Platforms

Platform guides for building AI solutions. Each covers the service landscape, architecture patterns, cost models, and implementation detail.

PlatformCoverageStatus
MicrosoftFoundry, OpenAI, AI Search, Agents, Copilot Studio, Fabric, Security, GenAI GatewayActive
DatabricksMosaic AI, MLflow, Vector Search, Unity Catalog, GenieActive
SnowflakeCortex Functions, Cortex Analyst, Cortex SearchActive
AWSBedrock, SageMaker, Textract, KendraActive
GCPVertex AI, Gemini, ADK, Document AIActive


Model and Service Catalogue

The platform pages above cover the major clouds in depth. In practice, delivery teams draw on a wider set of tools across six categories - a model layer, the platforms that serve those models, and the surrounding frameworks for orchestration, retrieval, modality handling, and safety.

CategoryTools
ModelsOpenAI, Anthropic, Meta, Cohere, Qwen
Model PlatformsAzure Foundry, Amazon Bedrock, Vertex AI, Snowflake Cortex AI, Databricks
Agentic Framework & OrchestrationMicrosoft Agent Framework, LangChain, LangGraph, Amazon Bedrock Agents, Google ADK, Agent 365, n8n
RAG & Vector StoreAzure AI Search, Pinecone, Chroma, ElasticSearch
Modality-Specific (Speech & OCR)Whisper, Amazon Textract, Tesseract OCR, Paddle OCR
Observability, Evaluation & SafetyLangSmith, Datadog, Azure AI Content Safety, Guardrails AI, AWS Guardrails
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Note

Illustrative selection of tools deployed on client engagements, not an exhaustive list or an approved-vendor register. Accurate as at August 2026.

Two things worth reading off this table. The model layer and the model platform layer are separate choices - Anthropic and Meta models are reachable through Bedrock, Vertex AI, and Foundry alike, so picking a platform does not lock in a model vendor. And the last two rows are the ones most often underestimated in scoping: modality handling and the observability/safety stack are frequently treated as afterthoughts, then turn into the bulk of the hardening work before a pilot can go to production.