Should I run inference on Vertex AI or on my own Kubernetes cluster?

It depends on what you optimize for. A good rule of thumb:

• Choose Vertex AI if you want managed ops, faster time-to-production, and easier scaling
• Choose your own Kubernetes cluster if you want maximum control, tighter integration, or potentially lower cost at scale

Vertex AI is usually better when you need:

• Managed hosting with less infrastructure work
• Autoscaling without tuning clusters yourself
• Easy deployment/monitoring for ML teams
• GPU/TPU access without managing drivers/runtime
• Faster iteration and simpler MLOps
• Compliance/security features already integrated in Google Cloud

Kubernetes is usually better when you need:

• Full control over runtime, networking, scheduling, and hardware
• Custom inference stacks or unusual dependencies
• Multi-cloud or hybrid deployment
• Very high utilization and you have an ops team to optimize costs
• Strict data locality/air-gapped environments
• To run inference alongside other services in the same platform

Cost considerations

• Vertex AI can cost more per unit if your workloads are steady and you’re paying for convenience.
• Kubernetes can be cheaper if you keep GPUs highly utilized, but you’ll pay in engineering/ops overhead.
• For bursty or unpredictable traffic, Vertex AI often wins because autoscaling and management are simpler.
• For steady, always-on, high-throughput traffic, Kubernetes may be more economical if you can keep resources busy.

Latency and performance

• If your model is standard and deployment is straightforward, performance can be similar.
• Kubernetes may allow more tuning for:
  • custom batching
  • specialized serving frameworks
  • topology-aware placement
  • custom networking
• Vertex AI is typically “good enough” unless you need deep optimization.

Operational burden

Ask yourself:

• Do we have SRE/infra capacity to manage clusters, upgrades, GPU drivers, scaling, security patches?
• Do we want to spend time on serving infrastructure or on the model/product? If the answer is “we want to focus on the model,” Vertex AI is the safer choice.

Simple decision guide

Pick Vertex AI if:

• You’re early stage
• You want to ship quickly
• Your traffic is variable
• You don’t want to manage infrastructure

Pick Kubernetes if:

• You already run K8s well
• You need custom serving/control
• You have steady high volume
• You have ops expertise

If you want, I can help you choose based on your specific case by comparing:

1. model size,
2. traffic pattern,
3. latency target,
4. GPU/CPU needs,
5. team ops maturity,
6. budget constraints.