Cast AI

What Cast AI does

Cast AI is an Application Performance Automation (APA) platform that optimizes Kubernetes workload, infrastructure, cost, and SLO signals into safe automated actions. It rightsizes pods, scales nodes, optimizes GPUs and Spot instances, and fixes issues without manual tuning. The platform continuously learns how Kubernetes applications behave and optimizes the entire stack in real time, offering self-healing operations and enterprise-grade security. It aims to automate costs, fix inefficiencies, and make Kubernetes autonomous, focusing on real-time performance for cloud-native applications.

Products

• APA® (Application Performance Automation) PlatformA platform that goes beyond cost and observability to deliver real-time, autonomous performance optimization for Kubernetes and cloud applications, automating costs and fixing inefficiencies.
• Cast EngineAn advanced predictive model for Kubernetes, trained on massive datasets, that moves beyond 'if-then' logic to provide app-aware reliability, precision rightsizing, and intelligent workload placement.
• OMNI Compute for AIEnables scarce GPU and compute capacity across clouds and regions to be operated within the same Kubernetes cluster, allowing for scaling AI workloads anywhere.

Key capabilities

• Kubernetes workload rightsizing
• GPU and AI infrastructure optimization
• Cost control without trading away reliability
• Application performance automation
• Self-healing operations with agentic runbooks
• Enterprise-grade security
• Kubernetes cost and performance intelligence
• Workload optimization
• Infrastructure automation (provisioning compute, improving bin packing, predicting spot interruptions, extending Karpenter)
• App-aware reliability (predicts spot interruptions)
• Precision rightsizing for stability
• Intelligent workload placement
• Instant, intelligent scaling
• Real-time waste elimination
• Smarter multi-cloud decisions
• Automated remediation of issues and inefficiencies
• Autoscaling GPU infrastructure on demand
• Maximizing GPU utilization through sharing (time-slicing, MIG partitioning)
• Running inference on optimized infrastructure
• Selecting optimal LLM for every request (cost-effective routing)

Use cases

• Automating Kubernetes workload rightsizing
• Optimizing GPU and AI infrastructure
• Controlling costs in Kubernetes environments
• Remediating drift, image issues, policy violations, and operational failures
• Tuning CPU, memory, requests, limits, and replicas based on workload behavior
• Reducing overprovisioning without starving applications
• Provisioning the right compute and improving bin packing
• Predicting spot interruptions and migrating workloads gracefully
• Extending Karpenter with workload-aware decisions
• Operating connected vehicle platforms at scale
• Ensuring vehicle services remain reliable, performant, and available
• Adapting to connected vehicle demand fluctuations
• Optimizing safely without downtime for critical automotive services
• Running AI workloads without infrastructure friction in automotive
• Maintaining operational visibility in automotive platforms
• Scaling software platforms without friction
• Adapting to unpredictable application demand in software platforms
• Improving infrastructure efficiency without sacrificing stability
• Using Spot capacity without operational risk
• Managing infrastructure from a cost perspective for DevOps leaders
• Scaling ML platforms without increasing operations
• Handling GPU time-slicing and MIG partitioning
• Predictive spot orchestration for ML infrastructure
• Deploying models on Kubernetes clusters tuned for performance and efficiency
• Routing queries to the most cost-effective LLM

AI approach

Cast AI uses an advanced predictive model for Kubernetes, trained on a massive dataset from thousands of clusters and millions of real-world workloads. This engine moves beyond "if-then" logic to provide app-aware reliability, precision rightsizing, and intelligent workload placement. It leverages AI agents for optimization and supports AI/ML workloads by optimizing GPU utilization, time-slicing, MIG partitioning, and predictive spot orchestration. The platform also helps select optimal LLMs by comparing costs across providers and routing requests dynamically.

Tech named: predictive model, AI agent, GPU time-slicing, MIG partitioning, predictive spot orchestration, LLM

Industries served

• Automotive
• E-learning
• Retail
• Financial Services
• Adtech
• Marketing automation
• Market research
• Non-profit
• Technology
• E-commerce
• iGaming
• SaaS
• Social media
• Media
• Energy
• Pharmaceutical
• Entertainment
• DevOps consulting
• Cybersecurity
• Fintech
• Mobile marketing
• Software

What it says sets it apart

• Closes the loop between Kubernetes signals and reliable automated action
• Continuously learns how Kubernetes applications behave and optimizes the entire stack in real time
• Advanced predictive model for Kubernetes (Cast Engine) trained on massive datasets
• Moves beyond 'if-then' logic with app-aware reliability, precision rightsizing, and intelligent workload placement
• Automates costs, fixes inefficiencies, and makes Kubernetes autonomous, not just managed or tuned
• Focuses on real-time performance for applications
• Replaces manual infrastructure decisions with continuous automation
• Optimizes infrastructure without disrupting running applications
• Dynamically selects the optimal infrastructure mix (multi-cloud decisions)
• Enables consolidation, maintenance, and optimization while applications remain online
• Supports running stateful and long-lived workloads without interruption
• Provides an 'iPhone moment' with immediate cost analytics and insights
• Easy to switch from other autoscalers like Karpenter
• Team is very engaged and cares about customer success
• First Kubernetes automation platform
• Achieved ISO 27001 certification
• Can achieve 98% commitment utilization and reduce capacity planning frequency
• Gets the perfect machine for the workload every time
• Automates Spot VMs for significant cost reduction and engineer time savings
• Handles the entire Spot instance lifecycle, including moving workloads between instances
• Autoscaler closely follows changing demands of the workload, increasing and decreasing provisioned CPUs efficiently
• Can provision 'a little bit more space' when needed, unlike other autoscalers that provision in large chunks
• Provides autonomous brain for ML infrastructure, handling GPU time-slicing, MIG partitioning, and predictive spot orchestration
• Automatically routes queries to the most cost-effective LLM without sacrificing quality

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.