Every enterprise shipping production agents eventually hits a wall: they need multiple models. GPT-4 for reasoning, Claude for compliance, Llama for cost, local models for privacy, Gemini for multimodal. The problem: managing 20+ LLM APIs isn't engineering—it's an operational crisis. Most teams start with one model. Cost pressures force switching. Compliance mandates specific vendors. Latency requires fallbacks. Suddenly you're managing: 20+ API contracts (versioning, rate limits, auth), cost tracking per model per task, fallback chains, compatibility matrices, infrastructure sprawl. Result: engineering spends more time on model operations than agent logic. This talk explores: Operational cost of diversity (hidden burden, engineering tax, complexity). API contract management (versioning, normalization, testing). Cost attribution & optimization (multi-tenant tracking, selection, waste). Reliability patterns (fallback hierarchies, circuit breakers, queuing, degradation). Vendor management (lock-in mitigation, negotiation, SLAs). Production case studies: Finance (12 models, 35% engineering time), Healthcare (8 models, compliance nightmare solved), Enterprise ($2M/year savings via routing). We show how to abstract diversity so engineers focus on agent logic, not infrastructure.

Every enterprise needs multiple models: GPT-4 for reasoning, Claude for compliance, Llama for cost, local for privacy. Managing 20+ APIs is an operational crisis: API contracts, cost tracking, fallback chains, compatibility matrices, infrastructure sprawl. This talk explores the operational tax of diversity, API abstraction, cost attribution, reliability patterns, and case studies where model routers save $2M/year. How to abstract diversity behind clean APIs so engineers focus on agent logic.