Hyground vs
Rootly
Hyground vs Rootly: investigation that runs inside your cluster
Rootly runs the whole incident lifecycle, and its AI SRE investigates alerts through read-only connectors into around twenty-seven of your tools. All of it runs in Rootly's cloud. Hyground runs the investigation inside your cluster, against a model you pick yourself, and leaves the lifecycle to your incident platform.
A fair starting point
Rootly is a complete incident platform: paging, response in Slack or Teams, retrospectives, status pages, and an AI SRE that investigates alerts on its own and hands back a likely root cause. The agent is scoped tightly. It can do only what the person who asked could do themselves, it is audited under that person's name, and its connectors are read-only across observability, cloud, code and documentation. If residency is the sticking point, Rootly will route AI requests through your own Azure OpenAI deployment. None of that changes the location. The agent reasons in Rootly's cloud, against credentials you hand it. Hyground covers the investigation only, and every part of it runs inside your cluster.
Side by side
Hyground vs
Rootly
at a glance
What matters
Hyground
Rootly
Where it runs
Entirely in your own Kubernetes cluster, on-premises and air-gapped included.
In Rootly's cloud. An Edge Connector reaches internal systems by outbound-only polling.
Where your telemetry goes
Nowhere. It is queried in place, and the only traffic leaving is the call to your model provider.
Connectors query your tools from Rootly's cloud, read-only, with per-connector limits on what each may read.
LLM choice
Any provider through LiteLLM: a cloud model in your own tenant, a self-hosted model, or any OpenAI-compatible API.
Rootly's own models, or your own Azure OpenAI deployment for incident workflows. No self-hosted option.
Kubernetes access
The full cluster API from inside the cluster, read-only and RBAC-scoped.
Through the AWS connector with EKS support, read-only, from Rootly's cloud.
What the agent can change
Nothing by default. Hyground diagnoses and recommends, and changes happen only if you enable them.
In Slack it pages responders, updates the incident, assigns roles and drafts comms, capped at the asking user's own permissions.
On-call, response and retrospectives
Not offered. Hyground takes the alert and hands findings back.
The whole lifecycle: paging, response, retrospectives, status pages and workflows.
Your documentation and runbooks
Confluence Cloud and on-premises, up to 100 Git repositories, Artifactory and uploaded files, embedded inside your cluster.
Confluence, Notion, GitHub and GitLab, read through connectors during an investigation.
Pricing model
Priced on infrastructure size, not seats. Quote on request.
Per user per month, with AI SRE priced on request.
Why teams choose Hyground
Where Hyground differs
Decision
When each platform fits
Rootly runs the incident; Hyground runs the investigation inside your cluster. They overlap on the diagnosis and nowhere else, so the decision is usually about what your security review will accept.
Choose
Rootly
when
You want paging, response, retrospectives and status pages in one platform. You can live with a SaaS control plane. And you would rather the AI sat inside the tool your incident process already runs on.
FAQ
Hyground vs
Rootly
:
common
questions
Is Hyground an alternative to Rootly?
For the investigation, yes, and Hyground runs it inside your own cluster. Rootly runs the incident lifecycle, and its AI SRE reasons in Rootly's cloud. Hyground does the investigating and hands the findings back to Rootly.
Do we have to replace Rootly?
No. Rootly keeps on-call, response, retrospectives and status pages, the alert goes to Hyground, and the investigation is posted back into the incident.
Where does the investigation run?
With Hyground, inside your cluster. Hyground queries the cluster API, Prometheus, Loki, Elasticsearch, OpenSearch, Jaeger and InfluxDB there, and the only traffic that leaves is the call to the model provider that you choose. Rootly's AI SRE queries your tools from Rootly's cloud, with the credentials that you give it.
Can we choose the model, or host it ourselves?
Yes. Hyground connects to any LLM provider through LiteLLM, including a cloud model in your own tenant or a model that you host yourself. Rootly can route incident workflows through your own Azure OpenAI deployment, but the model still runs in a cloud, and nothing self-hosted is supported.
How do we get Hyground running, and what does it cost?
A forward deployed engineer works with your team from install to daily use. The engineer connects your stack, builds any connector that you are missing, and runs the training and workshops. Hyground is priced on the size of the infrastructure that it covers, not per seat, and you get a quote on request.