Ship AI products,
not infrastructure.
GPU provisioning, model serving, and training pipelines are complex. Kapten provides AI-optimized blueprints so your team can focus on the models, not the machines.
AI infrastructure is its own beast.
GPU provisioning is complex
Finding GPU availability, configuring NVIDIA drivers, setting up CUDA, and managing node pools with GPU taints requires deep infrastructure knowledge.
Model serving needs custom infra
Serving models at low latency requires load balancing, auto-scaling, health checks, and GPU memory management -- all different from standard web apps.
GPU costs spiral fast
GPUs are expensive. Without proper scheduling and auto-scaling, idle GPUs burn through your budget while queued jobs wait.
AI-ready infrastructure in minutes.
Kapten provides pre-built blueprints for GPU node pools, model serving, and training pipelines. Your ML team deploys models, not YAML.
Provision GPU node pools
Select GPU types (A100, T4, L4), configure node pools, and Kapten handles drivers, CUDA, and scheduling automatically.
Deploy model serving blueprints
Pre-configured templates for serving models with TGI, vLLM, or custom inference servers. Auto-scaling based on request load.
Run training jobs
Submit training jobs with GPU scheduling, checkpointing, and automatic node scale-down when training completes.
Optimize costs automatically
GPU nodes scale to zero when idle. Spot instances for training jobs. Pay only for the compute you actually use.
Built for AI workloads.
GPU support
NVIDIA GPU node pools with drivers, CUDA, and device plugins pre-configured. A100, T4, L4, and more.
Training job templates
Submit training jobs with built-in checkpointing, distributed training support, and automatic resource cleanup.
Model serving blueprints
Deploy models with TGI, vLLM, Triton, or custom inference servers. Auto-scaling, health checks, and load balancing included.
Vector DB ready
One-click deployment of vector databases like Qdrant, Weaviate, or Milvus for RAG pipelines and semantic search.
Cost-optimized GPU scheduling
Scale GPU nodes to zero when idle, use spot instances for training, and share GPUs across workloads with time-slicing.
Monitoring for ML
GPU utilization, memory usage, inference latency, and throughput dashboards. Know when your models need attention.
Focus on models. Not machines.
AI-optimized infrastructure that scales with your models.