New Relic AI Alternative: Open Source AI Root Cause Analysis

Key Takeaways

• New Relic AI reasons over telemetry already ingested into New Relic, and it is SaaS-only. Its AIOps and Applied Intelligence layer does anomaly detection, alert correlation, noise reduction, and 'precise causal analysis that identifies root cause' across data inside the New Relic platform. The newer SRE Agent, announced 24 February 2026, adds 'always on' diagnostics, root cause analysis, and post-incident reports.
• New Relic deliberately does not own remediation. Per TechTarget's reporting, 'New Relic's tools won't include an AI agent control plane to control incident remediation that overlaps with those partner tools' such as ServiceNow, Atlassian, and PagerDuty. The SRE Agent diagnoses and recommends; humans and partner tools act.
• Aurora is open source under Apache 2.0 and self-hosted. Its LangGraph-orchestrated agents both investigate AND execute: they run kubectl, aws, az, and gcloud in sandboxed Kubernetes pods, with destructive actions human-gated.
• Aurora is multi-cloud and vendor-neutral. It queries AWS, Azure, GCP, OVH, Scaleway, and Kubernetes natively, builds a Memgraph infrastructure knowledge graph for blast radius, and is bring-your-own-LLM, including local models via Ollama for air-gapped use.
• Aurora ingests from your existing monitoring. It accepts alert webhooks from eleven connectors (PagerDuty, Datadog, Grafana, New Relic, OpsGenie, Netdata, Dynatrace, Coroot, ThousandEyes, BigPanda, incident.io) plus a Slack bot, so you can keep your current stack and add an open investigation layer on top.
• New Relic pricing is consumption-based. The published model bills $0.40 per GB ingested (Original Data) or $0.60 per GB (Data Plus) plus per-user seats, after a free tier of 100 GB and one full platform user. Aurora is free under Apache 2.0; you pay only for your own infrastructure and LLM usage.

New Relic AI is a convenient choice if your telemetry already lives in New Relic. The honest tradeoff: it reasons over New-Relic-ingested data, runs only as SaaS, and is built to diagnose and assist rather than to execute fixes. Aurora takes the opposite shape: open source, self-hosted, vendor-neutral, multi-cloud, and capable of running active investigations and human-gated remediations. This guide compares the two on the dimensions that actually drive a procurement decision, with every claim linked to a primary source.

For the broader category framing, see our explainer on AI SRE vs AIOps. For the full open-source landscape, see our roundup of the top AIOps platforms with free root cause analysis in 2026.

What is New Relic AI?

New Relic AI is the artificial-intelligence layer inside the New Relic observability platform, spanning the older Applied Intelligence and AIOps features and the newer SRE Agent. It is a SaaS product that analyzes telemetry already ingested into New Relic and helps engineers detect, correlate, and understand incidents faster.

The Applied Intelligence and AIOps capabilities are correlation and assist centric. New Relic describes them as connecting 'similar alert events into a single issue for simpler troubleshooting,' reducing 'mean time to resolve (MTTR) with precise causal analysis that identifies root cause,' and identifying 'root cause in seconds by correlating logs, metrics, traces, and changes.' The framing throughout is noise reduction, expert routing, and recommended next steps rather than self-healing.

On 24 February 2026, New Relic announced an SRE Agent that pushes further into investigation. It is positioned as an 'always on' AI teammate that performs 'deep, full-stack diagnostics,' delivers root cause analysis, impact assessments, and comprehensive post-incident reports, and surfaces 'a unified view of the evolving timeline of events.' It works through Slack and Zoom so responders can query New Relic from triage rooms, and it uses Intelligent Root Cause Analysis, which scores an entity's topology graph with probabilistic causal models. New Relic was also named a Leader in the IDC MarketScape: Worldwide AIOps 2026 Vendor Assessment (Doc #US54116226, March 2026), and in May 2026 it introduced New Relic Knowledge, which connects 'telemetry, documentation, and historical incidents to deliver context aware and trusted insights in real time.'

Two structural facts matter for buyers. First, New Relic AI reasons over data that has been ingested into New Relic, and that ingestion is what the pricing meter counts. Second, New Relic has consciously chosen not to own remediation. As CPO Brian Emerson told TechTarget, the company will continue to focus on 'agentic integrations with partners' rather than directly automating the software delivery lifecycle, and 'New Relic's tools won't include an AI agent control plane to control incident remediation that overlaps with those partner tools' from ServiceNow, Atlassian, and PagerDuty.

What is Aurora?

Aurora is an open-source AI SRE and incident-management platform that both investigates incidents and executes human-gated remediations across multiple clouds. It is built by Arvo AI, licensed under Apache 2.0, and designed to be self-hosted.

When an alert fires, Aurora's LangGraph-orchestrated agents autonomously investigate. They query AWS, Azure, GCP, OVH, Scaleway, and Kubernetes, and they run real commands, kubectl, aws, az, and gcloud, inside sandboxed Kubernetes pods. Aurora builds a Memgraph infrastructure knowledge graph to trace blast radius across services and cloud providers, generates structured root-cause analyses and postmortems that export to Confluence, Notion, or SharePoint, and can suggest code fixes or open pull requests. Destructive actions are gated behind human approval.

Aurora is bring-your-own-LLM. You can point it at a hosted frontier model or run local inference through Ollama for air-gapped deployments where no data leaves your environment. Crucially, Aurora does not require you to migrate your monitoring stack. It ingests alerts by webhook from eleven connectors, PagerDuty, Datadog, Grafana, New Relic, OpsGenie, Netdata, Dynatrace, Coroot, ThousandEyes, BigPanda, and incident.io, plus a Slack bot, so it sits on top of whatever you already run. For the deployment and data-sovereignty story, see our guide to self-hosted AI SRE.

New Relic AI vs Aurora: head-to-head

The clearest way to separate these tools is by what triggers them, what they reason over, and whether they can act. New Relic AI is triggered by telemetry inside New Relic and is built to diagnose and recommend. Aurora is triggered by an alert from any of your monitors and is built to investigate live infrastructure and then execute approved fixes.

Where they overlap. Both detect or correlate incidents, both produce root-cause analysis with an evidence trail, both generate post-incident reports, and both keep a human in the loop before consequential action. If you only need faster diagnosis on data you already send to New Relic, the two converge on outcomes.

Where they diverge.

• Data boundary. New Relic AI reasons over data ingested into New Relic; the more you want it to reason about, the more you ingest, and ingestion is metered. Aurora reaches directly into your cloud APIs and clusters at investigation time, so it can inspect infrastructure state that was never shipped to an observability backend.
• Action. New Relic deliberately stops at diagnosis and recommendation, deferring remediation to partner tools. Aurora runs commands and opens PRs, with destructive steps human-gated.
• Deployment. New Relic AI is SaaS-only. Aurora is self-hosted via Docker Compose or Helm and can run fully air-gapped.
• Cloud breadth. New Relic AI is cloud-agnostic for telemetry but does not query your clouds as an agent. Aurora natively investigates five clouds plus Kubernetes. See our overview of multi-cloud incident management.
• Cost model. New Relic AI rides on consumption pricing. Aurora is free software; your cost is your own compute plus optional LLM spend.

Comparison table

How much does New Relic AI cost?

New Relic does not publish a per-investigation or per-agent rate; its AI features are bundled into a consumption-based platform price. The two metered dimensions are data ingest and users.

Per the official pricing page, the free tier includes 100 GB of monthly data ingest and one full platform user. Beyond that, ingest is billed at $0.40 per GB on Original Data or $0.60 per GB on Data Plus, with an EU data-center add-on of $0.05 per GB. Full platform users cost $10 for the first user and $99 per additional user on Standard (maximum five), and $349 per user per year on Pro annual commitments, with $418.80 per user for monthly pay-as-you-go. Core users are $49 each and basic users are free. The billing documentation states plainly: 'We bill for data by the amount of GB Ingested, no matter the type.'

The practical consequence is that the value New Relic AI extracts is bounded by what you ingest, and what you ingest is what you pay for. There is no public per-investigation rate to compare against. Aurora has no license cost at all; its only spend is the infrastructure you self-host it on plus whatever LLM usage you choose, which can be zero with a local model.

Which should you choose?

Choose New Relic AI if your telemetry already lives in New Relic and you want a low-friction, managed add-on. It is a strong fit when you are already paying for New Relic, want anomaly detection, correlation, and diagnostics without standing up new infrastructure, and are comfortable letting partner tools and humans own remediation. Being named a Leader in the IDC MarketScape Worldwide AIOps 2026 assessment reflects a mature, well-supported platform, and the SRE Agent is a genuine step into agentic diagnosis for New Relic-native shops.

Choose Aurora if open source, self-hosting, multi-cloud breadth, or autonomous execution matter to you. Aurora is the better fit when you need to investigate cloud and Kubernetes state directly rather than only data you have already shipped to a vendor, when you want the agent to execute approved fixes rather than just recommend them, when you require air-gapped or data-sovereign deployment, or when consumption-based ingest pricing is hard to predict at your volume. Because Aurora ingests from eleven monitoring connectors, you can keep your existing alerting and add it as a vendor-neutral investigation and remediation layer. For the conceptual contrast between correlation-style AIOps and agentic AI SRE, see our AI SRE vs AIOps explainer.

Many teams will run both. New Relic AI can stay the observability and correlation home for telemetry that already lives there, while Aurora handles cross-cloud investigation and human-gated remediation on top of the alert stream. The honest summary: New Relic AI is the convenient option for New Relic-native shops, and Aurora is the open, executable option for everyone who needs vendor neutrality, multi-cloud reach, or the ability to act.

Getting started with Aurora

Aurora is on GitHub at github.com/Arvo-AI/aurora under Apache 2.0. Clone the repo, deploy with Docker Compose or the Helm chart, point your monitoring webhooks at it, and add your cloud credentials. Investigations begin automatically when alerts arrive. If you want a connector or capability that does not exist yet, the Arvo AI team builds custom integrations with users at no cost.