Strake tells you whether it's safe to push right now — based on system health, error budget burn, open incidents, and deploy velocity. When things break anyway, your runbooks are connected to alerts, your team knows what to do, and the knowledge doesn't live only in whoever has been here longest.
Teams are running more infrastructure with fewer SREs than at any point in the last decade. The tools haven't kept up.
Error budget at 18% remaining with SLO window closing in 72 hours. 2 active incidents on service dependencies. 3 deploys in the last 2 hours. Risk of cascading failure is elevated.
This is not two disconnected products. Deploy Gate and Runbooks feed each other — and feed back into a risk model that improves over time. That's the flywheel.
Before every deploy, Strake reads system health, error budget, incident state, and change velocity. It gives a GO or HOLD recommendation — with reasoning.
When an alert triggers, the connected runbook opens. Steps are tracked. The engineer works through it — what they found, what they decided, what resolved it.
Every incident record becomes a data point — what the deploy context was, which runbook ran, how long resolution took, whether the gate called it right or wrong.
Strake uses the accumulated incident history to refine what signals actually predict failures in your specific system. After 12 months, you have a model no competitor can replicate.
Every deploy Strake evaluates makes the next risk signal more accurate. Every incident it runs makes the next runbook faster. The data moat compounds — and it's built from your own production history, not a generic model.
Every deploy decision is based on four live signals pulled from your existing stack. No new agents. No new dashboards. Strake reads what's already there and tells you what it means right now.
How much error budget is left in the current window, and how fast it's burning. Strake flags when a deploy risks exhausting the rest of it before the window closes.
Open incidents across the service and its direct dependencies. Deploying into an active incident almost always makes things worse and root cause harder to find.
How many deploys have gone out in the last few hours. High velocity makes root cause isolation nearly impossible when something breaks.
Current health of the target service and its dependency graph — latency, error rates, resource saturation. The baseline you're deploying into matters.
Every incident Strake runs makes the next deploy decision smarter. After 12 months, your deployment and incident history is a risk model trained on your specific system — a model no competitor can replicate, because they don't have your data.
// The MoatStrake connects runbooks directly to the alerts that trigger them. When PagerDuty fires, the right runbook opens — not a search bar, not a Notion space, not a Slack message asking who knows what to do.
Steps are tracked. What the engineer found, what they decided. Every time the runbook runs, the record gets richer. The next engineer who gets paged starts from that, not from zero.
The Nov 22 incident took 94 minutes. The last three averaged 27 minutes. That's the runbook getting smarter — and it's the clearest signal of what Strake actually does.
+ CloudWatch · Terraform · Loki · Confluence · Notion · GCP Cloud Run · AWS ECS · and more
The runbook for our database failover lived in a Confluence page that hadn't been opened in 14 months. Found that out at 3am on a Sunday.
Senior SRE · Series B fintechWe had a deploy gate. It was a Slack message: "anyone know if it's okay to push right now?" Someone always said yes. Then we'd find out.
Staff Engineer · Infrastructure teamThe tribal knowledge problem is real. Three incidents in the last year that came down to one person not knowing what another person knew.
VP Engineering · 80-person startupStrake is in private beta with a small cohort of senior engineers and SRE leads. If you're on-call and your MTTR isn't where it needs to be, we want to talk.