Platform Comparison Table
If you know one cloud's AI stack well, you already know most of the others - the concepts repeat, only the names change. This page is the cheat sheet: the same capability, named across Vertex AI (GCP), Bedrock (AWS), and Azure AI Foundry.
Useful in two situations: converting existing GCP-stack experience into credible AWS/Azure vocabulary for an interview, and reasoning about a multi-cloud or migration architecture where the underlying capability is the same but the API/product name isn't.
Model Access, RAG, and Guardrails
| Capability | GCP (Vertex AI) | AWS (Bedrock) | Azure (AI Foundry) |
|---|---|---|---|
| Unified model invocation | Vertex AI model endpoints | Converse / ConverseStream API | Azure AI Foundry model deployments |
| Managed RAG | RAG Engine | Knowledge Bases | Azure AI Search integration |
| Safety/content filtering | Vertex AI Guardrails / Safety filters | Guardrails for Bedrock | Azure AI Content Safety |
| Managed agent runtime | Agent Builder / Vertex AI Agents | AgentCore | Azure AI Foundry Agent Service |
| Model evaluation | Vertex AI Evaluation | Bedrock Model Evaluation | Azure AI Foundry Evaluations |
Data Platform
| Capability | GCP | AWS | Azure |
|---|---|---|---|
| Data warehouse | BigQuery | Redshift | Fabric Warehouse / Synapse |
| Unified lakehouse layer | BigLake | Lake Formation | OneLake (Fabric) |
| Managed relational DB | Cloud SQL | RDS | Azure SQL Database |
| Globally distributed NoSQL | Spanner / Firestore | DynamoDB | Cosmos DB |
| Managed Spark platform | Dataproc | EMR | Azure Databricks / Synapse Spark |
MLOps
| Capability | GCP | AWS | Azure |
|---|---|---|---|
| Model registry | Vertex AI Model Registry | SageMaker Model Registry (or MLflow on Databricks) | Azure ML Model Registry |
| Experiment tracking | Vertex AI Experiments | SageMaker Experiments (or MLflow) | Azure ML / MLflow-native in Fabric |
| Pipeline orchestration | Vertex AI Pipelines | SageMaker Pipelines | Azure ML Pipelines |
How to Use This Table in an Interview
Don't just recite the table - use it to show you understand why the concepts map, not just that they do. "Bedrock's Knowledge Bases and Vertex AI's RAG Engine solve the same retrieval problem - managed chunking, embedding, and indexing" is a stronger answer than naming both products with no connective explanation.
The strongest framing in a platform-breadth interview question is architectural, not lexical: name the capability first (e.g. "managed RAG"), then the products across clouds, then one concrete difference that actually matters (e.g. Bedrock Knowledge Bases defaults to a managed OpenSearch Serverless vector store while Vertex AI RAG Engine defaults to a different backing index - both are swappable, but the defaults differ). This demonstrates transferable understanding rather than memorized vocabulary.
Study Notes
Must-know for interviews:
- Managed RAG: RAG Engine (GCP) / Knowledge Bases (AWS) / Azure AI Search integration (Azure) - same problem, different defaults
- Data warehouse: BigQuery / Redshift / Fabric Warehouse - Fabric's OneLake is architecturally distinct (single logical storage layer) from BigQuery/Redshift's more siloed models
- Globally distributed NoSQL: Spanner/Firestore / DynamoDB / Cosmos DB - all viable RAG vector stores via native vector search extensions
- The interview-winning move is naming the capability and one real architectural difference, not just reciting product names
Quick recall Q&A:
- What's the AWS equivalent of Vertex AI's RAG Engine? Bedrock Knowledge Bases - managed chunking, embedding, and retrieval.
- What's the Azure equivalent of BigQuery? Microsoft Fabric's Warehouse, built on the OneLake unified storage layer - architecturally different from BigQuery in that all Fabric workload types share one logical storage layer.
- Which AWS and Azure services are the closest match to Google Spanner? DynamoDB (AWS) and Cosmos DB (Azure) - both globally distributed, though with different consistency/data-model tradeoffs than Spanner's strong consistency.