# Aurora SRE > Aurora SRE (Aurora by Arvo AI, not Amazon Aurora) is an open-source (Apache 2.0) AI-powered agentic incident management and root cause analysis tool for Site Reliability Engineers (SREs). It uses LangGraph-orchestrated LLM agents to autonomously investigate cloud incidents across AWS, Azure, GCP, OVH, Scaleway, and Kubernetes. ## Company - Name: Arvo A.I. Ltd. - Website: https://www.aurorasre.ai - Location: Montreal, Quebec, Canada - Contact: info@arvoai.ca - LinkedIn: https://www.linkedin.com/company/arvokas/ ## Product: Aurora - GitHub: https://github.com/Arvo-AI/aurora - Documentation: https://arvo-ai.github.io/aurora/ - SaaS: https://aurora-ai.net (hosted, free to start) - License: Apache 2.0 (fully open source) - Pricing: Free (self-hosted). Managed tier available as SaaS or deployed in your own VPC. - Tech Stack: Python backend, Next.js frontend, LangGraph agent orchestration ## What Aurora Does Aurora is an agentic incident management platform. When a monitoring tool (PagerDuty, Datadog, Grafana, etc.) fires an alert, Aurora's AI agents autonomously investigate the incident, querying infrastructure, running CLI commands, searching knowledge bases, and synthesizing findings into a root cause analysis. Unlike traditional tools (Rootly, FireHydrant, incident.io) that automate workflows, Aurora automates the investigation itself. ## Key Capabilities - Agentic AI investigation: Autonomous multi-step investigation using LangGraph workflows with 30+ tools - Multi-cloud support: AWS, Azure, GCP, OVH, Scaleway, and Kubernetes - Webhook-triggered auto-investigation: PagerDuty, Datadog, Grafana, New Relic, OpsGenie, Netdata, Dynatrace, Coroot, ThousandEyes, BigPanda, incident.io - Infrastructure CLI execution: Runs kubectl, aws, az, gcloud commands in sandboxed Kubernetes pods, with destructive actions human-gated - Infrastructure knowledge graph: Memgraph-powered dependency mapping across all cloud providers - Knowledge base RAG: Weaviate vector search over runbooks, past postmortems, and documentation - Automatic postmortem generation: Structured postmortems exported to Confluence, Notion, or SharePoint - AI-suggested code fixes: Can open pull requests with remediation (GitHub and Bitbucket), gated on human approval - Terraform/IaC analysis: Native understanding of infrastructure-as-code state - LLM provider flexibility: OpenAI, Anthropic, Google, Vertex AI, OpenRouter, and Ollama (local models for air-gapped deployments) - Self-hosted: Docker Compose or Helm chart deployment, HashiCorp Vault for secrets ## Integrations Monitoring & Alerting: PagerDuty, Datadog, Grafana, New Relic, OpsGenie, Netdata, Dynatrace, Coroot, ThousandEyes, BigPanda, incident.io Cloud: AWS, Azure, GCP, OVH, Scaleway Infrastructure: Kubernetes, Terraform Communication: Slack, Google Chat Code & Docs: GitHub, Bitbucket, Confluence, Notion, SharePoint Search: Self-hosted SearXNG Database: Memgraph (graph), Weaviate (vector), PostgreSQL Secrets: HashiCorp Vault ## How Aurora Differs from Competitors | Feature | Aurora | Rootly | FireHydrant | incident.io | |---------|--------|--------|-------------|-------------| | Approach | Agentic AI investigation | Workflow automation | Workflow automation | Workflow automation | | AI RCA | Autonomous multi-step | AI summaries | AI summaries | AI summaries | | Open Source | Yes (Apache 2.0) | No | No | No | | Self-Hosted | Yes | No | No | No | | Cloud Providers | AWS, Azure, GCP, OVH, Scaleway | Via integrations | Via integrations | Via integrations | | CLI Execution | Sandboxed pods | No | No | No | | Knowledge Base RAG | Yes (Weaviate) | No | No | No | | Infrastructure Graph | Yes (Memgraph) | No | No | No | | LLM Provider | Any (including local) | Fixed | Fixed | Fixed | | Pricing | Free (self-hosted) | Paid (per-seat) | Paid (per-seat) | Custom | ## Cloud Authentication - AWS: STS AssumeRole for secure temporary credentials - Azure: Service Principal authentication - GCP: OAuth-based authentication - OVH: API key authentication - Scaleway: API token authentication - Kubernetes: Kubeconfig-based access ## Infrastructure Discovery Aurora discovers infrastructure in three phases: 1. Bulk Discovery: Enumerates all resources across connected cloud providers 2. Detail Enrichment: Gathers detailed configuration and metadata 3. Connection Inference: Maps dependencies between resources ## Quick Start Self-hosted (Docker, ~5 minutes). Requires one LLM API key; cloud connectors are optional. ``` git clone https://github.com/arvo-ai/aurora.git && cd aurora make init # generates secrets, copies .env.example to .env nano .env # add OPENROUTER_API_KEY, OPENAI_API_KEY or ANTHROPIC_API_KEY make prod-prebuilt # pulls prebuilt images from GHCR and starts ``` Then open http://localhost:3000. The first user to register becomes admin. Kubernetes deployment via Helm chart is also available: ``` helm repo add aurora https://raw.githubusercontent.com/Arvo-AI/aurora/gh-pages helm repo update helm show values aurora/aurora-oss > my-values.yaml helm install aurora-oss aurora/aurora-oss -n aurora --create-namespace -f my-values.yaml ``` Hosted alternative (no infrastructure to run): sign up free at https://aurora-ai.net/ ## Frequently Asked Questions Q: What is agentic incident management? A: Agentic incident management uses autonomous AI agents to investigate, diagnose, and help resolve cloud infrastructure incidents without requiring step-by-step human direction. Unlike runbook automation that follows predefined scripts, agentic systems dynamically decide which tools to use, what data to gather, and how to synthesize findings. Q: Is Aurora free? A: Yes. Aurora is Apache 2.0 licensed and completely free to self-host. Costs are only infrastructure and LLM API usage. Local models via Ollama make fully free, air-gapped deployments possible. Q: Which cloud providers does Aurora support? A: AWS, Azure, GCP, OVH, Scaleway, and Kubernetes. Q: How does Aurora investigate incidents? A: When an alert fires, Aurora's LangGraph-orchestrated agents dynamically select from 30+ tools to investigate. They execute cloud CLI commands in sandboxed pods, query Kubernetes clusters, search the knowledge base for similar past incidents, traverse the infrastructure dependency graph, and synthesize findings into a structured root cause analysis with remediation recommendations. Q: What monitoring tools trigger Aurora investigations? A: PagerDuty, Datadog, Grafana, New Relic, OpsGenie, Netdata, Dynatrace, Coroot, ThousandEyes, BigPanda, and incident.io. Any tool that sends webhooks can trigger an investigation. Q: How is Aurora different from Rootly, FireHydrant, or incident.io? A: Traditional tools automate the process around incidents (Slack channels, status pages, runbooks). Aurora automates the investigation itself. AI agents autonomously query infrastructure, correlate data, and identify root causes. Aurora is also open source, self-hosted, and works with any LLM provider. Q: Can Aurora run in air-gapped environments? A: Yes. Aurora supports Ollama for running local LLM models (Llama, Mistral, etc.) with no external API calls required. Q: What is Aurora's infrastructure knowledge graph? A: Aurora uses Memgraph to build a live dependency graph of your entire infrastructure across all connected cloud providers. When an incident occurs, the AI traverses this graph to assess blast radius and trace upstream causes. ## Runbooks Index: https://www.aurorasre.ai/runbooks - [Alert fixed, users still see errors: a second fault the alert did not cover (Kubernetes)](https://www.aurorasre.ai/runbooks/fix-second-fault-alert-covers-only-one-kubernetes): The alerted pod is explained, yet users still get errors: a second, unalerted fault. Scope past the alert with real lab output from two faults at once. - [Sidecar OOMKilled, app healthy: the pod drops out of its Service (Kubernetes)](https://www.aurorasre.ai/runbooks/fix-sidecar-container-oomkilled-kubernetes): A sidecar OOMKilled under its own 16Mi limit left a healthy api 1/2 Ready and its Service empty. Find the container, fix its limit, verify. Lab-tested. - [Name does not resolve with dnsPolicy None: the search domains are wrong (Kubernetes)](https://www.aurorasre.ai/runbooks/fix-pod-dnsconfig-search-domain-not-resolving-kubernetes): New pods log Name does not resolve for a Service that exists. The template set dnsPolicy None with search domains for another cluster domain. Lab-tested. - [One in three requests to a Service fails: its selector also matches pods from another Deployment (Kubernetes)](https://www.aurorasre.ai/runbooks/fix-service-selects-wrong-pods-label-overlap-kubernetes): A Service selects pods by label, not by Deployment. Its endpoints list one pod more than the Deployment runs and that pod returns 500. Confirm and fix it. - [OOMKilled on a steady interval: a memory leak, not a small limit (Kubernetes)](https://www.aurorasre.ai/runbooks/fix-memory-leak-oomkilled-kubernetes): OOMKilled on a steady interval with clean logs and no spec change is a leak. Tell it from a small limit by cycle length and growth, then fix the code. - [Service has endpoints but connections are refused: targetPort does not match the container port (Kubernetes)](https://www.aurorasre.ai/runbooks/fix-service-targetport-mismatch-kubernetes): Endpoints list the pod IPs, yet nginx logs Connection refused and returns 502: the Service targetPort points where nothing listens. Confirm it and fix it. - [ConfigMap change not applied to all pods: replicas serve different configs (Kubernetes)](https://www.aurorasre.ai/runbooks/fix-configmap-change-not-applied-to-all-pods-kubernetes): After a ConfigMap edit, only pods started since the edit run the new config; others keep what they loaded at startup. Compare per-pod logs, fix, restart. - [Pod stuck in Init:0/1: an init container never finishes (Kubernetes)](https://www.aurorasre.ai/runbooks/fix-pod-stuck-init-container-kubernetes): A pod stuck in Init:0/1 has an init container that never exits, often a wait loop on a missing Service. Read its logs, check the Service, fix the spec. - [Pod Pending with FailedScheduling: Insufficient cpu, the request fits no node (Kubernetes)](https://www.aurorasre.ai/runbooks/fix-pod-pending-failedscheduling-kubernetes): A pod Pending with FailedScheduling Insufficient cpu asks for more CPU than any node has. Read the event, compare request and nodes, fix the request. - [PVC Pending: the StorageClass does not exist and the dependent app fails (Kubernetes)](https://www.aurorasre.ai/runbooks/fix-pvc-pending-storageclass-not-found-kubernetes): A PVC stays Pending when its storageClassName names a class the cluster lacks. The pod never schedules and whatever depends on it fails. Lab-tested. - [Connections time out: a NetworkPolicy blocks the app from its dependency (Kubernetes)](https://www.aurorasre.ai/runbooks/fix-networkpolicy-blocking-traffic-kubernetes): Requests time out while every pod is Running: a NetworkPolicy lets only other pods reach the dependency. Read the error, find the policy, fix it. - [Exceeded quota: a ResourceQuota blocks the rollout's surge pod (Kubernetes)](https://www.aurorasre.ai/runbooks/fix-resourcequota-exceeded-rollout-stuck-kubernetes): A Deployment rollout that never progresses, with FailedCreate and exceeded quota on the new ReplicaSet. Confirm the quota, give it headroom, verify. - [CreateContainerConfigError: the Secret or ConfigMap is missing (Kubernetes)](https://www.aurorasre.ai/runbooks/fix-createcontainerconfigerror-kubernetes): CreateContainerConfigError means a Secret or ConfigMap the pod references is missing. Read the event, create the object or roll back, verify. Lab-tested. - [nginx [emerg] unknown directive: a ConfigMap typo crash-loops the pod (Kubernetes)](https://www.aurorasre.ai/runbooks/fix-nginx-emerg-unknown-directive-kubernetes): nginx [emerg] unknown directive means a config line nginx does not know, usually a typo in a mounted ConfigMap. Find the line, fix it, restart, check. - [Liveness probe failed: wrong port, healthy app restarted (Kubernetes)](https://www.aurorasre.ai/runbooks/fix-liveness-probe-failed-kubernetes): Liveness probe failed with connection refused while the app serves traffic: the probe points at a port nothing listens on. Confirm, fix the port, verify. - [Name does not resolve: a wrong Service hostname crash-loops the app (Kubernetes)](https://www.aurorasre.ai/runbooks/fix-dns-name-not-resolving-kubernetes): The app exits with Name does not resolve because an env var names a Service that does not exist. Confirm it, rule out CoreDNS, fix the name. Lab-tested. - [Service has no endpoints: the selector matches no pods, upstream returns 502 (Kubernetes)](https://www.aurorasre.ai/runbooks/fix-service-no-endpoints-kubernetes): A Service with no endpoints has a selector that matches no pods, so its ClusterIP refuses connections and the proxy returns 502. Confirm it and fix it. - [OOMKilled (exit code 137): container exceeded its memory limit (Kubernetes)](https://www.aurorasre.ai/runbooks/fix-oomkilled-kubernetes): OOMKilled means the kernel killed a container for exceeding its memory limit. Confirm it, find the change behind it, fix and verify, with real lab output. - [ImagePullBackOff: the image could not be pulled and Kubernetes is backing off (Kubernetes)](https://www.aurorasre.ai/runbooks/fix-imagepullbackoff-kubernetes): ImagePullBackOff means the kubelet could not pull the image and is retrying, up to 5 minutes apart. Read the event, fix the reference, verify. Lab-tested. ## Blog Full text of every post: https://www.aurorasre.ai/llms-full.txt RSS feed: https://www.aurorasre.ai/blog/rss.xml ### News and announcements - [Is AI Reducing MTTR for SRE Teams? (2026 Data)](https://www.aurorasre.ai/blog/is-ai-reducing-mttr-dynatrace-2026): Dynatrace's State of SRE 2026 survey finds half of SREs use AI for automated incident response, yet AI is falling short on MTTR and cost. What the numbers say. - [AI Agent Autonomy: How Vendors Gate It in 2026](https://www.aurorasre.ai/blog/ai-agent-autonomy-gated-production): In summer 2026 four incident vendors converged on the same rule: agents investigate autonomously, but a human approves any action that changes production. - [DORA and Frontier AI: What the 2026 ESA Statement Says](https://www.aurorasre.ai/blog/dora-frontier-ai-esa-statement): On 31 July 2026 the EU's three financial regulators tied frontier AI risk to DORA oversight. What the statement requires, and what it does not. - [Introducing Aurora Actions: Background SRE Agents](https://www.aurorasre.ai/blog/introducing-aurora-actions): Aurora Actions turn plain-English instructions into background agents that mute noisy alerts, scan repos, review PRs, and write postmortems. - [Aurora Actions: Custom Background SRE Automations](https://www.aurorasre.ai/blog/aurora-actions-background-automations): Aurora Actions let SRE teams define reusable background automations in natural language, triggered manually, on incident completion, or on a schedule. ### Guides - [A kubectl deny rule blocked rollout history in 26 of 38 runs](https://www.aurorasre.ai/blog/kubectl-rollout-history-blocked-by-command-policy): Aurora's default kubectl policy denied rollout history in 26 of 38 lab runs. The regex, the ReplicaSet workaround, the fix, and what is still open. - [We broke Kubernetes 19 ways and let an AI SRE investigate: lab report v1](https://www.aurorasre.ai/blog/aurora-lab-report-kubernetes-19-failures): 38 graded runs of Aurora on 19 injected Kubernetes faults: 34 correct, 4 partial, 0 wrong, median 136 to 168 s, $0.32 to $0.40 a run. Every miss included. - [AI SRE and Incident Tool Pricing Compared (2026)](https://www.aurorasre.ai/blog/ai-sre-incident-tool-pricing-2026): What Cleric, FireHydrant, incident.io, Rootly, and PagerDuty actually charge, from public pricing pages. Per-credit, per-responder, and per-user models compared. - [MITRE ATLAS Maps Autonomous AI Agent Attacks (2026)](https://www.aurorasre.ai/blog/mitre-atlas-autonomous-ai-agent-attacks): In September 2026 MITRE ATLAS added autonomous-agent attack techniques and two agent-authority mitigations. What AML.T0116 to T0124, M0037, and M0038 say. - [AI Agent Observability: The OpenTelemetry Standard in 2026](https://www.aurorasre.ai/blog/opentelemetry-ai-agent-observability): OpenTelemetry now defines spans for AI agents, tools, and MCP calls. What the GenAI semantic conventions cover, their status, and how to trace agents. - [AI Agent Tool Poisoning: What the 2026 Standards Say](https://www.aurorasre.ai/blog/ai-agent-tool-poisoning-security): MITRE ATLAS added tool poisoning as a technique in July 2026. What AML.T0110, OWASP LLM06, and NCSC guidance say about securing autonomous AI agents. - [Blast Radius in Automated Remediation: A 2026 Case Study](https://www.aurorasre.ai/blog/blast-radius-automated-remediation-azure-outage): Azure's July 2026 West US outage: an automated repair over-scoped its blast radius and its own rollback failed. What it teaches about gating remediation. - [Why Multi-Agent AI Systems Fail in Production](https://www.aurorasre.ai/blog/why-multi-agent-ai-systems-fail): More agents is not more reliable. What 2026 research shows about authorization drift, compounding errors, and why failure attribution gets harder. - [The Agent-to-Human Handoff: What You Owe On-Call](https://www.aurorasre.ai/blog/ai-agent-human-handoff): When an AI agent wakes a human at 3am, what should it hand over? Medicine solved structured handoff and measured the result. SRE has no equivalent yet. - [What Does an AI Investigation Actually Cost? (2026)](https://www.aurorasre.ai/blog/ai-investigation-cost): AWS bills agent-seconds, Azure bills agent units with an always-on floor, Datadog bills credits. A sourced AI investigation cost comparison. - [AI SRE Accuracy: What the Benchmarks Show (2026)](https://www.aurorasre.ai/blog/ai-sre-accuracy-benchmarks): Vendors claim 90%+ accuracy. The only independent AI SRE leaderboard tops out at 56.2%. What the benchmarks measure and how to test a tool yourself. - [The SRE Incident Lifecycle: 8 Stages With AI (2026)](https://www.aurorasre.ai/blog/sre-incident-lifecycle-with-ai-agents): The complete incident process an SRE runs, stage by stage, with what an AI agent can genuinely take over and what has to stay human at each step. - [Automated Incident Remediation: 2026 Guide](https://www.aurorasre.ai/blog/automated-incident-remediation): What automated incident remediation means for SRE vs security, how the five-stage loop works, and where to keep a human on the execute button. - [How to Evaluate an AI SRE Platform (2026)](https://www.aurorasre.ai/blog/how-to-evaluate-ai-sre-platform): A four-pillar framework for evaluating AI SRE platforms in 2026: investigation quality, trust, data sovereignty, and total cost of ownership. - [Self-Hosted AI SRE: The 2026 Deployment Guide](https://www.aurorasre.ai/blog/self-hosted-ai-sre): Self-hosted AI SRE runs the agent, memory, and LLM inside your perimeter. The 2026 architecture for air-gapped, multi-cloud, BYO-LLM deployment. - [How AI Incident Investigation Works: Two Agents Traced](https://www.aurorasre.ai/blog/ai-powered-incident-investigation): AI incident investigation, step by step: every tool call two agents made on the same injected fault, where both failed and what to require. Oct 2026. - [Automated Post-Mortem Generation: 2026 Guide](https://www.aurorasre.ai/blog/automated-post-mortem-generation): Automated post-mortem generation turns chat transcripts, observability data, and investigation traces into retrospectives. 2026 tools and architectures. - [CI/CD Auto-Remediation: How It Works and What Breaks](https://www.aurorasre.ai/blog/cicd-auto-remediation-complete-guide): Auto-remediation in CI/CD means pipelines that detect, diagnose, and recover from failure without paging a human. Architecture, tools, and pitfalls. - [AI Agent kubectl Safety: Sandboxed Execution](https://www.aurorasre.ai/blog/ai-agent-kubectl-safety): Giving an AI agent kubectl access is an architecture decision. OWASP threats, k8s-sigs/agent-sandbox, gVisor, and Aurora's pod-isolated execution model. - [AI SRE in 2026: What It Is, Tested on 19 Kubernetes Faults](https://www.aurorasre.ai/blog/ai-sre-complete-guide): An AI SRE is an LLM agent that investigates alerts. We ran Aurora, HolmesGPT and K8sGPT on 19 injected faults: accuracy, time, cost and every miss. - [Opsgenie Pricing, End of Life Date and Migration (Oct 2026)](https://www.aurorasre.ai/blog/opsgenie-complete-guide-2026): Opsgenie pricing by tier ($9.45 to $31.90 per user), the April 5, 2027 shutdown, what JSM and Compass cost instead, and alternatives. Checked Oct 21, 2026. - [AI Root Cause Analysis vs 5 Whys for SREs: 3 Tools Tested](https://www.aurorasre.ai/blog/root-cause-analysis-complete-guide-sres): AI root cause analysis vs 5 Whys: how SREs do RCA in distributed systems, and how 3 automated RCA tools did on 19 injected Kubernetes faults (Oct 2026). - [Agentic Incident Response: One Real Alert, Every Tool Call](https://www.aurorasre.ai/blog/what-is-agentic-incident-management): Agentic incident response shown on one tested alert: every tool call, the fix, what the agent was blocked from running, autonomy levels, where it fails. ### Tutorials - [How to Fix CrashLoopBackOff in Kubernetes (2026)](https://www.aurorasre.ai/blog/fix-crashloopbackoff-kubernetes): CrashLoopBackOff means a container keeps crashing and Kubernetes is waiting before it restarts again. A step-by-step diagnosis with the exact kubectl commands. ### Comparisons and alternatives - [Edwin AI vs Dynatrace Davis AI vs Datadog Bits AI (2026)](https://www.aurorasre.ai/blog/edwin-ai-logicmonitor-alternatives): Edwin AI, Dynatrace Davis AI and Datadog Bits AI on root-cause evidence, remediation, hybrid coverage and price (checked 2026-10-21), plus tested alternatives. - [Cleric vs Resolve.ai: AI SRE Compared (2026)](https://www.aurorasre.ai/blog/cleric-vs-resolve-ai): Cleric is a per-credit AI SRE that opens fix PRs for approval. Resolve.ai is an autonomous on-call AI SRE funded past $190M. How the two differ, with pricing. - [PagerDuty vs Jira Service Management (2026)](https://www.aurorasre.ai/blog/pagerduty-vs-jira-service-management): PagerDuty is an operations and on-call platform. Jira Service Management is an ITSM service desk with on-call built in. How the two compare for incident response, with pricing. - [HolmesGPT vs Cleric: AI SRE Compared (2026)](https://www.aurorasre.ai/blog/holmesgpt-vs-cleric): HolmesGPT is an open-source, read-only investigation agent. Cleric is a commercial agent that holds the pager and opens fix PRs. How the two differ, with pricing. - [Parallel-Agent Incident Investigation: AWS vs BigPanda](https://www.aurorasre.ai/blog/parallel-agent-incident-investigation-2026): In September 2026 AWS and BigPanda both shipped multi-agent parallel incident investigation. What each launched, how the architectures differ, and when parallel helps. - [Best Automated Post-Mortem Tools: 9 Checked, 1 Real AI Draft](https://www.aurorasre.ai/blog/best-ai-post-mortem-tools): The best automated post-mortem tools, checked Oct 21, 2026: exports, pricing, PagerDuty and Jeli EOL dates, and a real AI draft with what humans must add. - [Azure SRE Agent Alternative: Open Source (2026)](https://www.aurorasre.ai/blog/azure-sre-agent-alternative-open-source): Azure SRE Agent is Azure-locked, always-on billed, and closed source. Aurora is the Apache 2.0, self-hosted, multi-cloud open source alternative. - [Best AI Incident Investigation Tools: 12 Cleric Alternatives](https://www.aurorasre.ai/blog/best-ai-powered-incident-investigation-tools): Cleric alternatives for AI incident investigation, checked October 21, 2026: Traversal, Resolve, Datadog and 9 more. Pricing, self-hosting, 3 tested. - [Rootly vs incident.io: Prices and AI SRE Checked Oct 2026](https://www.aurorasre.ai/blog/incident-io-vs-rootly): Rootly vs incident.io, read off both sites on Oct 21, 2026: per-seat prices, AI SRE add-ons, on-call, status pages, integrations, and where each wins. - [FireHydrant vs incident.io: 2026 Comparison](https://www.aurorasre.ai/blog/firehydrant-vs-incident-io): Freshworks acquired FireHydrant in early 2026; incident.io is independent. Compare AI, pricing, and where open-source Aurora fits for SRE teams. - [Rootly vs FireHydrant: Which Is Better in 2026?](https://www.aurorasre.ai/blog/rootly-vs-firehydrant): Rootly stays independent and AI-native; FireHydrant is now part of Freshworks. Compare AI, pricing, and where open-source Aurora fits in 2026. - [Dynatrace Davis AI Alternatives: Open Source RCA (2026)](https://www.aurorasre.ai/blog/dynatrace-davis-alternative-open-source): Davis AI is locked to the Dynatrace platform and OneAgent. Aurora is a free, open source, self-hosted, multi-cloud alternative for AI root cause analysis. - [Datadog Bits AI Alternatives: Cost per Investigation (2026)](https://www.aurorasre.ai/blog/datadog-bits-ai-sre-alternative-open-source): Bits AI SRE, now Bits Investigation, lists ~6.5 AI credits per investigation: $6.50 to $8.45 by our math. Alternatives priced, 3 lab-tested. - [BigPanda Alternative: Open Source AIOps (2026)](https://www.aurorasre.ai/blog/bigpanda-alternative-open-source): Aurora is a free, open-source, self-hosted BigPanda alternative that investigates and verifies root cause instead of only grouping alerts. Full comparison. - [Grafana OnCall OSS Archived: Alternatives, Fork, IRM Pricing](https://www.aurorasre.ai/blog/grafana-oncall-alternative-open-source): Grafana OnCall OSS was archived on 2026-03-24. Checked 2026-10-21: Grafana IRM pricing, the Appwrite fork, GoAlert, Keep, PagerDuty and incident.io. - [Keep AIOps in 2026: Is the Open Source Platform Still Alive?](https://www.aurorasre.ai/blog/keep-vs-aurora-open-source-aiops): Keep (keephq) open source AIOps, checked 2026-10-21: MIT core, owned by Elastic, community-maintained, v0.54.3. What it does and what an AI agent adds. - [HolmesGPT Review 2026: What It Is and How It Did in 38 Runs](https://www.aurorasre.ai/blog/holmesgpt-alternative-multi-cloud): HolmesGPT is the CNCF Sandbox SRE agent Robusta created. We ran 0.43.0 on 19 Kubernetes faults: 31 of 38 judge-graded correct, about 3x faster than Aurora. - [Replacing Splunk ITSI: Open Source AI SRE Alternatives](https://www.aurorasre.ai/blog/splunk-itsi-alternative-open-source): How to replace Splunk ITSI with an AI SRE: what ITSI does well, what its ingest licensing costs, and the open source, multi-cloud alternatives to evaluate. - [K8sGPT Alternatives: We Tested K8sGPT on 19 Faults (2026)](https://www.aurorasre.ai/blog/k8sgpt-alternative-beyond-kubernetes): K8sGPT alternatives, tested: K8sGPT 0.4.40 saw nothing in 6 of 19 injected faults. Which tool fits each gap: HolmesGPT, Aurora, kagent, Robusta, Komodor. - [New Relic AI Alternative: Open Source RCA (2026)](https://www.aurorasre.ai/blog/new-relic-ai-alternative-open-source): New Relic AI is low-friction for New Relic-native shops. Aurora is open source, self-hosted, multi-cloud, and runs active investigations and remediations. - [Moogsoft (Dell APEX) Alternative: Open Source AIOps](https://www.aurorasre.ai/blog/moogsoft-dell-apex-alternative-open-source): Dell APEX AIOps (formerly Moogsoft) is a closed, contract-gated correlation engine. Aurora is an open source, self-hosted AI SRE alternative. - [AI SRE vs Traditional AIOps Platforms, With a Real Trace](https://www.aurorasre.ai/blog/ai-sre-vs-aiops): AIOps or AI SRE? AIOps groups alerts and flags anomalies; an AI SRE investigates one alert. A full lab trace and 4 vendors' docs, checked Oct 2026. - [HolmesGPT vs K8sGPT: Tested on 19 Kubernetes Faults (2026)](https://www.aurorasre.ai/blog/holmesgpt-vs-k8sgpt): HolmesGPT vs K8sGPT, tested Oct 2026 on the same 19 injected Kubernetes faults: real output from both, MCP, operator mode, kagent, and when to pick each. - [Open Source SRE Agents in 2026: 9 Compared, 3 Tested](https://www.aurorasre.ai/blog/top-ai-sre-tools-2026): Open source AI SRE agents and alternatives, checked October 21, 2026: Aurora, HolmesGPT and K8sGPT run on the same 19 Kubernetes faults, 6 more reviewed. - [HolmesGPT Alternatives, Tested: Aurora vs K8sGPT on 19 Faults](https://www.aurorasre.ai/blog/open-source-ai-sre-aurora-vs-holmesgpt-vs-k8sgpt): Open source HolmesGPT alternatives tested Oct 2026: Aurora and K8sGPT vs HolmesGPT on the same 19 Kubernetes faults, 2 runs each. Misses and raw output. - [AIOps RCA Tools 2026: Pricing, Open Source, 3 Tested](https://www.aurorasre.ai/blog/top-10-aiops-platforms-free-root-cause-analysis-2026): How 14 AIOps tools license root cause analysis, prices checked Oct 21, 2026, and the open source ones measured on 19 Kubernetes faults, every run published. - [FireHydrant Alternatives: Open Source and Paid (2026)](https://www.aurorasre.ai/blog/firehydrant-alternative-open-source): FireHydrant is now part of Freshworks and AI features are Enterprise-only. Aurora is a free, open source alternative with autonomous AI investigation. - [incident.io Alternatives: Open Source Incident Management 2026](https://www.aurorasre.ai/blog/incident-io-alternative-open-source): Open source incident.io alternatives checked Oct 21, 2026: Keep, GoAlert, openstatus and 6 more, two archived ones to avoid, and incident.io pricing. - [Rootly Alternatives: Open Source and Paid Options (2026)](https://www.aurorasre.ai/blog/rootly-alternative-open-source-incident-management): Alternatives to Rootly for incident management and AI investigation, including the free, open source Aurora. Features and pricing compared side by side. - [Resolve AI Alternatives 2026: Real Prices, 3 Tested in a Lab](https://www.aurorasre.ai/blog/resolve-ai-alternative-open-source): Resolve AI publishes no prices. 8 alternatives and their published pricing, checked Oct 21, 2026, plus lab results for the open source ones on 19 faults. - [PagerDuty Alternative for Root Cause Analysis (2026)](https://www.aurorasre.ai/blog/pagerduty-alternative-root-cause-analysis): PagerDuty handles alerting and on-call, but who finds the root cause? Aurora is an open source AI agent that investigates incidents automatically. - [Aurora vs Traditional Incident Management Tools](https://www.aurorasre.ai/blog/aurora-vs-traditional-incident-management-tools): Compare Aurora's open source agentic AI approach with platforms like Rootly, FireHydrant, and incident.io. Verified features, pricing, and use cases.