BigPanda Alternative: Open Source AIOps Event Correlation (2026)
Aurora is a free, open-source, self-hosted BigPanda alternative that investigates and verifies root cause instead of only grouping alerts. Full comparison.
Key Takeaways
- BigPanda is a closed, SaaS-only AIOps platform with no free or self-hosted tier. It is a SaaS-native platform and publishes no pricing, routing buyers to a sales contact form. Aurora is open source under Apache 2.0 and fully self-hosted.
- BigPanda groups alerts. Aurora investigates and verifies the root cause. BigPanda's Open Box Machine Learning correlates alerts, changes, and topology to reduce alert noise by up to 95%. Aurora's LangGraph agents run live commands against your cloud and Kubernetes to gather new evidence, not just cluster existing alerts.
- BigPanda is a well-funded enterprise vendor. It raised a 190 million dollar Series D at a 1.2 billion dollar valuation in January 2022, co-led by Advent International and Insight Partners. Aurora is a free open-source project at 263 GitHub stars and growing.
- Open-source AIOps does correlation, not agentic investigation. Prometheus Alertmanager does grouping and inhibition, and Zabbix does event correlation, but neither runs an LLM agent that investigates and executes. Aurora fills that gap.
- Aurora ingests BigPanda itself. BigPanda is one of Aurora's monitoring connectors, so you can keep BigPanda for noise reduction and add Aurora as the investigation layer on top.
- Aurora is air-gapped capable with bring-your-own-LLM. It runs local models via Ollama, so incident data and cloud credentials never leave your environment.
If you are evaluating BigPanda, you are almost certainly drowning in alerts and want fewer, smarter incidents. BigPanda is genuinely strong at that. The honest question is whether grouping alerts is where your time actually goes, or whether the real cost is the hour your on-call engineer spends figuring out why the grouped incident happened. This guide draws the line between correlation and investigation, names BigPanda's real strengths, and shows where an open-source agent fits. Every factual claim links to a primary source.
For the category-level distinction this post builds on, see our explainer on AI SRE vs AIOps.
What is BigPanda?
BigPanda is a closed-source, enterprise SaaS AIOps platform that correlates high-volume alert streams into a smaller number of actionable incidents. Founded in 2012 and headquartered in Redwood City, California (Wikipedia), it is one of the most established names in event correlation and noise reduction.
The core engine is what BigPanda calls Open Box Machine Learning: a deliberately transparent approach to ML correlation that lets operators read the automation logic in plain English, edit it, and preview it before it runs. That transparency is a real differentiator against opaque black-box AIOps. BigPanda claims its platform can reduce alert noise by up to 95 percent by capturing alerts, changes, and topology data from disparate tools and correlating them into high-level incidents in real time.
BigPanda is a serious, well-capitalized company. It raised a 190 million dollar Series D at a 1.2 billion dollar valuation in January 2022, co-led by Advent International and Insight Partners, reaching unicorn status. The product is delivered as a SaaS-native platform with built-in scaling and high availability. There is no published per-incident or per-seat price: the pricing page routes to a sales contact form, and there is no free tier or self-hosted distribution you can download and run yourself.
What is Aurora?
Aurora is an open-source, self-hosted AI SRE platform that autonomously investigates incidents and can execute verified fixes, rather than only grouping alerts. Built by Arvo AI and licensed under Apache 2.0, Aurora uses LangGraph-orchestrated agents that pick from dozens of tools per incident.
When an alert fires, Aurora's agents query infrastructure across AWS, Azure, GCP, OVH, Scaleway, and Kubernetes, run real commands like kubectl, aws, az, and gcloud inside sandboxed Kubernetes pods, and build a Memgraph infrastructure knowledge graph to trace blast radius. The output is a structured root-cause analysis and a postmortem you can export to Confluence, Notion, or SharePoint, plus suggested code fixes or an opened pull request. Destructive actions are human-gated, so the agent never executes a risky change without an operator approving it.
Aurora ingests alerts via webhook from eleven monitoring connectors: PagerDuty, Datadog, Grafana, New Relic, OpsGenie, Netdata, Dynatrace, Coroot, ThousandEyes, BigPanda, and incident.io, plus a Slack bot for conversational investigation. Because BigPanda is one of those connectors, the two are not strictly either-or. It is self-hosted and air-gapped capable, with bring-your-own-LLM support for local models via Ollama.
Correlation groups alerts. Investigation finds the cause.
The cleanest way to understand BigPanda versus Aurora is one sentence: correlation groups the alerts you already have, while investigation gathers new evidence to explain them.
BigPanda's job ends at a well-formed incident. Its Open Box ML reads your existing alert stream, deduplicates it, and clusters symptoms that belong together, so a single network partition surfaces as one incident instead of three hundred pages. That is enormously valuable, and BigPanda does it at enterprise scale. But the grouped incident still arrives as a question: now that you know these forty alerts are related, why did they fire?
Aurora starts where BigPanda stops. It treats the incident as a hypothesis to test. Its agents log into your cloud accounts and clusters, run live diagnostic commands, walk the dependency graph to find the blast radius, and assemble an evidence chain that points at a verified root cause. Then, with human approval, it can act on that conclusion: open a pull request with a fix, or run a remediation command in a sandboxed pod.
Neither approach makes the other redundant. A team running thousands of daily alerts benefits from correlation upstream and investigation downstream. For the broader breakdown of why these are different lifecycle stages, see AI SRE vs AIOps and our roundup of free AIOps platforms with root cause analysis.
BigPanda vs Aurora: head to head
| Dimension | BigPanda | Aurora |
|---|---|---|
| License | Proprietary, closed source (SaaS-native) | Apache 2.0, open source |
| Deployment | Vendor-hosted SaaS only, demo and sales-led | Self-hosted, air-gapped capable |
| Multi-cloud reach | Ingests alerts from cloud and on-prem tools | Actively queries AWS, Azure, GCP, OVH, Scaleway, Kubernetes |
| Correlation vs investigation | Groups alerts into incidents via Open Box ML | Investigates and verifies root cause with live evidence |
| Write and execute actions | Surfaces context and suggested steps | Runs commands in sandboxed pods, opens PRs, human-gated |
| Pricing model | No public price, custom enterprise quote via sales | Free software; you pay only for your own infra and LLM tokens |
| Self-host and air-gap | Not available as a self-hosted product | Self-hosted with bring-your-own-LLM (Ollama) |
BigPanda's genuine strengths are real: a mature correlation engine, transparent Open Box ML, up to 95 percent noise reduction, and a deep catalog of enterprise integrations backed by a unicorn-scale balance sheet. If a single vendor relationship and managed SaaS are what you want, those are points in BigPanda's favor.
Aurora's strengths are orthogonal. It is the only side of this comparison that is open source, self-hosted, free, and capable of taking action rather than only describing context. For teams with data-sovereignty or air-gap requirements, see self-hosted AI SRE.
Where open-source AIOps stops
The open-source ecosystem already does correlation. It does not do agentic investigation. That gap is exactly what Aurora is built for.
Prometheus Alertmanager is the canonical example. It deduplicates, groups, and routes alerts, and it supports inhibition rules that suppress symptom alerts when a parent root-cause alert is firing. That is powerful, but it is rule-based and manually configured: you tell it which alerts inhibit which. It never logs into your cloud to check what actually broke. Zabbix similarly ships a built-in event correlation engine for servers, VMs, and network gear, but it does not run an LLM agent that gathers new evidence during an incident.
So the practical landscape looks like this. BigPanda is the polished commercial correlation layer. Prometheus and Zabbix are the open-source correlation layer. None of them investigate. Aurora is the open-source investigation layer that sits on top of whichever correlation and routing stack you already run.
It is worth being precise about a few other names buyers compare here. Moogsoft is not gone: it is now Dell APEX AIOps Incident Management, following Dell's July 2023 acquisition, and it is actively maintained. The honest tradeoff there is Dell-ownership lock-in, ProSupport-contract gating, and opaque enterprise pricing around aging patented correlation ML, not a shutdown.
Routing is a separate concern again. Grafana OnCall OSS was archived on 24 March 2026, with Grafana pushing users toward Grafana Cloud IRM. OnCall is alert routing, scheduling, and escalation, not investigation, so it and Aurora are complementary, not substitutes. If you are migrating off OnCall, an open-source router like Keep, or notifications via ntfy or Twilio, can handle paging while Aurora handles the investigation on top.
Which should you choose?
Choose based on which problem actually costs your team the most time: grouping alerts, or explaining them.
Choose BigPanda if your primary pain is alert-storm noise at enterprise scale, you want a single managed SaaS vendor with white-glove support, you have budget for an opaque enterprise contract, and a demo-and-sales procurement cycle is acceptable. BigPanda's correlation maturity and transparent Open Box ML are real reasons to pick it for that job.
Choose Aurora if your bottleneck is the manual investigation that happens after an incident is formed, you need to run across multiple clouds rather than a single Kubernetes cluster, you require self-hosting or an air-gapped deployment, or you simply will not adopt a closed SaaS with no published price. Aurora is open source, free, vendor-neutral, and it executes verified fixes rather than only summarizing context.
Run both if you already own BigPanda. Point BigPanda's correlated incidents at Aurora's BigPanda webhook connector, and let Aurora investigate each one automatically. You keep best-in-class noise reduction and add an investigation layer that costs only your own infrastructure and tokens. For multi-cloud estates specifically, see multi-cloud incident management.
Getting started with Aurora
Aurora is on GitHub at github.com/Arvo-AI/aurora under Apache 2.0. Clone the repo, run the setup with the provided make targets, point your monitoring webhooks at it, add cloud credentials, and investigations begin automatically. Bring your own LLM, including local models for air-gapped environments, and keep every byte of incident data inside your own perimeter.
Every claim in this post is sourced from official vendor pages, primary press releases, and public repositories. BigPanda data from bigpanda.io. Aurora data from github.com/Arvo-AI/aurora. Last verified June 2026.