Scale your ML platform, not your operations
AI workloads shouldn’t require a massive DevOps team. Cast AI provides the autonomous brain for your ML infrastructure, handling everything from GPU time-slicing and MIG partitioning to predictive spot orchestration. We ensure your models have the exact compute they need to scale, without the manual toil of managing clusters.
Trusted by AI startups running Kubernetes at scale
Value
Built for fast-moving ML teams
Automation over operational toil
As ML platforms grow, manual GPU provisioning, cluster configuration, and capacity planning slow teams down. Cast AI replaces repetitive infrastructure work with continuous automation, keeping environments responsive without constant human intervention.
Reliability under variable demand
Training runs, inference traffic, and batch jobs rarely follow predictable patterns. Cast AI adapts infrastructure in real time, helping you maintain consistent model performance as conditions change.
Efficiency without tradeoffs
Over-provisioning GPUs is often the safest way to avoid bottlenecks, but it creates long-term waste. Cast AI continuously optimizes resource usage, improving efficiency naturally without sacrificing performance or slowing experimentation.
Autoscale GPU
infrastructure on demand
Provision and scale GPU resources dynamically without manual configuration or overprovisioning.
- Provision GPU instances automatically as workloads require them
- Scale down idle resources to eliminate unnecessary spend
- Leverage Spot Instances for GPU workloads to reduce costs further
Maximize GPU utilization through sharing
Run more workloads on fewer GPUs using time-slicing and Multi-Instance GPU (MIG) partitioning.
- Enable GPU time-slicing to let multiple workloads share a single GPU
- Partition GPUs with MIG for isolated, parallel execution on a single instance
- Combine both methods to balance cost efficiency with performance isolation
Run inference on optimized infrastructure
Deploy models on Kubernetes clusters tuned for performance and efficiency.
- Automatically select the right instance types for your inference workloads
- Reduce operational overhead with intelligent bin-packing and scheduling
- Integrate seamlessly with platforms like Hugging Face for streamlined deployments
Select the optimal LLM for every request
Automatically route queries to the most cost-effective model without sacrificing quality.
- Compare LLM costs across providers with real-time monitoring
- Route requests dynamically to the best-performing model at the lowest cost
- Eliminate guesswork when choosing between open-source and commercial models
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Additional resources
Market research
Fairgen saves 70% on the cloud while boosting stability for gen AI workloads
Product
Optimize and Scale Cloud Native workloads
Run cost-effective workloads on peak performance with Cast Al’s intelligent workload optimization.
Product
Scale AI Workloads anywhere
OMNI Compute for AI enables scarce GPU and compute capacity across clouds and regions to be operated within the same Kubernetes cluster.
4.8/5 50+ reviews
Run Kubernetes without the operational burden
Cast AI helps AI/ML startups operate cloud-native platforms that scale reliably, stay performant, and adapt automatically as demand changes.