This topic compares enterprise GenAI deployment platforms and the Model Context Protocol (MCP) integrations that let LLMs operate safely on cloud data and resources. In practice, organisations are pairing cloud data platforms (Snowflake, BigQuery) with MCP servers and cloud platform integrations (AWS, Azure, Google Cloud Run, Cloudflare) to enable LLM-driven SQL, search, resource operations and application deployments without direct human-level credentials. Snowflake’s open-source Cortex MCP servers expose Cortex Search, Cortex Analyst and SQL execution so models can query semantic views and run controlled data operations. BigQuery and Cloud Run MCP servers translate natural-language requests into secure queries and deployments. Cloud vendors have their own MCP implementations: AWS’s MCP server supports S3 and DynamoDB operations, Azure offers a unified MCP entry point for Azure services, and Cloudflare provides MCP endpoints for Workers, KV, R2 and D1. Enterprise platforms such as Red Hat (for hybrid Kubernetes-based deployments) and Unicorne/AWS (managed integrations on AWS) represent alternative approaches for on‑prem or cloud-native orchestration and policy enforcement. As of 2026, key considerations are interoperability, governance, auditability and operational observability. MCP standardisation reduces custom connectors and speeds integration, but organisations still weigh trade-offs between vendor-managed convenience and control over data gravity, compliance, and latency. Successful deployments combine MCP-enabled tooling with enterprise policy (access controls, logging, model oversight) and platform choices that match workload locality and security requirements. This comparison helps teams evaluate vendor capabilities, MCP compatibility, and integration patterns across cloud data platforms and cloud platform integrations.