Application Performance Automation Platform
Most automation tools don't put performance at the center. Many stop at providing visibility, or go no further than alerts and recommendations. Cloud cost dashboards show waste but don't fix it, monitoring platforms deliver warnings but don't take actual action, and resource optimization tools suggest changes but leave execution up to the user.
AI agents that solve real problems. They handle drift correction, container image updates, automated error recovery, and policy enforcement — no need to file a ticket, no need to wait.
Get real-time visibility into resource usage and application performance. Continuously maintain an accurate, up-to-date picture of how your apps are actually behaving.
Automatically adjust CPU and memory requests to match actual usage. Prevent over-provisioning without sacrificing performance. Continuously optimize every workload.
Scale nodes up and down based on real demand, optimize GPU allocation, and automate spot instance management. A single control plane spanning cloud and on-premises environments alike.
Applications require continuous tuning, but operations teams struggle to keep up with that pace. Most tools stop at identifying the problem, leaving the actual fix to be carried out manually, again and again, by people. The result is the same issues being handled by hand over and over — sometimes into the early hours of the morning.
Overcoming this limitation requires an approach that goes beyond mere visibility or recommendations, one that can automatically optimize actual operations.
Cast AI delivers full-stack optimization from a single platform, automating operations without manual intervention. It performs continuous, real-time optimization based on actual workload behavior — and rather than simply showing what needs to be fixed, it carries out the execution automatically as well. You no longer need to rely on tickets or alerts; operations can be improved automatically instead.
Cast AI performs continuous, real-time optimization based on actual workload behavior. Rather than simply showing what needs to be fixed, it automatically executes the necessary actions. You can automate operations without relying on repetitive tickets or alerts.
While traditional automation relies on static rules, Cast AI uses predictive models trained on real operational data. Drawing on data accumulated across thousands of clusters and diverse workloads, it understands application demand and automatically determines optimal resource operation.
It can be applied directly to Kubernetes clusters with no separate infrastructure changes, and can even start in read-only mode. It identifies optimization opportunities based on real workloads, automatically scales in response to real-time signals, and maintains a stable state.
Cast AI connects flexibly with the tools and environments you already use — including Grafana, Terraform, Prometheus, OpenTelemetry, Helm, and Pulumi — allowing you to extend automation while keeping your existing operational setup intact.
Autonomously optimize your Kubernetes workloads.
Run cost-efficient workloads at peak performance with Cast AI's intelligent workload optimization.
Instantly right-size workloads and operate without downtime, with in-place pod resizing and Live Migration(™), which preserves uptime for stateful workloads. Real-time cost visibility and autonomous stability enhancements are also included.
Continuously adjust CPU and memory requests based on actual usage patterns. Apply optimal resource settings by analyzing historical and real-time metrics. Prevent over-provisioning and under-utilization without manual intervention.
Combine vertical and horizontal scaling for smart, adaptive resource management. Enhance Kubernetes-native autoscaling with smarter, data-driven decision-making. Support both short-term demand spikes and long-term workload trends.
For stateful applications and long-running jobs, move running workloads between nodes without interruption while performing maintenance and optimizing costs. Enable migration of workloads previously considered unmovable, based on persistent storage. Eliminate node fragmentation to enable advanced bin-packing and keep critical applications running.
Dynamically modify resource allocation for running pods without restarts, preventing downtime. Adjust pod limits and requests according to real-time demand. Respond instantly to changing workload requirements without service interruption, improving SLA compliance and user experience.
Optimize a wide range of Kubernetes workloads with flexible configuration options. Support deployments, stateful sets, jobs, cron jobs, and custom workloads through label-based selection. Apply consistent optimization across diverse workloads without changing your deployment patterns.
Track Kubernetes costs with ease.
Manage your Kubernetes costs for free, and take advantage of detailed real-time tracking by namespace, workload, and resource allocation group.
Visualize network and cost usage, detect and respond to cost anomalies in real time, and integrate easily with your existing monitoring environment through API integration.
Transform Kubernetes management with real-time visibility into resource utilization across your entire cluster. Compare provisioned usage, requested usage, and actual usage patterns for CPU, GPU, and memory. Proactively identify unscheduled pods and other inefficiencies to reduce cloud waste.
Analyze Kubernetes costs through detailed breakdowns by workload, namespace, and allocation group. Identify key cost drivers and trends through comprehensive cost comparisons. Build strategic resource plans using detailed historical spending analysis.
Find out how much you can save with automation: Simulate the potential cost savings you can achieve with Cast AI's automation features (e.g., bin packing, spot instance management, workload autoscaling). See exactly how an optimized cluster configuration can deliver maximum performance at the lowest cost.
Understand GPU utilization levels and reduce waste: Examine GPU utilization by individual workload to identify wasted resources. Compare workloads to pinpoint which ones are driving the highest costs in your system configuration.
Scale capacity anywhere. Operate GPUs as one system.
OMNI Compute for AI enables AI teams to operate scarce GPU and compute capacity across clouds and regions within the same Kubernetes cluster, allowing them to scale anywhere without restructuring applications or adding operational overhead.
Access scarce GPU capacity across clouds and regions through a single control plane, while maintaining full control over where workloads run. Discover and utilize GPU capacity across multiple vendors and regions with cost, performance, and compliance controls. Deploy workloads wherever capacity is available, with no code changes required.
Maximize throughput on every GPU without sacrificing performance or isolation. Share and partition GPUs to increase utilization across diverse workloads. Place workloads using bin packing and dynamic resource allocation.
AI demand is highly variable. OMNI Compute continuously adjusts capacity. Scale GPU capacity up or down based on actual workload demand. Intelligently leverage spot and on-demand GPUs with automatic fallback.
Maintain visibility and control as your AI environment grows. Track GPU utilization, memory usage, and performance in real time. Attribute GPU usage to workloads, teams, and applications.
Run self-hosted LLMs at low cost.
Kimchi enables enterprises to run frontier AI coding and inference within their own infrastructure, built on the model routing, governance, and cost control capabilities enterprises require.
It manages the AI coding tools and LLM requests developers use within the enterprise's operational standards, efficiently connecting self-hosted LLMs with the models needed, enabling an AI development environment that accounts for security, cost, and productivity together.
Your cloud. Your data. Your rules.
Open source models run inside the customer's own cloud accounts, such as AWS, GCP, and Azure. Prompts, responses, and code never leave the customer's cloud.
Prevent unauthorized merges. Prevent secret leaks. Prevent unexpected cost spikes.
Every coding agent in the organization is routed through Kimchi Proxy. Budgets, PII filtering, usage metrics, and an approved skill registry are applied before requests reach the model, and all of this can be reviewed in the Kimchi web app.
See exactly who is incurring AI costs, on what, and where.
View usage in real time by developer, team, model, and tag. Forecast costs before scaling, and reduce the situations where you only find out about costs after seeing the bill on the 1st of each month.
You don't need to pay architect-level costs for every task.
Use powerful models for reasoning, and route execution tasks to cheaper self-hosted open source models. Hybrid mode captures the benefits of both approaches, with routing decisions made automatically.
Switch from your existing AI tools with a single command.
kimchi setup automatically detects Claude Code, Cursor, Continue, VS Code, and Windsurf, and automatically migrates endpoints. It supports OpenAI-compatible APIs, so no code changes are needed. You can start with Kimchi Serverless and switch to self-hosting at any time based on compliance or cost requirements. It automatically detects already-installed coding tools and supports migration by simply changing the base URL while keeping the same SDK and workflow. MCP servers, skills, and settings can also be migrated with a single prompt, and switching to a self-hosted environment requires only a single configuration flag.
Optimize your database and deploy in minutes.
No coding or configuration required—experience faster queries and improved performance.
Automatically optimize query caching and TTLs, support multiple operating modes, and get reliability and visibility together through automatic database discovery and built-in failover.
Uses advanced machine learning techniques to automatically detect query traffic patterns and identify caching opportunities. Continuously adapts to query patterns as they change in real time. Autonomously optimizes cache configuration at scale, achieving efficiency beyond manual tuning. Requires no separate setup or application-side coding.
View all query and cache-related information through an intuitive dashboard, and examine historical and real-time performance data. Identify high-impact queries by analyzing cache hit rates, patterns, and total query volume. Monitor query frequency, variability, and execution time across your apps. Track trends and identify usage patterns and bottlenecks through detailed metrics.
Use automation to achieve scalability through automatic invalidation. Instantly remove stale data with real-time automatic invalidation, requiring no code or configuration. Multi-layer invalidation with row-level analysis, table-level fallback, and automatic monitoring. Scales without burdening the database by inspecting wire traffic instead of using WAL.
Enabling AI agents to run Karpenter more intelligently and autonomously.
Karpenter helps you get started, and Cast AI completes the next step. It adds enterprise-grade optimization capabilities to your existing Karpenter environment, improving performance, cost, and stability all at once.
It identifies optimization opportunities through real-time cost visibility and workload-level analysis, and improves operational efficiency through usage-based resource tuning, zero-downtime container migration, and node consolidation that accounts for workload characteristics. It also predicts spot interruptions in advance and automatically recovers from them, enabling consistently stable operations.
Start optimizing in minutes and scale at your own pace. Activate features whenever you're ready. Connect your cluster in just two clicks and instantly see potential cost savings. Enable workload optimization, spot adoption, and other features as needed.
Add optimization capabilities that Karpenter can't handle on its own. Reduce waste, increase efficiency, and cut costs automatically. Rebalance and migrate workloads with no downtime or dropped connections. Automatically hibernate dev and staging clusters to reduce idle-time waste.
For stateful applications and long-running jobs, move running workloads between nodes without interruption. Enable migration of workloads previously considered unmovable, based on persistent storage. Eliminate node fragmentation to enable advanced bin-packing and keep critical applications running.
Observe actual resource consumption and automatically adjust requests to eliminate hidden waste caused by over-requested resources. Reduce over-provisioning without compromising application stability. Give Karpenter accurate requirements to enable tighter bin-packing.
Keep workloads on spot instances instead of defaulting to on-demand, and consolidate underutilized nodes without excessive disruption. Anticipate interruptions and return workloads to spot once capacity stabilizes. Consolidate by combining workload-aware logic with live migration.
Track spending in real time and demonstrate value through detailed cost monitoring across your Kubernetes infrastructure. Monitor compute costs in real time by cluster, namespace, and workload. Quantify the savings from each optimization decision by comparing current spend against a baseline.
Maximize cost savings while reducing operational burden.
Get unified monitoring of resource usage across your organization and at the cluster level, with a stable operational foundation built on automated resource allocation and zero-downtime scaling.
Keep clusters running at their optimal state through spot instance automation, workload consolidation, dynamic pod configuration adjustments, and cluster rebalancing, while managing performance and cost in balance through memory issue handling and vertical/horizontal autoscaling.
Reduce waste and maximize resource utilization through bin configuration and space allocation planning. Automatically provision the most cost-efficient compute resources. Scale resources up or down based on real-time requirements.
For stateful applications and long-running jobs, move running workloads between nodes without interruption while performing maintenance and optimizing costs. Enable migration of workloads previously considered unmovable, based on persistent storage. Eliminate node fragmentation to enable advanced bin-packing and keep critical applications running.
For stateful applications and long-running jobs, move running workloads between nodes without interruption. Enable migration of workloads previously considered unmovable, based on persistent storage. Eliminate node fragmentation to enable advanced bin-packing and keep critical applications running.
Observe actual resource consumption and automatically adjust requests to eliminate hidden waste caused by over-requested resources. Reduce over-provisioning without compromising application stability. Give Karpenter accurate requirements to enable tighter bin-packing.
Maximize the value of your investment by increasing resource utilization: Use commitments across all clusters, or prioritize commitments for specific clusters. Automatically balance commitment and spot instance usage to achieve maximum cost savings.
Solve the challenges of spot instance management and interruptions that can occur every 2 minutes. Maintain a balance between cost and stability with advanced spot instance management capabilities. Automatically handle spot instance lifecycle events, including interruptions, spot diversity, and fallback to on-demand nodes when needed.
Cast AI is trusted by more than 2,100 companies worldwide. Take a look at some key customer stories.
Reduced cloud costs by 40–70% and improved engineering productivity.
Cut Kubernetes operational overhead and costs through automation.
Automatically optimized K8s workloads with no risk of performance degradation.
