Multi-Cloud LLM Integration
AI Build
Models that fityour 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.
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.
One integration strategy across Azure, AWS, and Google
Start with an AI Activation Assessment to map model opportunities and readiness gaps—or contact us if you already know the integration pattern you need.
