Hyground vs Rootly

An investigation engine that runs in your cluster

Rootly is a SaaS platform that handles the incident lifecycle: on-call, response automation, retros, and AI suggestions. Hyground is an in-cluster investigation engine that correlates logs, metrics, runbooks, and tickets without telemetry leaving your network.

Different scope, different commitment

Both use a language model to help with incident response. Rootly runs as multi-tenant SaaS and covers the full lifecycle from page to post-mortem. Hyground runs in your Kubernetes cluster and focuses on producing grounded root-cause diagnoses across logs, metrics, runbooks, and ITSM tickets, with data staying inside your network.

Architecture

Where Hyground differs

Six differences that show up when data sovereignty, self-hosted models, or investigation depth are the deciding factors.

In-cluster, not SaaS

Hyground installs via Helm into your own Kubernetes cluster. AI inference, log analysis, and knowledge retrieval all run on infrastructure you control. Rootly is delivered as multi-tenant SaaS, with no publicly documented self-hosted option.

Data stays in your perimeter

Because Rootly is SaaS, incident context flows to Rootly's cloud for processing. That posture works for most US-headquartered teams. For regulated DACH and EU enterprises, the deciding question is often whether telemetry leaves the perimeter at all, and the architectural difference matters.

Any model, including self-hosted

Hyground routes through LiteLLM to Azure OpenAI, OpenAI, Anthropic, Vertex AI, Bedrock, or self-hosted Ollama with open-weights models. Rootly supports bring-your-own-key for SaaS LLM providers. Self-hosted GPU models are out of scope.

Multi-source investigation

Hyground solves problems or analyses root causes based on observability data, domain knowledge, source code and your knowledge base. Rootly's AI works primarily off alerts, code changes, and past incidents already held inside the platform.

Grounded in your documentation

Hyground ingests your technical and domain documentation, your code, incident history and knowledge into a continuously improving knowledge base. Rootly's learning loop is anchored in incident data within the platform, with no documented pipeline for ingesting docs or code.

Investigation, not lifecycle

Rootly is shaped around the incident lifecycle: paging, coordination, comms, retros, status pages. Hyground plugs into PagerDuty, Alertmanager, Slack, Teams, and Jira, but the coordination workflow stays in your existing stack. If the hard part of your incidents is diagnosis across many infrastructure layers, Hyground is built for that one job.

Decision

When each tool fits

Rootly and Hyground sit at different points in the incident workflow. Rootly owns the lifecycle as SaaS; Hyground produces grounded diagnoses inside the cluster. The fit depends on which bottleneck is heavier in your team.

Choose Hyground when

Data sovereignty or investigation depth is the deciding factor.

Telemetry cannot leave your network: regulated DACH or EU industries, air-gapped environments
Investigations need to be grounded in your own Confluence, Git, and runbooks, beyond what incident history alone provides
Your model strategy includes self-hosted LLMs on your own GPUs
Diagnosis across many infrastructure layers is harder than running the response process

Choose Rootly when

Lifecycle breadth in a single SaaS surface is the deciding factor.

You want one SaaS that covers on-call, incident response, retros, and status pages
A US-hosted, multi-region SaaS satisfies your data residency posture
Coordination overhead is the bottleneck: paging, comms, stakeholder updates, post-mortem write-up
Per-seat pricing and self-serve onboarding are operational priorities

See Hyground in action

Try the sandbox, or book a demo to see sovereign AI for DevOps run on your stack.

Try our sandbox Book a demo