Operating connected vehicle platforms at scale

Connected vehicles, OTA updates, and AI-driven systems depend on always-on platforms and on scarce compute resources. Cast AI provides an automation-first operating model for automotive cloud-native infrastructure, ensuring vehicle services remain reliable, performant, and available as demand, data, and compute constraints evolve.

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Trusted by automotive leaders running large-scale
cloud-native platforms

Value

Built for automotive scale and operational reliability

Automation and control

Automotive environments change constantly. Cast AI replaces manual infrastructure decisions with continuous automation, keeping Kubernetes environments governed as workloads change.

Production-grade reliability

From connected vehicle services to manufacturing systems, downtime is not an option. Cast AI optimizes infrastructure without disrupting running applications, preserving availability as demand shifts.

Performance at scale

Real-time telemetry, AI workloads, and backend services require consistent performance. With Cast, you can automatically adapt resources to meet application needs under variable load, balancing performance and cost at scale.

Adapt to connected vehicle demand

Support services that fluctuate by region, time, and vehicle program without manual intervention.

Scale infrastructure dynamically based on real usage patterns
Maintain consistent performance during OTA rollouts, launches, and regional spikes
Eliminate manual capacity planning as vehicle adoption grows

Optimize safely, without downtime

Evolve infrastructure while critical automotive services stay online.

Move running, stateful, and long-lived workloads without interrupting vehicle or backend services
Enable consolidation, maintenance, and optimization while applications remain online
Unlock higher infrastructure efficiency without risking service availability

Run AI workloads without infrastructure friction

Deliver real-time intelligence for perception, analytics, and automation, even when local resources are constrained.

Schedule and utilize GPU resources to meet latency and throughput requirements
Extend your GPU capacity across regions and clouds through OMNI Compute when local supply is limited
Keep AI services available as demand increases without redesigning the platform

Maintain operational visibility

Understand how infrastructure supports vehicle programs, regions, and applications.

Gain clear insight into utilization and performance across environments
Align infrastructure behavior with regions, workloads, and programs
Maintain predictable operations as platforms scale

Webinar

Mercedes-Benz.io operates Kubernetes at scale without operational friction

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“You can easily give your product teams the possibility of using Spot Instances by them, being in full control of the workload – for example, setting tolerations and node selector configurations for Spot. That was a quick win for us, especially when it comes to costs along the way.”