Should you build on an all-in-one AI platform… or assemble your own “best model for the job” stack?

In this episode, we break down one of the most important architectural decisions in the second wave of AI: integrated platforms (one vendor, one ecosystem, one set of tools) versus model routing (dynamically choosing the right model per task, per user, per cost/latency target). We’ll unpack what each approach optimizes for—speed of shipping, reliability, cost control, flexibility, and long-term leverage—and why many teams start integrated, then evolve toward routing as they scale.

We’ll also cover the hidden traps: lock-in, surprise inference bills, inconsistent outputs across models, eval complexity, and what “production-ready” routing actually requires (fallbacks, caching, guardrails, observability, and quality gates).

In this episode, you’ll learn:

• When integrated platforms win (and when they quietly cap your upside)

• What model routing really is—and how it reduces cost without killing quality

• The non-negotiables: evals, retries, fallbacks, and “fail safely” design

• How to route by task type: reasoning, code, extraction, support, creative, vision

• The decision framework: shipping speed vs. control vs. defensibility

If you’re building agents for real customers, this choice will shape your margins, your roadmap, and your freedom—long before you realize it.