Multi-Cloud LLM Integration

Models that fit your boundary.

Azure OpenAI, Bedrock, Gemini API, and private endpoints—OWCER designs model selection, RAG, and API patterns that match your security, residency, and cost requirements. One integration playbook, not vendor lock-in by default.

The problem

Multi-cloud LLM spend without a integration strategy

Teams provision API keys across Azure, AWS, and Google—then rebuild the same RAG pipeline three times with inconsistent guardrails. We help organizations consolidate patterns without forcing a single hyperscaler.

Duplicate RAG stacks

Each cloud gets its own embedding store, chunking logic, and retrieval API. Security reviews multiply. Operators cannot explain which corpus feeds which model.

Residency & endpoint confusion

Public endpoints, private links, and on-prem or private-cloud models sit side by side with no clear map of where prompts and responses land—or who can access them in use.

Model sprawl & runaway cost

Every team picks a different model tier. Token spend grows without routing rules, caching, or fallbacks to smaller models for low-risk tasks.

Audit gaps

Leadership asks which models process regulated data. The answer is a spreadsheet of API keys—not a documented architecture security can sign off on.

Our approach

Unified patterns across clouds

We design reference architectures for model routing, RAG, and API access—then implement on the platforms you already use or are evaluating.

1. Assess
   Inventory existing API spend, data classifications, and residency requirements. Map which workloads need which model capabilities.

2. Architect
   Define routing, RAG boundaries, logging, and cost controls. Align with AI governance and network segmentation.

3. Implement
   Deploy APIs, retrieval layers, and application integrations with consistent auth, telemetry, and error handling across clouds.

4. Operate
   Model versioning, spend review, and failover patterns so teams can adopt new models without rebuilding from scratch.

Outcomes

Integration deliverables

Reference architecture

Documented patterns for model selection, private endpoints, and cross-cloud routing that security and architecture reviewers can evaluate once.

RAG on approved corpora

Indexed libraries with sensitivity labels, retrieval boundaries, and citation patterns—so answers trace back to sources leadership can defend.

Cost & usage telemetry

Token tracking, model routing rules, and review cadences so LLM spend stays tied to business outcomes, not surprise invoices.