline their operations.

Why I picked LogicMonitor: I put LogicMonitor on this list for its anomaly detection. The system applies machine learning to historical data to detect anomalies outside normal patterns. Dynamic thresholds ensure that you only receive alerts for critical issues.

LogicMonitor Standout Features and Integrations:

Features that I think make LogicMonitor stand out from other AIOps platforms include its data forecasting tool, which uses historical data to make predictions about your infrastructure. For example, you can use it to anticipate when the disk space on a server will run out. These insights can help you take a more proactive approach to preventing outages.

Integrations are available natively for over 2,000 platforms. Notable integrations include Microsoft Azure, Google Cloud Platform, VMware, AWS, ServiceNow, Citrix, Fortinet, Java, MySQL, and ConnectWise.

ServiceNow is a cloud-based IT operations management platform. It leverages AI to simplify data ingestion and automate incident resolution.

Why I picked ServiceNow: I put ServiceNow here because it offers an array of features that can help organizations manage and optimize their IT infrastructure. It can automatically discover applications and map their dependencies, detect and remediate anomalies, and even optimize resources to reduce cloud spend.

ServiceNow Standout Features and Integrations:

Features that I think make ServiceNow worth considering include its predictive AIOps capabilities, which can instantly identify anomalies as they occur. The platform also offers pre-built actions that you can apply to alerts and speed up remediation. I also found its service health dashboards helpful for understanding which applications were at risk.

Integrations are available natively for platforms like AWS, Google Cloud Platform, Microsoft Azure, Citrix, Okta, Jira, and SAP. You can also use ServiceNow’s REST APIs to integrate with more applications.

New Relic is a network observability platform that allows you to monitor the health of your infrastructure. It automatically detects anomalies and correlates incidents to streamline troubleshooting.

Why I picked New Relic: I picked New Relic because it does an excellent job at cutting through the “noise” and minimizing false positives. The platform uses an AI-powered correlation engine that reviews incidents and groups related issues to create a single alert. You can easily configure workflows to notify and provide relevant context to the right people so they can get straight to work.

New Relic Standout Features and Integrations:

Features that make New Relic stand apart include its issues feed, which provides a clear overview of the issues the system detected. I could drill down into each issue and get specifics like issue duration, entity type, and user actions. I also found its “postmortem” feature useful as it provided insights into what worked and what didn’t when responding to an incident.

Integrations include native options for various platforms and systems, including Amazon ECS, Elasticsearch, Synk, Kubernetes, Azure Batch, Google BigQuery, Kamon, and Comet.

Elastic Observability unifies log data, application traces, and infrastructure metrics in one location. It uses AI to automate anomaly detection and reduce manual troubleshooting.

Why I picked Elastic Observability: Pinpointing the root cause of an issue isn’t easy unless you’re willing to scour through large volumes of data. I picked Elastic Observability because it offers an array of preconfigured ML models that can detect anomalies and automate root cause analysis. You can apply these models to all types of application and infrastructure data.

Elastic Observability Standout Features and Integrations:

Features that I want to highlight about Elastic Observability include its library of out-of-the-box integrations that enable you to ingest data from any source, like applications, endpoints, and servers. Another noteworthy feature is the machine learning wizard. The tool walks you through each step, so you can “train” the system and create your own anomaly detection models even if you have little technical expertise.

Integrations are available natively for various platforms, including AWS S3, Azure Logs, GitHub, PagerDuty, Slack, ServiceNow, and Fortinet.

Splunk is an observability platform that uses AI and ML to detect anomalies and automate incident response across hybrid environments.

Why I picked Splunk: I chose Splunk because it offers a robust ecosystem of apps and integrations that enable multi-cloud enterprises to gain full visibility of their infrastructure. Its service-level dashboards allow you to drill down to the code level to make troubleshooting issues more effective.

Splunk Standout Features and Integrations:

Features that impressed me about Splunk include its predictive analytics tool, which uses ML algorithms to predict what the health score of a service may look like in 30 minutes. With these models, I could determine which services required immediate attention before an outage occurs. I also like that Splunk lets you create and add templates to standardize incident responses.

Integrations include pre-built and native applications that are available via Splunkbase — a marketplace of apps and add-ons for Splunk.