Infrastructure
GPU Workloads Without Kubernetes
Get the container-based deployment workflow you want without the Kubernetes complexity you don't. Deploy GPU workloads in minutes, not days — no YAML, no node pools, no GPU device plugins.
TL;DR
- →No K8s expertise needed — push a Docker container, VectorLay handles the rest
- →Deploy in minutes, not days — skip cluster provisioning, GPU operator setup, and YAML debugging
- →Same performance — VFIO GPU passthrough means zero virtualization overhead
- →Much lower ops cost — no K8s engineers, no cluster maintenance, no surprise cloud bills
Why GPU Kubernetes Is Hard
Kubernetes is a powerful orchestration platform, but running GPU workloads on K8s introduces a layer of complexity that most teams underestimate. What starts as "just deploy a container" quickly turns into weeks of infrastructure engineering.
GPU Device Plugins
You need the NVIDIA device plugin DaemonSet running on every GPU node, properly configured to expose GPU resources to the Kubernetes scheduler. When it breaks (and it will), pods hang in Pending state with cryptic error messages.
Node Pools & Scheduling
GPU nodes need dedicated node pools with taints and tolerations, resource quotas, and affinity rules. One misconfigured label and your workload lands on a CPU node or gets stuck in the scheduler queue indefinitely.
NVIDIA Driver Updates
Updating NVIDIA drivers on a K8s node requires draining the node, updating the driver, rebooting, and verifying the device plugin re-registers. Do this across a fleet without downtime and you've earned a promotion.
Resource Quotas & Multi-Tenancy
GPUs can't be shared natively in K8s. You need MIG (Multi-Instance GPU) or time-slicing to serve multiple workloads per GPU, each with its own set of caveats and driver requirements.
Networking & Ingress
Exposing GPU workloads externally requires ingress controllers, TLS termination, load balancers, and DNS management. Each piece is another YAML file, another dependency, another thing that can break at 2 AM.
The result? Most teams spend 2-4 weeks just getting GPU workloads running on K8s before writing a single line of ML code. And that's before accounting for ongoing maintenance — cluster upgrades, security patches, node auto-scaling tuning, and the inevitable 3 AM incident when the GPU operator crashes.
VectorLay: The K8s-Free Alternative
VectorLay gives you the part of Kubernetes you actually want — container-based deployment with GPU access — and handles everything else. No clusters to manage, no YAML to write, no device plugins to debug.
Push a Docker Container
Use any Docker image — vLLM, TGI, ComfyUI, or your own custom image. If it runs in Docker with GPU access, it runs on VectorLay.
VectorLay Handles Scheduling
The platform automatically finds available GPU nodes, schedules your workload, and manages resource allocation. No node pools, no taints, no affinity rules.
GPU Passthrough, Networking, Health Checks
VFIO passthrough gives your container exclusive GPU access with zero overhead. HTTPS endpoints, TLS, and health monitoring are automatic. Failover happens in seconds when a node goes down.
The entire process takes about 5 minutes from sign-up to a running GPU workload. Compare that to the 2-4 weeks of Kubernetes setup — and the ongoing maintenance burden that never ends.
Complexity Comparison: Kubernetes vs. VectorLay
Here's a side-by-side comparison of what it takes to run GPU workloads on Kubernetes versus VectorLay:
| Aspect | Kubernetes | VectorLay |
|---|---|---|
| Setup time | 2-4 weeks | 5 minutes |
| GPU driver management | Manual per-node updates with draining | Handled by platform |
| Scaling | HPA + Cluster Autoscaler + node pool config | Set min/max replicas |
| Failover | Pod disruption budgets + manual setup | Automatic, under 30 seconds |
| Networking | Ingress controller + cert-manager + DNS | Automatic HTTPS endpoint |
| Monitoring | Prometheus + Grafana + DCGM exporter | Built-in dashboard |
| Ops engineer cost | $150k-200k/year (partial FTE) | $0 |
Cost Comparison: K8s on AWS vs. VectorLay
Running GPU workloads on Kubernetes isn't just operationally expensive — the infrastructure costs add up fast. Here's a realistic comparison for a team running 4 GPU nodes 24/7:
| Cost Item | K8s on AWS (4x A10G) | VectorLay (4x RTX 4090) |
|---|---|---|
| GPU compute | $3,484/mo (4x $1.21/hr) | $1,412/mo (4x $0.49/hr) |
| EKS control plane | $73/mo | $0 |
| Load balancer | $18/mo + data charges | $0 (included) |
| Egress | $50-200/mo (varies) | $0 (no egress fees) |
| Ops engineer time | $3,000-5,000/mo (20-30% FTE) | $0 |
| Total monthly | $6,625-8,775/mo | $1,412/mo |
Annual Savings: 4 GPUs Running 24/7
Feature Comparison
| Feature | Kubernetes | VectorLay |
|---|---|---|
| Deployment time | Hours to days (YAML, configs, debugging) | Under 5 minutes |
| Scaling | HPA + Cluster Autoscaler (complex) | Built-in auto-scaling |
| Failover | Requires PDB + readiness probes | Automatic, under 30 seconds |
| Isolation | Namespace-level (shared kernel) | VM-level (hardware isolation) |
| Billing model | Compute + control plane + LB + egress + storage | Single per-GPU hourly rate, no hidden fees |
When You Actually Need Kubernetes
We're not going to pretend VectorLay replaces Kubernetes in every scenario. K8s is a general-purpose orchestration platform, and there are legitimate reasons to use it. Here's when Kubernetes might be the right choice:
But if your primary goal is "run a GPU workload in a container and expose it as an API" — which is the case for the vast majority of ML teams — Kubernetes is overkill. You're paying a massive complexity tax for orchestration features you don't use.
How to Migrate from K8s to VectorLay
If you're already running GPU workloads on Kubernetes and want to simplify, migration is straightforward. You already have the hard part — a working Docker image.
Identify Your Container Image
Pull the Docker image reference from your K8s deployment spec. If you're using a private registry, make sure the image is accessible. That same image runs on VectorLay unchanged.
Map Environment Variables
Copy your env vars and config maps. VectorLay supports environment variables natively — no ConfigMaps or Secrets objects needed.
Deploy on VectorLay
Create a deployment with your image, env vars, and GPU selection. VectorLay provides an HTTPS endpoint automatically — update your DNS or API gateway to point to it.
Test and Cut Over
Run both environments in parallel, verify parity, then switch traffic. Once confirmed, decommission your K8s GPU node pool and stop paying for cluster overhead.
Most teams complete the migration in a single afternoon. The longest part is usually waiting for model weights to download on the first deployment.
Stop managing Kubernetes for GPU workloads
Deploy your GPU containers in minutes, not weeks. No YAML, no device plugins, no cluster maintenance. Just push a Docker image and get a working GPU endpoint with auto-failover and scaling built in.