LM-Kit OneThe Private AI Application Server
Promoted
Dash0 is the only OpenTelemetry Native observability platform built with developers in mind. With granular, resource-centric monitoring, Dash0 provides real-time visibility across your applications and infrastructure. Its simple, transparent pricing and seamless integration with open standards like Perses, PromQL, and Kubernetes make it a breeze to use. Dash0 helps you quickly spot issues and optimize performance with actionable insights from logs, traces, and metrics, all in one place.
This is the 3rd launch from Dash0. View more
Service Level Objectives · Dash0
Launched this week
Observability-first development for teams shipping AI-generated code. SLOs in Dash0 turn "is it reliable enough?" into one number the whole company agrees on: a target, a rolling window, and an error budget you spend on purpose. Predictive burn-rate alerts catch a slow leak before users do and page only when it is a fire. Align engineering to business goals, release with confidence, and see exactly what every deploy cost you. Now generally available. 25 SLOs per organization, included.
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Launch Team
Hi Product Hunt, Liat here, PM for SLOs at Dash0.
We built this because "is the service reliable enough?" kept getting three different answers from three engineers. With AI writing more of the code, releases are faster and the blast radius is less predictable, so you need a contract that is not a vibe. An SLO is that contract: a target, a rolling 28-day window, and the error budget that falls out of it. When the budget is healthy you ship. When it is burning you slow down. That is observability-first development, and it is how engineering and the business end up looking at the same number.
The proactive part is burn rate. Instead of paging on one bad minute, Dash0 derives fast and slow burn pressure from every SLO with multi-window alerts. Fast burn is a fire and pages you. Slow burn is a leak that would quietly break the promise by the end of the window, so it opens a ticket before users notice.
For the technically curious: pick a service, describe good and total events with two plain PromQL label selectors, set a target. Dash0 builds and manages the recording rules behind it, so there is no plumbing. Every SLO gets a burndown, remaining error budget, and pre-filled burn-rate check rules. Create them in the UI, with Agent0, or as OpenSLO YAML, API and Terraform. 25 SLOs per organization, included.
Would love to hear how you define reliability today, and what you would want an SLO product to do next.
Really happy to see this out in the wild! I’ve been working on the engineering side of SLOs at Dash0, and it’s been a pretty interesting journey getting all the pieces together.
One of the things we cared about was making SLOs feel like a natural part of the observability workflow rather than yet another isolated system.
Very curious to hear how people are using it in practice, especially at scale. Feedback, weird use cases and edge cases are all very welcome :)