Managing modern IT systems without AIOps is like flying a commercial jet without autopilot. You’ve got endless dials, gauges, and warning lights — each demanding constant attention. A human pilot can only track so much, and one missed signal could cascade into turbulence. With AIOps, the autopilot kicks in: analyzing thousands of inputs in real time, correcting course instantly, and alerting the crew only when true intervention is needed.

In this guide, we’ll cover what AIOps is, what AIOps platforms do, why finance teams need them, the key benefits, and the 11 best AIOps tools in 2025 for finance-specific needs.

What Is AIOps?

AIOps, short for Artificial Intelligence for IT Operations, applies machine learning and big data analytics to automate how organizations manage IT infrastructure. It ingests logs, metrics, and events from diverse systems, then detects patterns, reduces noise, and predicts incidents before they cause disruption. By offloading repetitive monitoring and troubleshooting tasks, AIOps reduces manual work and frees teams to focus on higher-value initiatives. Unlike traditional monitoring, which simply reports issues after they occur, AIOps is proactive — continuously learning and adapting to changes in scale and complexity.

Image source: Pure Storage

What Does An AIOps Platform Do?

Modern IT environments generate massive amounts of data across logs, metrics, and events. For DevOps teams, this flood makes it harder to deliver software quickly without being bogged down in monitoring. For FinOps teams, it obscures the connection between system performance and cost.

An AIOps platform cuts through the noise by applying machine learning and automation, turning raw data into actionable intelligence for DevOps and FinOps aims.

Data Collection and Aggregation

An AIOps platform pulls data from diverse sources — infrastructure logs, application metrics, monitoring tools, and cloud services — and unifies it into a single system. Instead of engineers manually chasing disconnected dashboards, the platform aggregates, normalizes, and correlates information at scale. This centralization enables faster insights and eliminates the wasted effort of data wrangling.

Noise Reduction

In large systems, constant alerts can overwhelm teams. AIOps reduces this “noise” by correlating related events, suppressing duplicates, and filtering out low-priority signals. By cutting through the clutter, it ensures engineers only see what actually matters, preventing alert fatigue and allowing faster focus on true issues.

Anomaly Detection

By comparing historical patterns with real-time indicators, AIOps detects unusual behavior before it becomes a problem. Whether it’s a sudden spike in costs, latency, or CPU usage, the platform flags anomalies early. This enables proactive intervention, avoiding the downtime or overspending that traditional monitoring often misses.

Predictive Analysis

AIOps doesn’t just react to issues — it forecasts them. Using machine learning, it can identify trends that point to future problems, such as resource bottlenecks or scaling inefficiencies. These predictive insights give IT and finance teams time to address challenges before they escalate, improving both reliability and cost management.

Root Cause Analysis (RCA)

When something breaks, finding the real cause can take hours. AIOps accelerates RCA by analyzing correlated data across systems, surfacing the most likely source of an issue. Instead of engineers digging through logs manually, the platform pinpoints the root cause quickly, reducing mean time to resolution (MTTR).

Monitoring At Scale

Traditional monitoring tools struggle with the complexity of hybrid and multi-cloud systems. AIOps handles monitoring at massive scale, processing thousands of signals and incidents simultaneously. It delivers real-time observability across infrastructure and applications, ensuring teams always have up-to-date insights into system health.

Automated Incident Response

Beyond detection, AIOps platforms can take direct action. Routine tasks like restarting services, scaling resources, or rerouting traffic can be automated, minimizing downtime without human intervention. This automation reduces manual workload, speeds up recovery, and frees teams to focus on strategic priorities instead of firefighting.

Why Finance Teams Need AIOps

AIOps isn’t just for DevOps — it’s equally key for finance teams who often struggle to get timely, accurate answers about cloud costs. Traditional reports often arrive late, tagging is incomplete, and manual spreadsheets slow down COGS and budgeting. That makes it difficult to forecast confidently or hold teams accountable for spend.

AIOps addresses these gaps by connecting infrastructure performance directly with financial data. 

Key reasons finance teams benefit from AIOps:

  • Real-time visibility into drivers: Understand which accounts, workloads, or business units are responsible for spend.

  • Anomaly alerts framed in dollars: Get notified when costs deviate unexpectedly, not just when systems behave oddly.

  • Faster, more reliable COGS: Automate the link between infrastructure data and financial reporting for cleaner unit economics.

  • Stronger forecasting inputs: Tie usage trends to financial models, reducing guesswork in budgets and forecasts.

  • Collaboration on ROI: Provide finance and engineering with the same source of truth so investments can be judged on business impact, not just technical performance.

11 Best AIOps Tools for Finance Teams in 2025

You don’t need to build an AIOps platform from scratch to get value. The AIOps platforms below are ready-made options that combine automation, anomaly detection, and financial insight — giving finance teams immediate visibility into cloud spend and the drivers behind it.

1. nOps – Best AIOps Tool for Finance Teams

nOps is built specifically with FinOps in mind, making it a natural choice for finance leaders who need cost clarity alongside operational intelligence. 

Unlike general-purpose AIOps platforms, which focus mainly on uptime and performance, nOps helps you optimize both performance and cost simultaneously. AI-powered automation ensures both finance and engineering teams have the tools they need to track, forecast, and optimize spend in real time.

nOps EKS Insights Dashboard

nOps Features for Finance Teams:

  1. AI-Powered Automation & Optimization – Continuously rightsizes workloads, eliminates waste, and optimizes commitments to reduce costs without manual intervention.

  2. Intelligent Forecasting & Anomaly Detection – Predicts spend trends and surfaces unexpected cost spikes before they disrupt budgets.

  3. Automated Recommendations & Actions – Provides context-rich guidance for savings and can auto-apply optimizations to cut down repetitive work.

  4. FinOps Agent “Clara” – Acts as a co-pilot, answering financial questions, surfacing cost insights, and automating FinOps tasks.

  5. AI-Assisted Migration Planning – Helps evaluate cloud migrations (including AI/ML workloads) with cost, efficiency, and ROI modeling for finance stakeholders.

nOps was recently ranked #1 in G2’s Cost Management category – you can give it a try for free.

2. Dynatrace

Dynatrace offers full-stack observability with built-in AI. Its standout feature is Davis AI, a causal engine that automatically maps dependencies and explains the “why” behind issues. Unlike tools that rely on manual thresholds, Dynatrace continuously learns from your environment, delivering precise root cause analysis across apps, infrastructure, and cloud services at enterprise scale.

Key AIOps Monitoring Tools features:

  • Davis AI for causal root cause analysis

  • Automatic service and dependency mapping

  • Predictive analytics for capacity and performance

  • End-to-end observability across hybrid and multi-cloud

3. Datadog

Datadog is best for combining observability and AIOps in a single cloud-native platform. Its Watchdog engine applies machine learning across metrics, logs, and traces to automatically detect anomalies and correlate alerts. Unlike tools that require heavy setup, Datadog provides out-of-the-box insights, making it a strong choice for multi-cloud teams that need quick time to value.

Key AIOps Features:

  • Watchdog AI for real-time anomaly detection

  • Automated alert correlation to cut down on noise

  • Unified view of metrics, logs, and traces across environments

  • Continuous learning from historical and live data patterns

  • Pre-built integrations with hundreds of services and cloud platforms

4. AppDynamics

AppDynamics: Cloud Observability and Application Performance Management

AppDynamics focuses on connecting application performance with business outcomes. As part of Cisco’s AIOps suite, it extends beyond traditional APM by using AI to detect anomalies, correlate them with business transactions, and trigger automated responses. Unlike many tools, it focuses heavily on the end-user journey, mapping how performance issues translate into customer and revenue impact.

Key AIOps Features:

  • AI-driven anomaly detection with automated root cause analysis

  • Real-time application performance monitoring across distributed systems

  • Experience Journey Mapping to visualize user paths and friction points

  • Correlation of technical metrics with business KPIs for impact analysis

  • End-to-end visibility across applications, infrastructure, and third-party services

5. New Relic

New Relic is best for integrating AIOps directly into a unified telemetry database that serves as the backbone for its platform. The NRDB ingests billions of data points from metrics, logs, and events in real time, allowing AI models to correlate incidents, suppress noise, and surface only the most relevant alerts. Unlike many tools that layer AI onto monitoring, New Relic treats AIOps as a core function. This makes it especially approachable for teams that want intelligent automation without heavy configuration or data science expertise.

Key AIOps Features:

  • NRDB telemetry database for unified data ingestion and analysis

  • Intelligent incident correlation with context-rich alerts

  • Automated detection of coverage gaps and recommendations for new alerts

  • Real-time anomaly detection across metrics, logs, and events

6. BigPanda

BigPanda is geared torwards event correlation and incident management at scale. It specializes in reducing alert overload by automatically linking related events across monitoring tools, cutting noise and helping teams focus on true problems. Its “Open Box AI” approach also makes its decision-making transparent, giving enterprises more trust and control compared to black-box AIOps platforms. 

Key AIOps Features:

  • Advanced event correlation to reduce alert noise by up to 95%

  • Machine learning–driven root cause analysis for faster resolution

  • Automated incident workflows to shorten mean time to resolution (MTTR)

  • “Open Box AI” for transparency and control over AI decisions

  • Flexible integrations with existing monitoring and ITSM tools

  • Scalable architecture designed for complex enterprise environments

7. PagerDuty

Among AIOps management tools, PagerDuty is best for incident response and real-time operations management. It takes existing monitoring alerts and turns them into actionable workflows, ensuring the right people are notified instantly. Unlike broader AIOps platforms, PagerDuty focuses on response speed, with automation designed to triage, escalate, and resolve issues without delay.

Key AIOps Features:

  • Intelligent alerting with noise reduction and smart routing

  • Automated incident response workflows for faster resolution

  • Predictive analytics to anticipate recurring incidents

  • Over 700 integrations for seamless connection to monitoring and collaboration tools

  • Flexible escalation policies to ensure accountability and coverage

8. Splunk

Splunk combines observability, security, and AIOps into a single enterprise platform. Known for its IT Service Intelligence (ITSI) module, Splunk applies machine learning to vast streams of logs and metrics, helping teams prioritize incidents, predict outages, and automate remediation. Unlike lighter-weight tools, Splunk’s strength is depth: it scales across massive, complex environments where both security and performance must be monitored in real time.

Key AIOps Features:

  • IT Service Intelligence (ITSI) for prioritizing incidents based on business impact

  • Predictive analytics to identify and prevent service degradations

  • Automated incident response to reduce downtime and manual effort

  • Customizable dashboards for cross-team visibility into performance and risk

  • Integration of security and operations data for unified monitoring

9. CloudZero

CloudZero is best for unit cost visibility and cost anomaly detection rather than broad IT operations. Its strength lies in mapping cloud spend to features, products, or customers, giving engineering and finance teams more granular financial insights. Unlike full-stack AIOps platforms, CloudZero doesn’t provide deep observability or infrastructure automation — it’s more of a cost-focused overlay than an operational backbone.

Key AIOps Features:

  • Cost anomaly detection with context-rich alerts

  • Telemetry-driven mapping of spend to features or customers

  • Unit economics insights such as cost per request or cost per environment

  • Centralized cost visibility across AWS, Kubernetes, and other services

10. LogicMonitor

LogicMonitor offers infrastructure monitoring with a layer of AIOps to cut alert noise and predict issues. Its agentless collectors make it easier to deploy than many competitors, and the Early Warning System uses machine learning to flag anomalies before they impact availability. While it provides strong coverage for hybrid and multi-cloud environments, LogicMonitor is more monitoring-first than automation-first.

Key AIOps Features:

  • Early Warning System for predictive anomaly detection

  • Agentless collectors for streamlined deployment and scaling

  • Automated event correlation to reduce alert fatigue

  • Hybrid and multi-cloud infrastructure monitoring at scale

11. Zenoss Cloud

Zenoss Cloud unifies diverse monitoring data into a single AIOps platform. It ingests metrics, logs, events, and dependency data, then applies machine learning to detect anomalies and automate responses. Unlike point solutions that focus on one data type, Zenoss emphasizes breadth, aiming to provide real-time observability across hybrid and multi-cloud infrastructures.

Key AIOps Features:

  • Ingestion and correlation of metrics, logs, events, and dependency data

  • Real-time anomaly detection to catch issues before they escalate

  • Automated incident response workflows to reduce downtime

  • Dependency mapping for faster root cause analysis

  • Continuous learning from operational data to improve predictive accuracy

Bring FinOps intelligence to your AIOps strategy

With nOps, optimization isn’t limited to one layer. The platform unifies every dimension of cloud efficiency — usage, price, and architecture — to maximize savings. Benchmarking shows where you stand against peers, with anomaly detection and automated recommendations to make it easy to cut waste.

nOps features are purpose-built for Finance and Engineering, with the FinOps AI Agent (Clara) tailoring insights to your role. Finance leaders get instant answers for forecasting and COGS, while engineers receive guidance on rightsizing and performance — which can be applied by Clara with one click.

The result: more optimization, less friction, and a stronger return on every dollar spent in the cloud. Schedule a demo to see for yourself.

Frequently Asked Questions

What is the difference between AI and AIOps?

AI refers broadly to systems that simulate human intelligence for tasks like prediction, classification, or language processing. AIOps is a specialized application of AI for IT operations, using machine learning and analytics to automate monitoring, anomaly detection, and incident response across complex systems, improving reliability and reducing manual overhead.

What is AIOps and MLOps?

AIOps applies artificial intelligence to IT operations, enabling automated issue detection, root-cause analysis, and remediation. MLOps, on the other hand, manages the full machine learning lifecycle—model development, deployment, monitoring, and retraining. While AIOps optimizes system performance, MLOps ensures ML models stay accurate and reliable across changing data environments.

What is the difference between AIOps and DevOps?

DevOps focuses on collaboration between development and operations teams, emphasizing automation in software delivery pipelines. AIOps enhances operations by using AI to process massive telemetry data, detect anomalies, and trigger automated actions. In practice, DevOps accelerates code delivery, while AIOps ensures the underlying systems remain stable, scalable, and self-healing.