solution
Safe Kubernetes Optimizatiation
Let Flightcrew manage every pod, node and scaler through safe, GitOps automation

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The Problem
Everybody knows their Kubernetes clusters are overprovisioned, so why aren’t they sized and scaling correctly?
- Kubernetes is complex Requiring engineers to understand multiple (shared) layers of infrastructure
- Optimization workflows are slow and frustrating. Engineers want to ship code and stay out of configs - not play guess and check with configs and dashboards.
- Autoscaling takes time to master. Picking the right autoscaler, deploying and tuning for every workload & cluster is a full-time job.
- Vendors require invasive permissions to automate sizing recommendations and realize cost savings.
Using Flightcrew to Optimize Kubernetes
Flightcrew is a config copilot that optimizes Kubernetes for availability and cost without invasive permissions.
- Flightcrew monitors, forecasts and evaluates workloads and infrastructure
- Engineers are alerted when
- Workloads are failing, at-risk or wasteful
- A configuration change will have a negative impact on their workload, or a neighboring workload
- Flightcrew generates GitHub PRs with the correct configuration.
- Pod and Node Sizing
- Resource Lifecycle
- Pod and Node Autoscaling (including KEDA and Karpenter)
- Engineers review and deploy Flightcrew recommendations through standard CI/CD processes
Why Flightcrew
Flightcrew uses a holistic understanding of Kubernetes workloads to generate personalized sizing and scaling recommendations for every workload:
- Flightcrew recommendations understand architecture, framework and dependencies (ex: customScaler: QueueSize for QueueConsumer).
- Flightcrew understands resource lifecycles, pod and node interactions and autoscaling behavior
- Flightcrew uses sophisticated AI/ML to understand workload utilization across time periods, replicas and clusters.
- Recommendations are surfaced and approved through native engineering workflows: no invasive permissions and no unreviewed changes
- Flightcrew makes Kubernetes accessible and self-serve through guardrails, AI explanations and conversation
Results
- Save >40% in cloud costs from personalized workload, node and autoscaling recommendations, deployed through safe, GitOps workflows
- Save >10% of engineering time by simplifying Kubernetes resource management
- Prevent incidents from resource starvation and node bullying