Rootly vs incident.io: Prices and AI SRE Checked Oct 2026

incident.io is cheaper to start: it has a free Basic plan, and its Team plan is $15 per user a month billed annually, plus $10 for on-call. Rootly is $20 per user a month for Incident Response, which includes status pages and its AI scribe, and another $20 for On-Call. Neither publishes a price for its AI SRE agent: incident.io sells Investigations as an add-on on Pro and Enterprise, and Rootly sells AI SRE as a separate product that requires Incident Response. The clearest product difference is the trigger: Rootly's agent can investigate an alert the moment it arrives, while incident.io's investigates incidents, so an alert has to become an incident first.

Every cell below was read on incident.io's and Rootly's own pricing pages and docs on 2026-10-21 and links to its source. We have not used either AI SRE agent: both are hosted, and Rootly's AI SRE is turned on by its account team. Arvo AI builds Aurora SRE, an open source investigation agent that already connects to incident.io; that is the one section about us, near the end.

Rootly vs incident.io at a glance (checked 2026-10-21)

Where incident.io is better

• Cost of entry. A free plan exists, and a 10-person team on Team with on-call lists at $250 a month at the annual price (our arithmetic: 10 × ($15 + $10); the page does not say whether the on-call add-on is also discounted annually). Rootly has no free plan, and the same team on Incident Response and On-Call lists at $400 a month.
• Kubernetes and private telemetry for the agent. Investigations reads any Kubernetes cluster you connect, by kubeconfig or discovered through AWS (EKS) or Google Cloud (GKE), read-only (docs), and for sources inside your network you run a proxy and attach the source to it (docs). Rootly reaches EKS through its AWS connector, but other clusters and private-network sources go through Private Agent, which is an Early Preview "available only to approved customers".
• Accuracy method in the open. incident.io documents how it grades each investigation against the cause your responders established, on a four-point scale (0%, 35%, 65%, 100%), and shows the scores in your account (docs). That is the closest either vendor comes to a test a buyer can read. It is still your incidents and their grader, with no published totals.
• Status pages. Every incident.io plan, including the free one, gets a public status page with a custom domain and unlimited subscribers (pricing), and status pages are "not sold as a separate product" (status pages FAQ). Below Enterprise the pricing table caps public pages at one, even though the same FAQ says there are no limits on the "number of status pages"; ask before you plan on several.

Where Rootly is better

• AI at the base price. Rootly's $20 Incident Response seat includes the @Rootly agent, AI Scribe and AI Retrospectives. On incident.io, Scribe, Suggestions and the AI post-mortem editor start at Pro, $25.
• Investigating alerts, not just incidents. An Auto-run rule can start Rootly AI SRE on a new alert that matches it, before anyone declares anything (docs). incident.io's escalation path can wait for an investigation, but only once an incident exists.
• Azure and Google Chat. Rootly has Azure Monitor and Azure Resource Manager connectors for its AI and runs incidents in Google Chat. We found neither on incident.io's telemetry or pricing pages.
• Schedules and on-call extras on the base seat. Rootly On-Call includes 20 schedules, the pay calculator and readiness reports at $20. incident.io's Team plan stops at 2 schedules, and the compensation calculator and readiness reports are Pro features.
• Your own model key and startup pricing. Rootly lets Enterprise customers bring OpenAI, Anthropic or Gemini keys, and offers up to 50% off for startups under 100 employees, under $50M raised and under 5 years old.

One thing to check before you connect Rootly to AWS: its AWS connector passes through the AWS MCP tool catalog without a read-only tool-name filter, and "with all services allowed, the IAM role's policies are the limit" (docs). Keep the IAM role read-only, and treat other connectors the same way: Rootly's AI SRE docs say plainly not to treat AI SRE as read-only.

Which incident management tools charge extra for AI features?

Both, for the part that investigates. incident.io's Investigations (root cause analysis, the autonomous agent, Nexus data sources) are an add-on on Pro and Enterprise and are not available on Basic or Team. Rootly's AI SRE (probable root cause with evidence, alert-to-change correlation, drafted PRs) is a separate product on top of Incident Response, priced through sales or bundled into Enterprise. Rootly's pricing page lists "A separate add-on for every AI feature" under "What you won't find here"; in practice the drafting and summarizing AI is in the $20 seat and the investigating AI is not. You need a sales call to learn the AI SRE price from either vendor.

Does Rootly automate root cause analysis during incidents?

Yes, with AI SRE. Rootly's docs describe an evidence-first loop: read the alert or incident, measure the signal, draft competing explanations, query connected tools to confirm or rule out each one, challenge the leader, then write a verdict that cites evidence. The outcome can be "Root cause identified" (high, medium or low confidence), "Contributing factor identified", "Inconclusive" (no cause confirmed, a human is needed), "Blocked" (missing access) or "Could not investigate" (docs). incident.io's Investigations work the same way at the level the docs describe: "It forms a hypothesis and then tests it, reading your code and querying your dashboards to confirm or rule it out" (docs), and they use "an adversarial agent to challenge its own conclusions before sharing" (AI SRE).

How do you test either AI SRE before you buy?

Neither vendor publishes a test set, so bring a known fault. Each scenario in our open lab, Arvo-AI/aurora-lab, injects one Kubernetes failure and records the alert, the true root cause and the findings a correct answer must contain (must_identify in scenario.yaml).

git clone https://github.com/Arvo-AI/aurora-lab && cd aurora-lab
./lab.sh up
./lab.sh break 07-service-selector-mismatch

Connect the cluster to the trial: in incident.io, a kubeconfig behind its proxy (a local lab cluster is not reachable from the internet); in Rootly, Private Agent if you are approved, or the AWS connector if you run the lab on EKS. Send the scenario's alert, declare an incident for incident.io, let the agent run, grade its answer against must_identify, then ./lab.sh reset. Try a second fault the alert does not cover too (scenario 12): in our runs, both alert-driven agents we tested found the fault the alert named and missed the other one. For a single failure on your own cluster, there is /diagnose.

Where does Aurora SRE fit next to incident.io or Rootly?

Aurora is an open source (Apache 2.0) investigation agent, not an on-call tool or a status page, so it sits behind either platform rather than replacing it. The README lists incident.io as an integration; Rootly is not listed, and Aurora has no Rootly connector today.

With incident.io, we built and use the round trip. Aurora takes incident.io's public_incident.incident_created_v2 and public_alert.alert_created_v1 webhooks, investigates, and when the investigation completes posts the root cause back onto the same incident with POST /v2/incident_updates, keyed aurora-rca-<id> so a retry cannot post twice (PR #681, merged 2026-10-02). For an alert-triggered run it posts onto the alert's incident, passing over declined, merged and canceled ones; when there is no such incident it leaves a note on the alert instead. Testing it on our own incident.io account, we found that incident.io renders markdown in updates but collapses single newlines, so the note is built for that. The agent can also read incident.io incidents and timelines while it investigates. With Rootly, point the same monitors (Datadog, Grafana, PagerDuty, New Relic and others) at Aurora directly; the result will not land on the Rootly incident.

What we measured: on 19 injected Kubernetes faults, two runs each, Aurora was correct in 34 of 38 runs (median 153.5 s, median model cost $0.35), graded by a judge model and read by a person at Arvo. HolmesGPT 0.43.0 was correct in 31 of 38 and about 3x faster at the median (45 s); its grades are judge-model only. Same cluster and faults, but the prompts differed slightly (each tool got the alert plus a one-line instruction in its own wording), and Arvo wrote the scenarios and builds Aurora. Method and caveats are on /benchmark and in the lab report. We have no comparable numbers for incident.io or Rootly, because neither publishes any.

Related comparisons

• AI incident investigation tools compared, including incident.io Investigations and Rootly AI SRE
• incident.io alternatives and Rootly alternatives
• FireHydrant vs incident.io and Rootly vs FireHydrant
• Aurora vs HolmesGPT vs K8sGPT

Verified 2026-10-21 on incident.io's pricing, AI SRE, on-call, status pages and integrations pages and docs, and on Rootly's pricing (including pricing.md), AI SRE, integrations and llms.txt pages and docs, as linked. Prices are list prices in USD per user a month. Both vendors change packaging often, so recheck the pricing pages before you sign.

Aurora is on GitHub, or book a demo to see it post an RCA into incident.io.