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Multi-Provider LLM Routing: One Governed API in Front of Every Model

· 5 min read
DVARA Team
DVARA Team

Once you're past a single model, a question shows up on every request: who should serve this one? Maybe cheap prompts go to a small model and hard ones to a frontier model. Maybe EU tenants must stay on EU providers. Maybe you're splitting load across two vendors for redundancy. Encode that in every application and you've spread the same brittle if/else — and the same ungoverned decision — across your whole codebase.

The reason to centralize routing isn't convenience; it's governance. The moment every model call flows through one control point, routing stops being scattered plumbing and becomes the place where policy is evaluated, cost is attributed, PII is scanned, and the decision is audited — before the request leaves your perimeter. Multi-provider routing is the mechanism; a single governed control plane is the point.

What multi-provider routing is​

Multi-provider LLM routing is the practice of exposing one consistent, OpenAI-compatible API to your applications while a governed control point decides, per request, which underlying provider and model actually serves it. In DVARA, that control point is the DVARA LLM Gateway — the data plane for model traffic within the AI governance platform. The application asks for a capability; the Gateway owns the mapping to a provider, and applies policy, cost, and PII governance in the same pass.

Because the interface is OpenAI-compatible, your code doesn't change when the routing does. Swap a model, add a provider, shift a weight — the application keeps calling the same endpoint, and governance keeps applying on call one.

Governance travels with every routed request​

Before we get to routing strategies, the part that matters most: routing in DVARA is never just routing. On the same request path, every call is:

  • Policy-evaluated — Policy-as-Code decides whether the requested model, tools, region, and token budget are allowed. A denied request is never routed anywhere.
  • Cost-attributed — the call is priced and booked to a tenant, key, and model in real time.
  • PII-scanned — prompts (and responses) are checked, and can be redacted or blocked, before they reach a provider.
  • Audited — the routing decision and its governance outcome land in a tamper-evident trail.

Routing decides where; governance decides whether and records what happened.

The routing strategies​

With that framing, here are the strategies the Gateway supports, chosen per route:

Model-prefix routing​

The simplest and most common. The model name's prefix picks the provider:

  • gpt-*, o1-*, o3-* → OpenAI
  • claude-* → Anthropic
  • gemini-* → Google
  • ollama/* → a local Ollama model
  • azure/* → Azure OpenAI

The application names a model; the Gateway knows where it lives. Adding a new provider is a config change, not a code change.

Round-robin and weighted​

For a pool of interchangeable models, distribute requests evenly (round-robin) or by weight (e.g. 80% to a primary, 20% to a canary). Weighted routing is how you roll a new model out gradually and compare it against the incumbent before committing.

Capability-aware routing​

The one people forget until it bites them. If a request asks for response_format: json_schema, it can only go to a provider that supports structured outputs; if it asks for JSON mode, only a provider with that capability. The Gateway filters the pool by capability before it routes, so a request never lands on a model that can't serve it. (The same filtering protects failover — see the sibling on LLM fallback and failover.)

Cost- and latency-aware routing​

More advanced pools route on live signals: send a request to the cheapest capable model, or the one with the lowest recent latency (tracked with a decaying moving average so stale data doesn't mislead). This is where routing pays for itself — and it ties directly into LLM cost management.

Why routing belongs in the platform, not a library​

You could build routing into a shared library. Teams try. It goes wrong for the same reason all cross-cutting logic in a library goes wrong: every service has to adopt it, upgrade it, and configure it identically, and the moment two services drift you have two routing behaviors — and two governance postures.

A governed control plane makes routing a property of the platform:

  • One place to change routing — shift a provider weight or add a model without redeploying an application.
  • Consistent governance — every service routes and is policed the same way, because they all traverse the same control point.
  • Versioned and reversible — routing config is a reviewable, roll-back-able artifact, not code scattered across repos.

Provider breadth​

Routing is only as useful as the providers you can reach. The DVARA LLM Gateway presents one OpenAI-compatible API in front of more than a dozen providers — the frontier vendors (OpenAI, Anthropic, Google, AWS Bedrock, Azure) and the long tail (Mistral, Cohere, Groq, DeepSeek, xAI's Grok, local models via Ollama, and more) — because the reason you want routing is usually that you're not betting on one vendor.

Where DVARA fits​

DVARA is an AI governance platform; multi-provider routing is one mechanism its LLM Gateway uses to put every model call on a single governed path. Routes are versioned config you edit and roll back without touching application code, and every routing decision happens in the same pass as Policy-as-Code, real-time cost attribution, and PII governance. Read the pillar guide, What Is an LLM Gateway?, for how routing fits the rest of the platform.