What is automated root cause analysis in AIOps platforms?

Automated root cause analysis uses AI to identify the underlying source of incidents by correlating metrics, logs, and traces across your infrastructure. The best platforms provide structured explanations and suggested remediation steps, reducing the time engineers spend on manual investigation.

Which AIOps platforms offer truly free root cause analysis?

Aurora by Arvo AI (fully free, open source Apache 2.0), OpenObserve (free self-hosted), Grafana Cloud (free tier with 10k metrics), and Metoro (free Hobby plan) offer free RCA capabilities. Note that PagerDuty's free tier does NOT include RCA — it requires the AIOps add-on at $699+/month.

How quickly can organizations implement AIOps for root cause analysis?

Most teams can pilot AIOps RCA within days. Aurora can be deployed via Docker Compose in under 30 minutes. Cloud platforms like Datadog and New Relic offer free tiers for immediate experimentation. Full production implementation varies by complexity and integration requirements.

What is the difference between deterministic and agentic AI for RCA?

Deterministic AI (like Dynatrace Davis AI) consistently produces the same output for the same input — it maps topology and correlates known patterns. Agentic AI (like Aurora) autonomously decides what to investigate, dynamically selects tools, and executes commands to gather new data during each investigation.

What limitations should I expect from free AIOps RCA tools?

Free tiers typically limit data retention (Datadog: 1 day, Grafana: 14 days), number of hosts/users, or exclude advanced features. Open source tools like Aurora have no feature limits but require self-hosted infrastructure. Always verify what RCA capabilities are actually included in the free plan.

Top 10 AIOps Platforms Offering Free Root Cause Analysis in 2026

Key Takeaway: AIOps platforms now compete on the quality of AI-driven root cause analysis and the accessibility of free or open source entry points. Whether you need a full enterprise observability suite or a focused open source investigation tool, there's a platform with a free starting point for your team.

AIOps — Artificial Intelligence for IT Operations — combines AI/ML algorithms with big data analytics to automate IT operations and incident response across cloud and hybrid environments. In 2026, the landscape has matured significantly: platforms now offer autonomous investigation, deterministic AI, and agentic workflows that go far beyond basic alert correlation.

This guide covers the 10 best AIOps platforms that offer free root cause analysis capabilities — either through free tiers, open source licenses, or trial access.

Quick Comparison

Platform / Type / Free Access / RCA Approach / Best For

Aurora by Arvo AI — Open source (Apache 2.0) — Free forever (self-hosted) — Alert correlation + AI summarization + agentic autonomous investigation — SRE teams needing the full AIOps workflow in one free tool
Dynatrace — Enterprise SaaS — 15-day trial — Deterministic AI (Davis AI) — Large enterprises with complex microservice architectures
Datadog — SaaS — Free tier (5 hosts) — Watchdog anomaly detection — Teams wanting unified observability with easy onboarding
New Relic — SaaS — Free tier (100 GB/month) — Applied Intelligence — Organizations seeking usage-based pricing flexibility
OpenObserve — Open source (AGPL-3.0) — Free forever (self-hosted) — Log/metric/trace analytics — Cost-conscious teams needing petabyte-scale observability
Splunk ITSI — Enterprise SaaS — Trial available — Predictive ML analytics — Enterprises with heavy log volumes and existing Splunk investment
Grafana Cloud — SaaS + Open source — Free tier (10k metrics) — ML-powered Sift diagnostics — Teams already using the Grafana/Prometheus stack
Metoro — SaaS — Free tier (1 cluster) — AI SRE for Kubernetes — Kubernetes-native teams wanting automated deployment verification
BigPanda — Enterprise SaaS — Demo only — Open Box ML correlation — Large IT ops teams drowning in alert noise
PagerDuty — SaaS — Free tier (5 users) — AIOps add-on (paid) — Teams needing on-call + incident coordination

1. Aurora by Arvo AI

Aurora covers the full AIOps investigation workflow — from alert correlation and incident summarization all the way to autonomous multi-step root cause analysis. When alerts fire, Aurora's AlertCorrelator groups related alerts into incidents, generates AI summaries, and then triggers autonomous agents that query your cloud infrastructure directly.

How Aurora does RCA:

Alert correlation — groups related alerts into incidents by service and time proximity (AlertCorrelator service)
AI incident summarization — generates structured summaries with context and suggested next steps
Autonomous multi-step investigation — LangGraph-orchestrated agents dynamically select from 30+ tools per investigation
Executes kubectl, aws, az, gcloud commands in sandboxed Kubernetes pods (non-root, read-only filesystem, seccomp enforced)
Queries cloud APIs directly — AWS (STS AssumeRole), Azure (Service Principal), GCP (OAuth), OVH, Scaleway
Traverses Memgraph infrastructure dependency graph for blast radius analysis
Searches Weaviate knowledge base via vector search over runbooks and past postmortems
Generates structured RCA with timeline, evidence citations, and remediation steps
Suggests code fixes with diff preview — human approves and creates PR
Auto-generates postmortems exportable to Confluence and Jira

Free access: Completely free. Apache 2.0 open source, self-hosted via Docker Compose or Helm chart. No per-seat pricing, no usage limits. Use any LLM provider including Ollama for local models.

Integrations: 25+ verified — PagerDuty, Datadog, Grafana, New Relic, Dynatrace, Splunk, BigPanda, Kubernetes, Terraform, GitHub, Confluence, Slack, and more.

Best for: SRE teams that need a single free platform covering alert correlation, AI summarization, AND deep autonomous cloud investigation — without paying for three separate tools.

"We built Aurora to cover the full investigation workflow. It correlates alerts, summarizes incidents, then actually queries your AWS accounts, checks your Kubernetes pods, and traces the dependency chain — all autonomously." — Noah Casarotto-Dinning, CEO at Arvo AI

git clone https://github.com/Arvo-AI/aurora.git
cd aurora
make init && make prod-prebuilt

2. Dynatrace

Dynatrace is an enterprise observability leader powered by its Davis AI engine, which uses deterministic AI for precise root cause identification.

RCA approach: Deterministic AI that consistently produces the same result for the same input — as opposed to probabilistic models that may vary. Davis AI continuously auto-discovers your infrastructure and maps dependencies across microservice architectures.

Free access: 15-day free trial plus a public sandbox environment. No permanent free tier.

Pricing: Usage-based. Infrastructure monitoring starts at $7/month per host (Foundation), $29/month (Infrastructure Monitoring), $58/month (Full-Stack).

Strengths: Deep auto-discovery, topology mapping, precise deterministic RCA. Limitations: Enterprise-oriented pricing, complex configuration for advanced features.

Best for: Large enterprises with complex microservice architectures needing precise, repeatable RCA.

3. Datadog

Datadog provides a comprehensive observability ecosystem with a generous free tier for experimentation.

RCA approach: Watchdog — an AI engine that continuously analyzes billions of data points for automatic anomaly detection, root cause analysis, and contextual insights across metrics, logs, traces, and security data.

Free access: $0 free tier for Infrastructure Monitoring — up to 5 hosts with 1-day metric retention.

Pricing: Pro starts at $15/host/month (billed annually). Modular pricing across 20+ products.

Strengths: Unified platform, easy onboarding, broad integration ecosystem. Limitations: Costs can scale quickly with multiple products and high cardinality.

Best for: Teams wanting unified cloud monitoring with AI-assisted incident detection and easy experimentation via the free tier.

4. New Relic

New Relic offers telemetry-centric observability with built-in AI for incident analysis.

RCA approach: Applied Intelligence — an AI module that deduplicates alerts, correlates incidents, and pinpoints root causes across cloud-native infrastructure using ML.

Free access: Free tier includes 100 GB/month data ingest, 1 full platform user, and 50+ capabilities. Usage-based pricing allows low-risk adoption.

Pricing: Usage-based — pay for data ingested and number of users.

Strengths: Flexible pricing, full-stack observability, large integration library. Limitations: Advanced AI features may require higher tiers.

Best for: Organizations seeking flexible, usage-based pricing with built-in AI for alert correlation and incident analysis.

5. OpenObserve

OpenObserve is an open source observability platform built in Rust for high-performance log, metric, and trace analytics.

RCA approach: Analytics-driven observability — fast search and correlation across logs, metrics, and traces. Not agentic AI, but provides the data foundation for manual or scripted RCA.

Free access: Fully open source under AGPL-3.0. Self-hosted is free forever with unlimited users. Cloud plan also offers a free tier. Self-hosted Enterprise is free up to 200 GB/day ingestion.

Strengths: Claims 140x lower storage cost vs Elasticsearch. Petabyte-scale. Written in Rust for performance. Limitations: Observability platform, not a dedicated AIOps/RCA tool. Requires engineering effort for investigation workflows.

Best for: Cost-conscious engineering teams needing high-performance observability as a foundation for RCA.

6. Splunk ITSI

Splunk ITSI (IT Service Intelligence) is an enterprise AIOps platform for organizations with heavy log volumes.

RCA approach: ML-powered predictive analytics — uses machine learning and historical data to detect future service degradations. Includes automated event aggregation with out-of-the-box ML policies and alert correlation.

Free access: Trial available. No permanent free tier.

Pricing: Not publicly listed. ITSI is a premium add-on requiring a base Splunk Enterprise or Cloud license. Widely considered one of the most expensive options in the AIOps space — costs scale significantly with data volume.

Strengths: Predictive alerting, deep service-level insights, mature ML capabilities. Limitations: Significant cost at scale, proprietary query language (SPL), complex implementation.

Best for: Mid-to-large enterprises with existing Splunk investment and heavy log volumes.

7. Grafana Cloud

Grafana Cloud extends the popular open source Grafana ecosystem with cloud-hosted observability and ML-powered diagnostics.

RCA approach: ML-powered Sift for automated diagnostics, plus Correlations features that create interactive links between data sources. Application Observability auto-correlates metrics, logs, and traces.

Free access: Permanent free tier — 10,000 active metric series/month, 50 GB logs/traces/profiles, 3 active users, 14-day retention. No credit card required.

Strengths: Strong community, extensible with thousands of dashboards and plugins, works with Prometheus/Loki/Tempo natively. Limitations: Operational tuning may be required for effective RCA at scale. ML features are newer additions.

Best for: Teams already using the Grafana/Prometheus stack who want cloud-hosted ML-powered diagnostics.

8. Metoro

Metoro is a developer/SRE-focused AIOps platform built specifically for Kubernetes environments.

RCA approach: AI SRE for Kubernetes — autonomous deployment verification, AI issue detection, root cause analysis, and remediation suggestions. Uses eBPF for telemetry collection.

Free access: Hobby plan — free forever, includes 1 cluster, 1 user, 2 nodes, 200 GB ingested/month.

Strengths: Kubernetes-native, automated deployment verification, APM + log management + infrastructure monitoring in one. Limitations: Focused on Kubernetes — less suitable for non-containerized environments.

Best for: Kubernetes-native teams wanting an AI SRE that automates deployment verification and incident investigation.

9. BigPanda

BigPanda specializes in transparent, explainable ML-based event correlation for large IT operations teams.

RCA approach: Open Box Machine Learning (OBML) — transparent ML where users can examine automation logic in plain English, edit it, and preview before deploying. Correlates alerts across time, topology, context, and alert type. Claims 95%+ IT noise reduction.

Free access: No free tier or self-serve trial. Access through demo requests and sales engagement.

Strengths: Transparent/explainable AI (not black box), massive noise reduction, customizable correlation rules. Limitations: Enterprise-only, no self-serve access, requires sales engagement.

Best for: Large IT ops teams drowning in alert noise who need transparent, customizable AI correlation.

10. PagerDuty

PagerDuty is the industry standard for incident response and on-call coordination, with AIOps capabilities available as add-ons.

RCA approach: AIOps add-on provides alert noise reduction (claims 91% reduction), intelligent correlation, and "Probable Origin" for root cause suggestions. Note: RCA features are not included in the free tier — they require the AIOps add-on ($699+/month) on top of a paid plan.

Free access: Free tier includes up to 5 users, 1 on-call schedule, basic incident management, and 700+ integrations. Basic alerting and response only — no RCA.

Pricing: Professional from $21/user/month (annual). AIOps add-on from $699/month. PagerDuty Advance (GenAI) from $415/month.

Strengths: Industry-standard on-call, 700+ integrations, robust mobile app, strong ecosystem. Limitations: RCA requires expensive add-ons, not included in base plans.

Best for: Teams that already use PagerDuty for on-call and want to add AI-powered correlation and noise reduction.

How to Choose the Right Platform

When evaluating free AIOps RCA tools, prioritize these criteria:

RCA approach — Deterministic AI (Dynatrace), probabilistic ML (BigPanda), or agentic investigation (Aurora)?
Telemetry breadth — Does it cover logs, metrics, traces, and infrastructure state?
Cloud integration — Does it work with your cloud providers and existing monitoring stack?
Free tier limitations — What's actually included? Some "free" plans exclude RCA entirely (PagerDuty).
Self-hosted vs SaaS — Do you need data sovereignty? Only Aurora and OpenObserve offer full self-hosted deployment.
Investigation depth — Does it correlate alerts, or does it actually query your infrastructure?

Start with a free tier or open source instance to validate whether automated RCA reduces your MTTR before scaling to paid plans.

Key Features to Look For

AI/ML approach — Deterministic vs probabilistic vs agentic
Telemetry support — Logs, metrics, traces, and infrastructure state
Cloud provider integration — Native connectors for AWS, Azure, GCP, Kubernetes
Remediation guidance — Does it just identify the cause, or suggest fixes?
Postmortem automation — Auto-generated incident documentation
Knowledge base — Search over runbooks and past incidents
Compliance — SOC 2, HIPAA, GDPR if required

Mean Time to Repair (MTTR) — the average time to detect, diagnose, and resolve an incident — is the key metric. Research shows that AIOps root cause automation can cut MTTR by up to 50%.

Learn more about automated RCA in our Root Cause Analysis: The Complete Guide for SREs and explore how agentic investigation works in What is Agentic Incident Management?. For open source options, see Open Source Incident Management: Why It Matters.

All platform claims verified from official vendor websites. Last verified: April 2026.