Your data, your model provider, your control.
Most AI-powered observability assistants run against a single vendor-hosted model you have no say in, so your telemetry reaches infrastructure you didn't choose. Raiya lets you configure the model provider, AWS, Azure, Google, Anthropic, or OpenAI, under the account you already have. A fully self-hosted option is coming, for teams that want inference to never leave their own environment at all.
Most AI observability assistants are wired to one backend model, controlled entirely by the vendor, with no way to route inference through a provider or account you already trust.
Raiya can run against AWS, Azure, Google, Anthropic, or OpenAI, whichever you already have an account and a data-processing relationship with, not a new one Randoli introduces.
A vendor's promise not to move your data is still just a promise, written into a DPA that can change with the next contract renewal.
Randoli's own control plane has no path to receive raw observability telemetry, logs, traces, metrics, cost, or security data. There's nothing to promise not to send, because the pipeline was never built to send it.
Turning on a typical AI assistant means trusting the vendor's own AI backend, a new third-party processor for your compliance team to review from scratch.
Point Raiya at the AWS, Azure, Google, Anthropic, or OpenAI account your organization already has a contract and DPA with, instead of a new one Randoli introduces.
If the AI has to reach a fixed vendor cloud to work, a disconnected or data-residency-restricted environment simply can't use it.
Today, Raiya's correlation and detection run fully locally, and you choose which model provider handles reasoning. A fully self-hosted model option is coming, extending that same guarantee to a disconnected environment.
Some vendors respond to data-sensitivity concerns by sampling or redacting telemetry before their AI sees it, so the model reasons over a partial picture.
Raiya's correlation engine works against your complete logs, traces, and metrics. When it calls a model, it sends the real evidence, not a redacted copy, because that call goes to a provider and account you chose, not a shared multi-tenant service you don't control.
Control over the pipeline today. A path to full control over inference.
Whether the requirement comes from a regulator, a government sovereign-cloud mandate, or your own security team, the observability pipeline stays local regardless of connectivity, and you decide who processes the data an AI model needs to see.
- ✓Observability, cost, and security data are processed locally, by architecture, regardless of which model provider is configured
- ✓You choose the AI model provider, AWS, Azure, Google, Anthropic, or OpenAI, using the account and contract you already have, not one Randoli introduces
- ✓A fully self-hosted model option is coming, for teams that need AI reasoning to stay inside a disconnected or air-gapped environment too
- ✓Runbooks and permissions are defined and enforced by your team, through your own RBAC
- ✓The Randoli control plane receives derived signals and correlation results only, never raw telemetry
See the full data-sovereignty architecture on SRE Agent (Raiya).