⇆ Platforms & Cloud

Cloud AI migrations

Moving AI workloads between Azure OpenAI, AWS Bedrock, Google Vertex AI, direct APIs and self-hosted models.

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Pick where your AI workloads run today and where you want to go. We’ll show why teams make that move, what to watch out for and how we de-risk it.

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Azure OpenAI / AI Foundry

Why teams move here
  • Microsoft enterprise agreement or Azure commitment to consume
  • OpenAI models with Microsoft enterprise, compliance and EU data-boundary terms
  • Native fit with Entra ID, Microsoft 365, Copilot and Fabric
When leaving it, watch for
  • Replace Azure-specific content-filter configurations with equivalent guardrails
  • Provisioned throughput (PTU) commitments and quotas to wind down
  • Features tied to Azure AI Search or Foundry agents need an equivalent on the target

AWS Bedrock

Why teams move here
  • Consolidate on AWS and use existing commitments and security controls
  • Wide model choice in one API: Anthropic Claude, Meta Llama, Mistral, Amazon Nova and more
  • VPC endpoints, IAM and Bedrock Guardrails in the same account model as your apps
When leaving it, watch for
  • Bedrock Knowledge Bases and Agents are managed features; rebuild or map them
  • Provisioned throughput contracts to plan around
  • IAM-based auth patterns change to the target’s identity model

Google Vertex AI

Why teams move here
  • Gemini models with long context and strong multimodal capabilities
  • Data gravity: analytics already live in BigQuery
  • Partner models (including Claude) with Google Cloud commitments and VPC Service Controls
When leaving it, watch for
  • Vertex AI Search / Agent Builder features need an equivalent on the target
  • Gemini-specific capabilities (very long context, native multimodal) must be re-validated
  • Service-account-based auth and perimeter rules to re-create

Direct model APIs (OpenAI, Anthropic, Mistral…)

Why teams move here
  • Newest models and features usually arrive first
  • Simplest setup for startups and fast-moving product teams
  • Fewer cloud quota requests and regional constraints
When leaving it, watch for
  • Rate limits and data-processing terms differ from the cloud-hosted versions
  • Provider-specific APIs (e.g. built-in tools, file search) need equivalents
  • Billing and usage tracking move into your cloud bill and gateway

Self-hosted open models

Why teams move here
  • Full data sovereignty: nothing leaves your environment
  • Lower unit cost at high, steady volume
  • Deep customisation through fine-tuning and distillation
When leaving it, watch for
  • You give up operating GPUs; capacity planning becomes the provider’s job
  • Fine-tuned weights may not be portable; managed fine-tuning may be needed
  • Check data-processing terms meet the same requirements as self-hosting

What always changes

  • Model mapping and prompt re-tuning: the same prompt behaves differently on another model
  • Embeddings are not portable: vector stores must be re-embedded and re-indexed
  • SDKs, auth, networking (private endpoints) and IAM differ per provider
  • Quotas, throughput reservations and regional availability
  • Safety filters and guardrail settings
  • Fine-tuned models must be re-trained on the target
  • Logging, monitoring and cost attribution

How we de-risk it

  1. Assess: inventory every model call, prompt, embedding and provider-specific feature
  2. Decouple: route traffic through an AI gateway / abstraction layer
  3. Evaluate: run old and new side by side on golden sets and shadow traffic
  4. Cut over: phased by feature or tenant, with instant rollback
  5. Optimise & decommission: tune cost and latency, close old resources and contracts

Next step

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