How Mistral Monitors Models and Products at Shipping Speed with Checkly

Checkly did not just help Mistral create more checks. It helped Mistral turn monitoring into a reproducible workflow.

• Consistency across public models

Mistral now has a repeatable, Terraform-managed way to apply the same monitoring standards across public models, rather than rebuilding that logic by hand every time a new model ships.

• Cleaner operations

With Checkly sending alerts into Rootly, and Rootly grouping related incidents, the team gets a more scalable alerting flow and a monitoring stack that is easier to reason about.

• Developer self-service

Because monitoring now lives in code, Mistral built abstractions on top of Terraform so developers do not need to work directly with every Checkly or Rootly resource to make common changes. That makes monitoring easier to extend across teams and easier to review as part of the normal development workflow.

That self-service model is now expanding again through AI coding agents. Internally, Mistral uses its own coding agent, Mistral Vibe, to let developers configure monitoring through conversation rather than code. Developers can ask the agent to add a new team, configure checks, or modify alert routing.

As Devon puts it, "As long as you provide the coding agent proper documentation, it can figure out how to perform certain tasks. 98% of the time I can just pass it off, go get a cup of coffee, and come back and the tests pass."

For Mistral, that changes the SRE team's role. Instead of manually stitching together checks and alert rules, they can focus on building the abstractions, standards, and guardrails that let the rest of the organization move faster.

What's Next

Mistral is already exploring how AI agents can assist with incident investigation by pulling context from tools such as Checkly, Grafana, and Rootly via MCP (Model Context Protocol) servers. The goal is not just faster alerting, but faster understanding, so engineers spend less time assembling context and more time resolving the issue.

The team is also looking at checks that adapt to their code: a system smart enough to correlate a check failure with a recent deployment, roll back the offending change, and notify the team without waking an engineer at 3 a.m.