Multi-Provider AI Routing
Claude for reasoning. GPT-4o for structure. Gemini for multimodal. Routed per task, with cost-aware fallback.
Production AI systems that route per task across multiple frontier providers — with fallback chains that absorb single-provider outages and per-provider logging that catches quality regressions within hours.
Single-provider AI is single-point-of-failure. When a provider goes down, the product goes down. When a model rev quietly regresses on a task you depend on, quality drops and nobody can attribute the cause. Routing is the operational hygiene that turns the LLM layer into infrastructure rather than a vendor lock.
- →Build a typed routing table that names the primary, fallbacks, and budget for every task type
- →Route per task by capability, latency budget, and cost ceiling — not by provider preference
- →Run fallbacks against real alternative providers, not against the same provider with a different model
- →Log which provider produced which output so quality regressions are observable per task and per model rev
Different parts of any non-trivial AI product need different models. Long-form reasoning runs on Claude. Tight structured extraction runs on GPT-4o-mini. Multimodal long-context runs on Gemini. Cited research runs on Perplexity. The routing table makes those decisions explicit, typed, and observable.
Multi-provider routing is risk management, not cost optimization. The dominant value is reliability and quality fit; the cost benefit is real but third-place. The single most important consequence is that a single-provider outage no longer takes the product down with it.
Per-call logs capture provider, model version, prompt, response, and latency. When a quality regression shows up — usually after a model rev — the logs answer which model, which prompt, and which task was affected. The system is debuggable.
- →Single-provider is single-point-of-failure. Build the routing table from week one.
- →Per-task selection beats per-product selection. The routing table is the architecture.
- →Log which model produced which output. Quality regressions are invisible without it.
Schema-first, RLS in week one, GraphQL on top, multi-surface from day one.
From iPad in a customer's space to a structured, line-item quote in ninety seconds.
Run the business from anywhere. Same tools, same context, voice or text, picked by the situation.