Resolve.ai Alternative: Open Source AI for Incident Investigation

Key Takeaway: Resolve.ai is a $1B-valued AI SRE platform used by Coinbase, DoorDash, and Salesforce — but pricing requires contacting sales with no public pricing page. Aurora is an open source (Apache 2.0) alternative that delivers autonomous AI investigation with sandboxed cloud execution, infrastructure graphs, and knowledge base search — completely free and self-hosted.

What is Resolve.ai?

Resolve.ai is an AI-powered autonomous SRE platform founded in 2024 by Spiros Xanthos (former SVP at Splunk, co-creator of OpenTelemetry) and Mayank Agarwal. It raised $125M in Series A at a reported $1 billion valuation, backed by Lightspeed and Greylock with angels including Fei-Fei Li and Jeff Dean.

Resolve.ai positions as "machines on call for humans" — a multi-agent AI system that autonomously investigates production incidents across code, infrastructure, and telemetry.

Notable customers: Coinbase (73% faster time to root cause), DoorDash (87% faster investigations), Salesforce, MongoDB, Zscaler, Toast, Pinecone.

What is Aurora?

Aurora is an open source (Apache 2.0) AI agent for automated incident investigation and root cause analysis. When an alert fires, Aurora's LangGraph-orchestrated agents autonomously query your infrastructure across AWS, Azure, GCP, OVH, Scaleway, and Kubernetes — correlating data from 25+ tools and delivering a structured RCA with remediation recommendations.

Aurora is free, self-hosted, and works with any LLM provider including local models via Ollama.

How They Compare

AI Investigation Approach

Resolve.ai:

• Multi-agent architecture with parallel hypothesis testing

• Formulates multiple theories per incident, deploys sub-agents to investigate each simultaneously

• Correlates alerts across services and dependencies

• Constructs causal timelines linking code changes, infra events, and telemetry

• Generates root cause analysis with confidence scores

• Human-in-the-loop approval gates before automated actions

• Per-customer fine-tuned models

Aurora:

• Multi-agent architecture via LangGraph with dynamic tool selection (30+ tools)

• Correlates alerts across services and dependencies (AlertCorrelator + Memgraph graph)

• Constructs investigation timelines linking deployments, infra events, and telemetry

• Generates structured RCA with evidence citations and remediation steps

• Human-in-the-loop for write/destructive actions — read-only commands run automatically

• Executes kubectl, aws, az, gcloud commands in sandboxed Kubernetes pods (non-root, read-only filesystem, capabilities dropped, seccomp enforced)

• Queries cloud APIs directly — AWS (STS AssumeRole), Azure (Service Principal), GCP (OAuth), OVH, Scaleway

• Traverses Memgraph infrastructure dependency graph for blast radius

• Searches Weaviate knowledge base (vector search over runbooks and past incidents)

• Works with any LLM provider — choose your own model

Cloud & Infrastructure

Resolve.ai:

• AWS and GCP confirmed

• Azure is not listed on their integrations page

• Kubernetes support confirmed

• Deploys an on-premise "satellite" agent as a secure gateway — core platform runs in Resolve's cloud

Aurora:

• AWS, Azure, GCP, OVH, Scaleway — all five with native authentication

• Deep Kubernetes integration via outbound WebSocket kubectl-agent

• Fully self-hosted — Docker Compose or Helm chart

• No data leaves your environment

Integrations

Resolve.ai (resolve.ai/integrations):

• Monitoring: Grafana, Datadog, Splunk, Prometheus, Dynatrace, Elastic, Chronosphere, Kloudfuse, OpenSearch

• Infrastructure: Kubernetes, AWS, GCP

• Code: GitHub

• Chat: Slack

• Knowledge: Notion

• Custom: MCP, APIs, Webhooks

• Total: ~17+ confirmed

Aurora (github.com/Arvo-AI/aurora):

• Monitoring: PagerDuty, Datadog, Grafana, New Relic, Netdata, Dynatrace, Coroot, ThousandEyes, BigPanda, Splunk

• Cloud: AWS, Azure, GCP, OVH, Scaleway

• Infrastructure: Kubernetes, Terraform, Docker

• CI/CD: GitHub, Bitbucket, Jenkins, CloudBees, Spinnaker

• Docs: Confluence, Jira, SharePoint

• Network: Cloudflare, Tailscale

• Communication: Slack

• Total: 25+ confirmed

Knowledge & Learning

Resolve.ai:

• Learns from runbooks, wikis, chats, and historical incidents

• Builds a knowledge graph of infrastructure components

• Captures tribal knowledge from production systems

• Per-customer fine-tuned models that improve from feedback (thumbs up/down)

Aurora:

• Built-in Weaviate vector store for semantic search over runbooks, postmortems, and documentation

• Memgraph infrastructure dependency graph maps relationships across all cloud providers

• Learns from past investigations stored in the knowledge base

Code Fixes & Remediation

Resolve.ai: Generates remediation PRs via GitHub with supporting context. Suggests kubectl commands and scripts. All actions require human approval before execution.

Aurora: Suggests code fixes with diff preview — human reviews and creates PR with one click via GitHub and Bitbucket. Executes read-only CLI commands in sandboxed pods. Generates postmortems exportable to Confluence and Jira.

Feature Comparison

Resolve.ai has, Aurora doesn't:

• Automatic JIRA ticket updates during investigation

• Enterprise support with SLAs

• Available on AWS Marketplace

Aurora has, Resolve.ai doesn't:

• Azure, OVH, and Scaleway cloud support

• Open source (Apache 2.0) — full codebase auditable

• Self-hosted deployment (Docker Compose, Helm)

• LLM provider flexibility (OpenAI, Anthropic, Google, Ollama for air-gapped)

• Slack incident channel creation and management

• PagerDuty, New Relic, BigPanda, ThousandEyes, Coroot integrations

• Terraform/IaC state analysis

• Bitbucket, Jenkins, CloudBees, Spinnaker integrations

• Confluence and SharePoint integration

• Network integrations (Cloudflare, Tailscale)

• Free — no licensing costs whatsoever

Both have:

• Autonomous AI incident investigation

• Multi-agent architecture

• Root cause analysis with evidence

• AI-suggested code fixes (human-approved PRs)

• Infrastructure dependency/knowledge graph

• Knowledge base search (runbooks, wikis, past incidents)

• Kubernetes investigation

• AWS and GCP support

• Datadog, Grafana, Splunk, Dynatrace integrations

• Slack integration

• RBAC and security controls

• AI that learns from user feedback

• Causal timeline construction with dependency chain mapping

• Human-in-the-loop for destructive actions

• Per-customer tuning (Resolve.ai via fine-tuned models; Aurora via open source customization)

• SOC 2 Type II compliance (Resolve.ai: certified; Aurora: in progress)

Pricing

Resolve.ai:

• No public pricing page

• Custom enterprise pricing (contact sales)

• No free tier or self-service signup

• Target: large enterprise SRE teams

Aurora:

• Free — Apache 2.0, self-hosted

• Costs: infrastructure (VM or K8s cluster) + LLM API usage

• $0 LLM cost with Ollama local models

• No contracts, no sales calls, no per-user pricing

The price difference is the core story. Resolve.ai delivers enterprise AI investigation for enterprise budgets. Aurora delivers open source AI investigation for everyone else.

Open Source vs Enterprise SaaS

Resolve.ai is a closed-source, cloud-hosted enterprise platform. You cannot audit the AI's reasoning, choose your own LLM, or self-host. Your incident data flows through Resolve's infrastructure (they state they don't persist raw data or train across customers).

Aurora is fully open source. You can:

• Read every line of code the AI uses to investigate your infrastructure

• Self-host with zero data leaving your environment

• Use any LLM provider — or run local models for fully air-gapped operation

• Modify investigation workflows, add custom tools, fork for your needs

• Contribute back to the project

When to Choose Resolve.ai

• You're a large enterprise company with budget for enterprise AI tooling

• Managed fine-tuned models — you want the vendor to handle per-customer model training rather than customizing open source yourself

• You need certified compliance today — SOC 2 Type II, HIPAA, GDPR already certified (Aurora's SOC 2 is in progress)

• Managed service preferred — you don't want to maintain AI infrastructure

When to Choose Aurora

• Budget matters — you can't justify custom enterprise pricing for AI investigation

• Open source is required — you need full transparency into how AI investigates your production systems

• Self-hosted is required — compliance, data sovereignty, or air-gapped environments

• Multi-cloud breadth — you need Azure, OVH, or Scaleway alongside AWS and GCP

• LLM flexibility — you want to choose your own provider or run models locally

• You're a startup or mid-market — Resolve.ai has no mid-market pricing

• You want a custom integration — the Arvo AI team actively builds custom integrations for companies at no cost. If there's a feature gap, reach out and they'll build it with you.

Getting Started with Aurora

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

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

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Configure your monitoring webhooks (PagerDuty, Datadog, Grafana), add cloud provider credentials, and investigations start automatically. See the full documentation for deployment guides.

Further Reading

• Aurora vs Traditional Incident Management Tools — Comparison with Rootly, FireHydrant, incident.io

• PagerDuty Alternative for Root Cause Analysis — PagerDuty vs Aurora deep dive

• Rootly Alternative: Open Source AI Incident Management — Rootly vs Aurora

• Aurora Documentation — Full setup and configuration guides

Originally published at arvoai.ca by team arvoai.ca