Komodor is an autonomous AI SRE platform for Kubernetes. Powered by Klaudia, it’s an agentic AI solution for visualizing, troubleshooting and optimizing cloud-native infrastructure, allowing enterprises to operate Kubernetes at scale.
Proactively detect & remediate issues in your clusters & workloads.
Easily operate & manage K8s clusters at scale.
Reduce costs without compromising on performance.
Guides, blogs, webinars & tools to help you troubleshoot and scale Kubernetes.
Tips, trends, and lessons from the field.
Practical guides for real-world K8s ops.
How it works, how to run it, and how not to break it.
Short, clear articles on Kubernetes concepts, best practices, and troubleshooting.
Infra stories from teams like yours, brief, honest, and right to the point.
Product-focused clips showing Komodor in action, from drift detection to add‑on support.
Live demos, real use cases, and expert Q&A, all up-to-date.
The missing UI for Helm – a simplified way of working with Helm.
Visualize Crossplane resources and speed up troubleshooting.
Validate, clean & secure your K8s YAMLs.
Navigate the community-driven K8s ecosystem map.
Who we are, and our promise for the future of K8s.
Have a question for us? Write us.
Come aboard the K8s ship – we’re hiring!
Here’s what they’re saying about Komodor in the news.
Cost and Performance Optimization
Komodor autonomously slashes between 40–70% of cloud compute costs while improving reliability and performance. The platform continuously searches for opportunities to optimize cloud native resources – overprovisioned nodes, autoscaler inefficiencies, underutilized GPU instances and more.
In complex cloud native environments, reliability and cost are inseparable. Overprovisioning ensures uptime but drives waste, while aggressive cost-cutting risks performance. Komodor enables SRE and platform teams to maintain reliability for business-critical applications while saving money through reduced cloud costs.
Komodor provides a unified view of Kubernetes spend across the organization – incorporating actual cloud pricing that includes discounts, and custom on-prem unit costs. It enables cost allocation by business unit, team, environment, or application to identify trends, detect anomalies, and uncover inefficiencies. Clearly mapping cost and optimization opportunities allows for true cost aware ownership and collaboration across engineering, FinOps, and platform teams. With Komodor, engineering, platform and FinOps teams can align and collaborate effectively to save costs while ensuring continuous reliability.
Komodor removes guesswork by analyzing real-time and historical CPU and memory data, application behavior, and health signals to recommend and maintain optimal resource settings. It continuously right-sizes workloads to cut waste from overprovisioning and prevent issues caused by underprovisioning, ensuring efficiency and reliability across changing demand. Komodor also aligns KEDA and HPA scaling triggers, allowing event-driven and horizontal autoscaling to work seamlessly as one adaptive scaling system.
Komodor makes autoscaling faster, smarter, and more reliable. It actively analyzes and refines Cluster Autoscaler and Karpenter scaling behavior by detecting inefficiencies, conflicting parameters, sub-optimal instance types selection or poor bin-packing decisions to improve performance, reliability, and cost efficiency. The platform also optimizes node-level placement and instance type selection to maximize density and reduce idle capacity. When current configurations pose risks, such as overly aggressive thresholds, unbalanced limits, or underutilized nodes, Komodor identifies them early, preventing instability and unnecessary spend. The result is an intelligent, self-optimizing autoscaling layer that keeps your infrastructure lean, balanced, and dependable.
“By cutting down overspend without compromising performance, we could reinvest those savings into our creative operations. Komodor didn’t just save us money; it made our Kubernetes architecture smarter.”
Mark
Head of Cloud Platforms, Travel Technology Company
Komodor supports both fully-autonomous and co-pilot operation modes, giving teams full control over how optimization decisions are applied to meet performance requirements. Fine-grained policies let you define what should run automatically and what requires review, along with guardrails like buffers, ranges, and scopes to align with organizational policies and standards.
Komodor optimizes binpacking by intelligently placing pods where they give the cluster maximum flexibility for later node consolidation. It automatically identifies unevictable pods (i.e., pods with bound volumes or pods without an ownerReference that can reschedule them) and directs them to dedicated nodes so they don’t block scale-down events. This keeps other nodes flexible, reducing fragmentation, and enabling autoscalers to consolidate and remove nodes far more effectively.
Komodor accelerates scaling with Smart Headroom, a pre-allocated, dynamic capacity buffer that allows new workloads to be scheduled instantly without waiting for node cold starts. It ensures burst-ready capacity and consistent performance during traffic spikes while avoiding the waste of blanket overprovisioning.
PodMotion enables zero-downtime migration of Kubernetes stateful workloads, automatically moving pods across nodes without disrupting availability. It allows teams to reduce costs, boost efficiency, and manage infrastructure events like upgrades without affecting applications, while also taking advantage of spot instances and better bin packing for additional savings.
Faster incident management with Komodor’s AI SRE platform helps SRE teams focus and reduces incident impact on customers.
Operational friction slows development and degrades productivity. Komodor gives developers the self-service capabilities they need to resolve issues independently, sharply reducing TicketOps for SRE teams.
Improved performance and uptime protect the bottom line and maintain customer trust.
Gain instant visibility into your clusters and resolve issues faster.
