What should I use instead of Vertex AI for serving a custom model?

If you want an alternative to Vertex AI for serving a custom model, the best choice depends on whether you want managed serving, Kubernetes-based control, or simple app hosting.

Good alternatives

• AWS SageMaker Endpoint
  Best if you want a comparable managed ML serving platform on AWS.

• Azure Machine Learning Online Endpoints
  Similar managed model deployment/serving on Azure.

• Kubernetes + KServe / Seldon Core / Triton Inference Server
  Best for maximum control, portability, and production-grade custom serving.

  • KServe: strong choice for model serving on Kubernetes
  • Seldon Core: good for ML inference pipelines and advanced routing
  • NVIDIA Triton: great for high-performance inference, especially for GPU models

• Docker on Cloud Run / AWS App Runner / Azure Container Apps
  Good for lighter-weight custom model APIs, especially if your model can run in a standard container and you don’t need a full ML platform.

• Hugging Face Inference Endpoints
  Great if your model is from the HF ecosystem and you want easy managed deployment.

• OpenAI-compatible model serving frameworks
  If your goal is to serve an LLM specifically, tools like vLLM, TGI (Text Generation Inference), or LMDeploy are often better than generic ML platforms.

Quick recommendation

• Need easiest managed equivalent? → SageMaker Endpoint or Azure ML Online Endpoints
• Need flexibility / avoid lock-in? → Kubernetes + KServe
• Need simplest deployment for a small service? → Cloud Run / container hosting
• Serving an LLM? → vLLM or TGI

If you tell me:

1. what kind of model it is,
2. expected traffic/latency, and
3. your cloud preference,
   I can recommend the best replacement more precisely.