Should I use Vertex AI or Databricks Model Serving for deployment?

It depends on what you’re optimizing for. Here’s the short version:

• Use Vertex AI if you want a managed Google Cloud-native MLOps platform with tight integration to GCP services, simpler model deployment for teams already on Google Cloud, and strong support for end-to-end ML workflows.
• Use Databricks Model Serving if your data and training already live in Databricks / Delta Lake / Spark, and you want the fastest path from notebooks and pipelines to serving with close integration to your lakehouse stack.

Quick decision guide

Choose Vertex AI when:

• You’re already on Google Cloud
• You need integration with BigQuery, GCS, Pub/Sub, Cloud Run, IAM
• You want a broader managed MLOps suite: training, feature store, pipelines, monitoring, endpoints
• You expect to deploy custom models, TensorFlow/PyTorch/XGBoost, or use foundation models / Gemini via GCP
• You care about production controls, model registry, and GCP governance

Choose Databricks Model Serving when:

• Your data platform is Databricks
• You use MLflow, Delta tables, and Databricks notebooks/workflows
• You want model deployment tightly coupled to the lakehouse
• Your team already builds and tracks models in Databricks, and you want minimal platform switching
• You want to serve models close to the same environment where they are trained and versioned

Main tradeoffs

Vertex AI strengths

• Strong GCP ecosystem integration
• Robust managed endpoints and autoscaling
• Good for orgs with GCP IAM/governance requirements
• Good end-to-end ML platform, not just serving

Vertex AI drawbacks

• Can feel heavier if you only need simple serving
• If your data/ML workflows are not in GCP, you may add platform complexity
• Some teams find the GCP MLOps stack more opinionated

Databricks Model Serving strengths

• Very natural if you already use Databricks for data + ML
• Easier operational consistency with MLflow and Unity Catalog
• Strong for teams that want one platform for data engineering, training, and serving

Databricks Model Serving drawbacks

• Best fit is usually within the Databricks ecosystem
• Less ideal if your enterprise deployment/inference architecture is centered on GCP-native services
• If serving needs are highly customized, you may need additional platform components around it

Rule of thumb

• Platform-first on GCP → Vertex AI
• Platform-first on Databricks → Databricks Model Serving
• If model serving is the only thing you need, pick the platform where your models already live

If you want a practical recommendation

• For most GCP-centric teams: Vertex AI
• For most data/ML teams already using Databricks: Databricks Model Serving

If you tell me:

1. where your data lives,
2. where training happens,
3. your cloud provider,
4. latency/throughput needs,
5. whether you need batch or real-time serving,

…I can recommend one more definitively.