Autonomous Intelligent Execution Layer
Zipher's models dynamically route workloads to the most suitable compute in real-time
How Zipher Works
Say Goodbye to Static Cluster Configuration
AI agents now create workloads faster than teams can manage them. Static configurations no longer fit dynamic workloads, and manual tuning is no longer viable. Zipher applies proprietary ML to automate infrastructure configuration end-to-end, learning from historical workload behavior, seasonality patterns, and instance performance trends. This allows Zipher to dynamically allocate infrastructure that is aligned with how workloads actually behave over time.
An ML Based Autoscaler that Understands Context
Sometimes autoscalers can scale up more than expected or take a while to scale back down. This happens when they aren't designed to learn from the patterns of historical runs. Zipher’s ML-based autoscaler adds that missing context, helping manage resource usage in real time, helping teams meet their SLAs while improving efficiency.
Leverage Spot Instances
Dynamically leveraging spot instances can be tricky. That's why data teams are hesitant to use them in production. Zipher empowers data teams to use spot in production by continuously monitoring the availability of spot instances and automatically allocating them to the right workloads.
Reliability Layer for Improved Performance
When a resource issue (e.g., OOM) or spot reclaim occurs, Zipher automatically adjusts the cluster to meet the workload's needs. Easily maintain control through defined cluster policies and target durations.
