Flightcrew

If you were opening a McDonald’s, you wouldn’t wait until the first lunch rush to hire a cashier. You wouldn’t pay a janitor for a full day of work if they checked for a mess once in the morning then just went home the rest of the day. So stop managing your Kubernetes containers like this. KEDA is now 5 years old and we at Flightcrew are celebrating by sharing our top reasons to switch over to a more powerful and adaptable autoscaling platform for every type of workload. 1. Proactively scale up during traffic spikes to exactly match the incoming traffic 2. Coordinate scaling across interdependent microservices to avoid cascading bottlenecks 3. Migrate wasteful “wake and check” data and AI cronjobs to simple, scale-to-zero deployments 4. Run Serverless workloads on Kubernetes itself, achieving the benefits of fully-managed Serverless platforms while maintaining flexibility Read on:

AI can now automate the most repetitive and undifferentiated parts of engineering. One of our most popular automation workflows in early access has been rightsizing Kubernetes pods and nodes through the safe, ubiquitous language of engineers: Github PRs. Here’s a look at how Flightcrew operates like Dependabot to proactively reduce costs and protect uptime on Kubernetes. (read more here! https://lnkd.in/gPnNe44Q)

At Flightcrew we use AI to analyze a firehose of data, and then surface tips, insights, and warnings into an engineer’s native workflow. Here’s a preview of Flightcrew preventing an outage by autonomously checking my PR, detecting blindspots, and generating the corrected PR that I should deploy on Kubernetes. We’re debating internally whether to call it a coach or an intern - suggestions welcome! #platformengineering #devops #kubernetes

We're opening Flightcrew up for Early Access ?? Flightcrew is a config copilot that helps engineers scale and bulletproof cloud infrastructure. You can use Flightcrew to scale Kubernetes for availability and cost, manage misconfigurations and dependencies, and make infrastructure 'self-serve' for your feature teams. https://lnkd.in/eqgeSG-a